A Survey of Wireless Technologies Coexistence in

Wireless Networks manuscript No. (will be inserted by the editor)

A Survey of Wireless Technologies Coexistence in WBAN: Analysis and Open Research Issues Thaier Hayajneh1,? · Ghada Almashaqbeh2 · Sana Ullah3 · Athanasios V. Vasilakos4

Received: date / Accepted: date

Abstract Wireless Body Area Network (WBAN) is the most convenient, cost-effective, accurate, and noninvasive technology for e-health monitoring. The performance of WBAN may be disturbed when coexisting with other wireless networks. Accordingly, this paper provides a comprehensive study and in-depth analysis of coexistence issues and interference mitigation solutions in WBAN technologies. A thorough survey of state-of-the art research in WBAN coexistence issues is conducted. The survey classified, discussed, and compared the studies according to the parameters used to analyze the coexistence problem. Solutions suggested by the studies are then classified according to the followed techniques and concomitant shortcomings are identified. Moreover, the coexistence problem in WBAN technologies is mathematically analyzed and formulas are derived for the probability of successful channel access for different wireless technologies with the coexistence of an interfering network. Finally, extensive simulations are conducted using OPNET with several real-life scenarios to evaluate the impact of coexistence 1

School of Engineering and Computing Sciences, New York Institute of Technology, New York, USA E-mail: [email protected] 2

Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA E-mail: [email protected] 3

CISTER Research Center ISEP, Polytechnic Institute of Porto (IPP), Porto, Portugal E-mail: [email protected] 4

Department of Computer and Telecommunications Engineering, University of Western Macedonia, Kozani 50100, Greece E-mail: [email protected]

interference on different WBAN technologies. In particular, three main WBAN wireless technologies are considered: IEEE 802.15.6, IEEE 802.15.4, and low-power WiFi. The mathematical analysis and the simulation results are discussed and the impact of interfering network on the different wireless technologies is compared and analyzed. The results show that an interfering network (e.g., standard WiFi) has an impact on the performance of WBAN and may disrupt its operation. In addition, using low-power WiFi for WBANs is investigated and proved to be a feasible option compared to other wireless technologies. Keywords WBAN · Coexistence · Interference · E-health · IEEE 802.15.6 · IEEE 802.15.4 · Low-power WiFi

1 Introduction Hospital stay is accompanied by anxiety and frustration by patients and their families. Cost, lack of privacy, and limitation of patient roles are some of the underlining reasons for this negative image. Staying at hospitals is usually required for medical monitoring especially after critical medical interventions such as surgeries and accidents. Monitoring include following and recording vital signs and significant changes in the patient health status. The accelerating healthcare cost and high demand on medical services call for innovative solutions to ensure fast and cost-effective medical monitoring and follow up. The recent advances in communication systems, electronic devices, and wireless networking make these solutions feasible and affordable. The Wireless Body Area Networks (WBANs) come to provide an efficient paradigm for e-health and telemedicine applications and requirements [60] [100] [53].

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Usually, a WBAN consists of tiny nodes that record body health indicators such as blood pressure, glucose level, heart rate, body temperature ...etc [60]. These nodes are distributed either on the human body (i.e. wearable sensor nodes) or implanted inside the body and they are under the control of a coordinator node [100]. The goal is to provide long-term monitoring of patients under medical observations without interfering with their daily life activities. WBANs have many advantages that make them attractive for researcher and industry [18]. WBANs are non-invasive and capable of automatically monitoring the patient medical status and then reporting the information to a nearby coordinator device. This device is then responsible of relaying the medical information to healthcare professionals at the hospital to further analyze the data and take recommended medical actions. Such distance monitoring is time-saving and costeffective as it allows early detection and intervention of health problems without invading the patient privacy or employing full-time medical staff (e.g., 24-hour nurse) [95]. There is a consensus among researchers that there are differences between wireless sensor network (WSNs) and WBANs [60] [18] [54] [100] [59]. Hence, current protocols designed for WSNs and ad hoc networks are not well-suited for the properties of WBANs. In what follows, we highlight some of these differences and properties of WBANs as compared to WSNs. First of all, the density and number of nodes deployed in WSNs are orders of magnitude higher than that in WBANs where redundant devices are usually not deployed. This imposes several constraints on the communication protocols used in WBANs and at different layers. In addition, size and energy limitations are more strict in WBANs compared to WSNs. Moreover, in WBANs the data contains medical information which makes issues of reliability, security, and delay critical when compared to WSNs applications. Furthermore, the transmit power is much limited in WBANs due to concerns on health hazards. Consequently, the coverage and transmission distance in WBANs are significantly shorter than that in WSNs. Finally, unlike the nodes in WBANs where they have different demands and properties, nodes in WSNs are homogenous and perform similar functions. Given the aforementioned requirements of WBANs, the used wireless technology becomes an important design issue. The first wireless option is the IEEE 802.15.1 (Bluetooth) standard [5] which was adopted in the implementation of many telemedicine and e-health applications [108] [80] [30]. However, the properties of this standard (e.g., high bandwidth requirement, small size networks, does not support multi-hop communication,

Thaier Hayajneh1,? et al.

and long startup time for devices) makes it unsuitable for WBANs applications [60] and most critical amongst which is its high power consumption during operation [52] [35]. A derived option of the Bluetooth standard is the Bluetooth Low Energy [1] which was introduced as a more suitable choice for WBANs applications where less power consumption is possible using low duty cycle operation. However, using exaggerated low duty cycle to save energy makes it unsuitable for health monitoring applications as they critically require frequent data reporting. In addition, this technology is still not supported by many market appliances and devices [82]. The IEEE 802.15.4 (Zigbee) standard [4] is used widely for WSNs implementation where it offers larger coverage area in addition to having better performance under interference when compared to Bluetooth [112]. Both Bluetooth and Zigbee operates within the 2.4 GHz ISM band. Moreover, the power consumption in Zigbee is about one-half to one-third that of Bluetooth [86]. However, Zigbee data rate is considered low causing higher delays on data delivery due to longer channel fades [86] which is critical in medical applications carrying urgent data. Hence, Zigbee does not support adequate Quality of Service (QoS) for all WBANs applications and is not scalable in terms of power consumption [60]. Given the reported limitations in Bluetooth and Zigbee, there is a need for a new standard that operates properly with WBANs applications. Hence, the IEEE 802.15.6 WBAN standard is recently proposed as a promising wireless technology for low power devices such as the ones used in a human body [6]. This technology is specifically designed for WBANs operation and deployment environment making it a suitable option to different applications in medicine, entertainment, and other fields. IEEE 802.15.6 standard uses different frequency bands for data transmission including: The Narrowband (NB) which includes the 400, 800, 900 MHz and the 2.3 and 2.4 GHz bands; the Ultra Wideband (UWB) which uses the 3.1-11.2 GHz; and the Human Body Communication (HBC) which uses the frequencies within the range of 10-50 MHz. However, some of the bands are not suitable for WBANs applications as they cannot support video or voice transmission (e.g., HBC) or are only eligible to be used by authorized users (e.g., UWB) [100]. Therefore, researchers agreed that 2.4 GHz band is the most attractive spectrum to be used in medical applications in addition to their ability to protect adjacent channel interference [100]. Another wireless technology, that has been recently investigated in WSNs applications, is the low-power

Survey of Wireless Technologies Coexistence in WBAN

WiFi (which was modified from the original IEEE 802.11 standard) [31]. The modified version includes lower transmission power, cycling operation, and other energy-saving options. The IEEE 802.11 has well-tested security and high QoS. In addition, devices using the WiFi technology are frequently obtained at home, hospitals, banks, smartphones ...etc. Thus, integrating low-power WiFi devices with surrounding networks is easy and their infrastructure already exists [97]. Besides, low-power WiFi has high data transmission rate which reduces the transmission time (i.e. nodes are able to switch to the low-power sleep mode faster), and enables the implementation of heavy traffic applications such as live video and voice monitoring. A reason for using lowpower WiFi in WSNs is to enable the concept of Internet of Things (IoT) [96] [87], which makes it also a promising technology for WBANs applications. A common issue in all wireless technologies used to date is the issue of coexistence with other wireless systems. For example, where WBANs are used such as in hospitals, home, and public areas, other devices using WiFi may exist and interfere [54]. Most of WBANs wireless technologies (discussed earlier) operate at the 2.4 GHz ISM band causing them to interfere with other nearby wireless technologies operating on the same band. The effect of WiFi on the performance of WBANs, amongst the aforementioned wireless technologies, is most significant for several reasons. First, WiFi is widely spread and convenient to all users everywhere. Hence, an individual with WBAN is likely to be surrounded by many WiFi devices (laptops, notebooks, smartphones ...etc.) within a short distance and of high density, which may affect the function of the WBAN. Second, WiFi uses the highest transmission power compared to other technologies, operating at the same frequency band, resulting in a higher interference with WBAN performance. Third, the use of large packet sizes in WiFi technology affects the surrounding wireless devices albeit more significant in the case of WBANs. Finally, since most WiFi users tend to use multimedia and social networking applications on their devices, the expected high traffic rate in WiFi is another issue that may affect WBANs operation. For the aforementioned considerations, the coexistence analysis in this paper focuses on the effect of WiFi networks on WBANs. Current surveys presented detailed reviews of WBANs architecture, design, physical layer, medium access control (MAC) protocols, and QoS. Ullah et al. [100] reviewed WBAN architecture, topology, and studied the proposed technologies for WBANs at physical, MAC, and network layers and analyzed the behavior of these layers. In [60] the researchers discussed current and past projects in WBAN and pointed out open research is-

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sues and challenges. Similar to [100] the authors surveyed the first three layers of the protocol stack, discussed cross layer, quality of service, and security issues. An overview of body area networks was demonstrated in [18] in which WBAN communication paradigms and their related issues were discussed. The authors investigated several aspects of WBAN research such as: sensor devices, physical layer, data link layer, radio technology, and reviewed current WBAN projects. A discussion of WBAN design techniques for medical applications is given in [54]. The researchers examined WBAN design issues with a concentration on the design of MAC protocols and power consumption profiles. Lai et al. presented a survey on body sensor networks in which they focused on sensor, data fusion, and network communication [59]. The review studied system design and explained the current research status. Moreover, [95] presented a comprehensive review of state-of-the art requirements in hardware, computing, and communication in WBANs and ubiquitous computing. Different from other surveys, this paper provides a comprehensive study of coexistence issues and interference mitigation solutions in WBAN technologies. Particularity, we considered three main WBAN wireless technologies that operate at 2.4 GHz band: Zigbee, IEEE 802.15.6, and Low-power WiFi. In the first part of this paper, we performed a thorough survey of stateof-the art research studies in WBAN coexistence issues. The studies under each WBAN technology are classified according to the type of interference they considered: mutual or cross interference. Then, the studies of each type of interference are further classified according to the approach used by the researchers to analyze the coexistence issue: simulation, testbeds, analytical models, or any combination of these. The studies are then discussed and compared according to the parameters and factors they used to study the coexistence problem. The second part of this paper discusses the studies that proposed interference mitigation solutions for the coexistence problem in WBAN. The studies are classified according to their followed technique and then analyzed in an attempt to identify shortcomings in stateof-the art solutions. The third part of this paper performs analytical analysis for the coexistence problem in WBAN technologies. We introduce system model and problem definition for the analysis and derive formulas for the probability of successful channel access for the three wireless technologies with the coexistence of a standard WiFi network. The analytical results are analyzed and compared. Finally, extensive simulations are conducted using OPNET with several real-life scenarios to evaluate the

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following: coexistence of IEEE 802.15.6 WBANs with standard WiFi networks, and the performance of lowpower WiFi WBANs including: their mutual interference, coexistence with standard WiFi, and their interoperability with standard WiFi. To the best of our knowledge, no study has been conducted to analyze coexistence issues in WBAN wireless technologies and provide in-depth analysis for the problem. One aim of the mathematical analysis and simulations is to study the feasibility of using low-power WiFi for WBANs and compare it with Zigbee and IEEE 802.15.6 standards. In addition, the immunity of different WBAN technologies to WiFi interference is thoroughly analyzed. Moreover, it is applicable to apply the methodology we follow in the mathematical analysis to analyze the coexistence of any WBAN wireless technology, other than the three considered technologies, with interfering network (other than std-WiFi) operating at the same band. Hence, the proposed approach can be followed by changing the parameters values according to the wireless technology under study. 1.1 Major Contributions The main contributions provided in this paper can be summarized as follows: 1) a comprehensive review of state-of-the-art studies discussing the coexistence issues and mitigation solutions in WBAN technologies. The studies are classified, discussed, and compared according to the parameters and factors used to study the coexistence problem; 2) a comprehensive review of studies proposing solutions for interference mitigation for the coexistence problems in WBAN. The studies are classified according to the followed technique and then analyzed in an attempt to identify potential shortcomings. 3) An analytical analysis for the coexistence problem in WBAN technologies and derived formulas for the probability of successful channel access of WBAN with the coexistence of an interfering network. 4) Extensive simulations using OPNET with several real-life scenarios to evaluate the impact of standard WiFi on WBAN. The rest of the paper is organized as follows: Section 2 reviews WBANs in more detail including the network architecture, operation, and applications. Design challenges and requirements are discussed in Section 3. Section 4 reviews related work in coexistence issues of WBANs. The main proposed solutions to mitigate interference and enhance coexistence are presented in Section 5. The system model and problem statement are introduced in Section 6. Section 7 presents the mathematical analysis of coexistence between the different WBANs technologies and WiFi networks while the simulation results are reviewed in Section 8. Finally, Sec-


tion 9 highlights the main conclusions derived from this paper.

2 WBANs Architecture and Applications This section briefly reviews WBANs architecture and applications, other detailed review on the matter are available in the literature [60] [54] [100] [18] [59]. WBAN consists of nodes that are either placed in-body (implantable sensors), on-body (wearable sensors [22]), or surrounding the body for behavioral recognition [59]. These nodes regularly monitor human’s vital information and usually use wireless communication to connect to a coordinator node. The coordinator node (also referred to as master node) first collects data from all other nodes in the network and then sends the information to the medical staff. Based on the received data and the patient’s profile the medical team can take the suitable medical action. The sensors in any WBAN are divided according to the type of measured signals into two categories [59]. The first type collects continuous time-varying signals that require real-time signal processing, examples are: electrocardiograph (ECG) sensors [68] and accelerometers [12]. The second type collect discrete time-varying signal that changes slowly, examples are: pressure sensors [67], and respiration sensors [72]. Figure 1 shows a WBAN network architecture with different types of nodes that are distributed in/on/ around the human body. There exist three tiers of communication similar to the ones defined in [18] [60] [95] [19]. Tier 1 communication is between the WBAN nodes and the master node and also possibly between the WBAN nodes themselves. This communication tier is critical as it is directly related to the human body, it has short range (1-5 meters), may require low or high data rate depending on the sensor type, and equipped with small batteries, hence it has limited energy. Tier 2 communication is between the master node and the access point (AP) or the gateway which is responsible to connect the WBAN to the cloud network. Tier 3 communication is between the AP and the medical staff through a direct link, using the Internet, or the cloud network. In the WBAN architecture shown in Figure 1 several WBANs coexist where each group of WBAN nodes are attached to a human and connected to their own master node. Obviously, there is a need for medium access control protocol to avoid collisions and interference among nodes that belong to the same WBAN. However, as illustrated in the figure, interference is still possible from WBAN nodes that belong to other WBAN networks. One good side in this issue is that it is most likely that


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Fig. 1 WBAN architecture and coexistence

the WBAN networks are using the same MAC protocol, hence, with some coordination between the master nodes interference may be eliminated or reduced. On the other hand, the WBAN may be surrounded with other wireless technologies that operate at the same band, using higher transmission energy, different MAC protocols (possibly without CSMA/CA), and coexist within the interference range of the WBAN. In the example shown in Figure 1, external interference is possible from Zigbee devices, bluetooth devices, microwave ovens, and WiFi networks. The accelerating demand on healthcare services and the exponential cost associated with medical interventions call for innovative solutions for remote health monitoring such as WBANs [22] [50] [38] [7]. Furthermore, WBANs have many other applications including sports training where sensors are attached to the athletes body and record movements to check the appropriateness of the performed exercises [18]. Safe navigation is another application of WBAN where infants or children are monitored while walking or in a vehicle [79]. Security and military applications [100] [79] are also covered by WBAN, especially for battlefield surveillance where sensors are deployed in the soldiers uniform to track their ambulation, monitor their health, and detect the presence of toxic materials. Moreover, WBAN are widely used in entertainment venues such as interactive

gaming, dancing lessons, shopping and human-centered applications [18] [76] [99] [32] [17].

3 WBANs Design Challenges This section highlights the main design challenges in WBANs. Due to their special properties as size, data rate, reliability, security, mobility, power constraint, QoS requirements, transmission range ...etc, they require special design adjustment to meet their peculiar needs. The most critical function of WBAN is to efficiently deliver reported information from a certain application. Efficient communication is described as reliable, secure, fast, fault-tolerant, scalable, interference-immune, and low-power data communication [83]. In order to achieve the aspired efficiency, the design of WBAN must take into consideration many important issues related to its special coverage area, mostly the human body. Small coverage area, body temperature and movements, and location of nodes (i.e, inside/outside/ surrounding the body) are critical factors that affect the design of this type of networks [60] [95]. Communication between implanted nodes and surface nodes may experience high signal attenuation and distortion upon facing the body tissue and heat. The movements of the body parts (e.g., arms and legs) carrying the WBAN nodes may affect

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the network functionality by changing the network topology, communication links, or signal attenuation or blockage. The situation may become more complicated considering the limited computation, communication, as well as the nodes size. Nonetheless, the most crucial limitation is the power source where WBAN nodes are equipped with small batteries. Hence, extending the lifetime of the network is highly required especially for the implanted nodes. Therefore, special attention must be given to the network lifetime when selecting or designing communication protocols for WBAN (e.g., routing, MAC, ...etc) [102] [54]. In [8] researchers emphasized on the importance of estimating the lifetime of WBAN and used a probabilistic analysis to determine the timing and distribution of node failure. Scalability, self-organization, and configuration are other challenges to WBAN communication [65]. The WBAN nodes are expected to join or leave the network on the fly. The deployed networking protocols must be capable to support the possible dynamic changes in the network topology. For example, the MAC protocol must not perform static assignments to the nodes for medium access control [102], it must be scalable and dynamic. Moreover, according to [32] and [9] programming complexity is also a challenging issue that limits the diffusion of WBAN in real life. QoS is another issue in WBANs communication that must be satisfied where best effort service is not acceptable given the sensitivity of their applications and the nature of the transported data [55] [94] [20]. For example, when WBAN is used to monitor a patient’s health condition, information have to be delivered instantly. Delays in delivering critical or emergency alarms may be catastrophic [64] [119] [62] [101]. In [110] the researchers stated that it is difficult to ensure acceptable QoS in distributed healthcare systems due to the relative unpredictability of the network environment. Yet another important aspect for WBANs applications is information security. WBAN nodes must ensure confidentiality, integrity, and correctness of the received data [48] [90] [10] [120] [16]. False alarms or injection of false data may lead to misleading results on the patients health. Due to its importance and complication, security in WBAN must be addressed at different layers and levels [100], i.e. MAC, physical, and network layers. However, security overhead may increase the energy consumption and affect the network lifetime [84]. Hence, lightweight security schemes must be adopted or customized for WBANs. In [118] the researchers proposed a practical Lightweight biometric-approach to authenticate messages in WBAN. They also developed a key-agreement scheme that allows key sharing be-


tween WBAN nodes with low overhead. Their analysis proved that the proposed scheme is energy efficient. In addition, security in WBAN may be significantly improved when trust is established between nodes in the network [43]. In [44] they proposed an attack resistant and lightweight trust management protocol. The proposed protocol was tested in network of TelosB motes and the results showed that the protocol improved the network performance and protected the network from malicious behaviors. Moreover, He et al. proposed an application-independent and distributed trust evaluation model for WBAN that helps to exclude malicious nodes by identifying their malicious behavior [43]. In [14] the researchers studied the impact of misbehavior on physical layer in distributed wireless interference networks. Integration of multiple networking technologies (i.e. interoperability) is also an important design aspect of WBANs. In most cases, WBAN transfer the collected data to a gateway connected to the medical staff. The gateway may be a laptop, a PDA, or a smartphone and the coordinator node at the WBAN must be capable to communicate with the gateway. As discussed in the previous section, the WBAN communication tiers need to efficiently operate to successfully deliver the data to the decision makers at the application side. Coexistence with other wireless technologies that use the same frequency band is a challenging issue that faces WBAN designers [104]. For example, the 2.4 GHz ISM band is also used by IEEE 802.11 (WiFi), IEEE 802.15.4 (Zigbee), IEEE 802.15.1 (Bluetooth) standards, and other home appliances. Thus, the heterogeneity of wireless technologies standards affect their performance. The interference problem exists either in the same network, i.e. effect of nodes on each other, or across multiple networks, i.e. nodes from different networks interfere with each other. For example, a patient with a WBAN may be surrounded by other patients with WBANs (e.g., in a hospital) which causes interference issues leading to signal degradation [41]. Coping with interference inside the same network is easier than the one across multiple different networks due to many reasons. First of all, the use of different MAC protocols that use different techniques to access the medium, or the use of different parameters values for the same MAC protocol leads either to unfair usage of the medium or collisions due to the simultaneous transmissions [69]. Another reason is the use of different packet sizes where networks with large packet sizes, e.g. WiFi, will reserve the medium for longer time than networks with small packet sizes, e.g. Zigbee. Moreover, the use of different transmission power levels makes the high power networks aggressive to other low power net-


works and may ruin their operation completely due to the high level of interference. Accordingly, if the same standard can be used for all networking types, i.e. high and low rate, high and low power, wide and narrow bands, ...etc., then a large part of the interference problem may be solved. The interference problem will affect several aspects including throughput, delay, and success packet delivery ratio. Hence, there is a need to develop techniques to mitigate the impact of interference in order to enhance the overall WBAN performance and guarantee its interoperability.

4 Coexistence Issues in WBAN Technologies This section reviews the main research effort that studied the coexistence issues in WBANs technologies. A common objective among most of the presented work is to quantify the effect of interference on the operation of WBANs. The aim is to suggest useful recommendations on WBANs deployment, configuration, and settings that may help to mitigate the negative impact of interference. In general, the existing studies related to coexistence issues in WBANs could be classified according to several criteria. One classification criterion, which is adopted in this paper, is the underlying wireless technology that is used in WBANs. Based on that, WBANs are classified into three main categories: IEEE 802.15.4-based WBAN, IEEE 802.15.6-based WBAN, and low-power WiFi based WBAN. The studies under each category are further classified according to the interference source that they considered. Two types of interference were studied: the intra-network interference (will be referred to as “mutual interference”), and the inter-network interference (will be referred to as “cross interference”). In the former, the source of interference comes from multiple adjacent WBANs that are based on the same wireless technology and are operating simultaneously with interfering frequency channels, e.g. several patients with WBAN located in one area at a hospital. However, the problems caused by this type of interference can be avoided or reduced especially in small to medium network sizes by implementing an efficient MAC protocol. On the other hand, cross interference occurs when networks that are based on different wireless technologies operate in the same frequency bands. This situation is complicated since different standards use different MAC protocols, power settings, data rates, packet sizes ...etc. Hence, interoperation becomes a critical issue in this case. Moreover, the studies of each type of interference have considered different approaches in analyzing the

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effect of coexistence. Researchers have based their analysis on: simulations, testbed experiments, analytical models or any combination of these. A taxonomy of the related work in WBANs coexistence issues based on the aforementioned classification and categories is shown in Figure 2. 4.1 IEEE 802.15.4-based WBANs IEEE 802.15.4 (Zigbee) is the most common wireless technology for WSNs. At the beginning WBAN were considered as a special case of sensor networks, hence, the IEEE 802.15.4 standard was extensively adopted to implement WBAN. In the 2.4 GHz band the Zigbeebased WBANs are vulnerable to interference from other technologies such as: Bluetooth, IEEE 802.15.6, and WiFi networks. Unsurprisingly, the coexistence issues of WSNs has been studied thoroughly in the literature and this section presents the most relevant studies to WBANs starting with those that considered mutual interference and followed by the studies that considered cross interference. In what follows mutual interference in IEEE 802.15.4 studies are discussed and classified to: simulation-based, testbes-based, and analytical modeling-based studies. Cross interference studies in IEEE 802.15.4 are then presented following the same classification. 4.1.1 Mutual Interference in IEEE 802.15.4 WBANs Simulation-based Studies: Coexistence studies using simulation for zigbee WBANs mutual interference are found in [26] [25] [37]. The researchers in [37]started by studying a single WBAN network and then considered two interfering networks. They explored the network scalability by varying the number of nodes within the WBAN until it is saturated. Also, the effect of MAC layer parameters were studied including back-off time and packet segmentation. Specifically, they studied their impact on the packets delay which is an important aspect in most WBANs applications. A simulation based study of mutual interference in Zibgee UWB WBANs, i.e. IEEE 802.14.5a, was presented in [26] which was then extended in [25]. Both studies considered a WBAN that is composed of a set of wearable sensors distributed on a patient’s body. However, [26] studied interference between nodes that belong to the same WBAN, whereas [25] has considered interference between two WBAN networks. The interference in their networks was due to the lack of synchronization between the nodes which caused overlapping in transmission between nearby nodes, i.e. collisions. The authors studied the effect of the asynchronism level,


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WBANs Technologies Zigbee

IEEE 802.15.6

Low-power WiFi

⇓

⇓

⇓

Mutual Interference

[92] [26] [25] [37] [74] [91] [24] [89]

[28] [41] [104] [45] [23] [117]

[97]

Cross Interference

[37] [78] [11] [42] [46] [33] [77] [47] [88] [21] [116] [70] [66]

[28] [105] [69] [45]

[98]

Fig. 2 Taxonomy of coexistence study in WBANs related work.

i.e. duration of transmission overlapping between the nodes, of the nodes on the throughput in terms of the Bit Error Rate (BER). They showed that BER has an acceptable value when the asynchronism level is less than the time burst needed to transmit a symbol over the channel, with the maximum number of interfering nodes was five. Coexistence with another WBAN was studied in [25] where higher BER was reported as the number of interfering nodes increases for the same asynchronism level. Based on these results a code assignment technique was proposed to maximize the frequency offset between the interfering nodes and so to reduce the probability of collisions between them. Testbed-based Studies: Empirical studies of mutual interference are found in [74] [91] [89] [92]. In both [74] and [91] the deployed WBAN testbed consisted of three nodes; a transmitter, a receiver, and an interferer node. In [74], the authors studied the effect of distance, orientation of transmitters and receivers, packet size, and the transmission power level on the interference level between the nodes in terms of the packet loss rate (PLR). The exhibited results indicated that an interferer with antenna orientation of 90◦ or 270◦ has less effect on PLR compared to other angels. Moreover, as expected, with larger distances the effect was small while PLR increased with higher interferer transmit power. Based on the obtained results the authors proposed a transmission scheduling technique to minimize interference. On the other hand, the work presented in [91] studied the effect of channel spacing in addition to the packet size and transmit power level on interference. Similar results to [74] were obtained; PLR at the receiver increased with the increase of the signal power of the interfering node, and decreased with the increase

in the channel spacing. Further, it was shown that interference has larger impact on large packet sizes, this is due to the higher collision and packet error probabilities. In [92] researchers studied the link characteristics of Zigbee-based WBAN with test-bed experiments. They evaluated the received signal strength indicator (RSSI), link quality indicator (LQI), packet reception ratio, and movement intensity of body under indoor and outdoor experiments. Finally, the authors in [89] measured the PDR of WBAN nodes while varying the number of active WBANs with variable packet transmission rates and considering the human body effect. PDR experienced large degradation as the number of interfering networks was increased, the degradation was higher with high packet transmission rate. Analytical Modeling-based Studies: An analytical model of the effect of interference caused by coexisting WBANs was derived in [24]. The authors assumed homogeneous WBANs that use a similar packet sizes and data rates, under the IEEE 802.15.4 slotted MAC. In this mode of operation the nodes are synchronized using small control packets called beacons. The potential loss of beacons makes the nodes unsynchronized and unable to transmit their data. For this purpose the probability of both successful beacon transmission and successful data packet transmission were analyzed. The obtained mathematical model was verified using simulation. 4.1.2 Cross Interference in IEEE 802.15.4 WBANs Simulation-based Studies: It is noted that most of the work that studied cross interference in Zigbee-based WBANs were carried out empirically. Hence, few work used simulations to study cross interference in these


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The PLR for two Zigbee nodes interfering with two WiFi nodes was also considered in [46]. It was found that at low packet rates PLR was about 20% and at high rate it was around 80%. Memon et al. [70] used a similar testbed as in [46]. PLR of the Zigbee nodes was measured while varying the distance and the frequency channel offset with respect to the WiFi nodes. It was found that Zigbee nodes have shorter collision time when interfering with IEEE 802.11g as compared to IEEE 802.11b. Hence, PLR was lower for the IEEE 802.11g interference case. As expected, PLR decreased as the channel spacing and distance were increased. However, IEEE 802.11b required larger distance to achieve a reasonable PLR at Zigbee nodes compared to IEEE 802.11g. Testbed-based Studies: Most of the experimental based Liang et al. [66] studied the coexistence between Zigstudies considered the coexistence of Zigbee with the bee and IEEE 802.11b/g at the bit level, which was not different versions of WiFi standards; i.e. IEEE 802.11b/g/n. considered in the previous studies. The main parameter Coexistence with IEEE 802.11b networks was explored in the study was the distance between the interfering in [78] [11]. The authors in [78] used a testbed with two WiFi device and the Zigbee sender, also the packet size WiFi stations placed next to a single Zigbee transceiver. was changed. Two types of interference were found to Different scenarios were tested by changing the nodes’ exist: symmetric (WiFi device can hear Zigbee transroles, i.e. being a transmitter or receiver, and the dismission) and asymmetric (WiFi device cannot hear Zigtance between the nodes. The results showed that WLAN bee transmission). The reported results indicated that experienced degradation in throughput when the Zigthe symmetric interference only corrupted the header of bee node was very active, i.e. sending at high rate. Such the Zigbee packet but not the payload. This is because degradation was higher at high WLAN data rates with the WiFi nodes back off immediately after detecting the the 1 Mbps data rate being most immune to interferZigbee transmission. However, in the asymmetric interence. On the other hand, [11] deployed a larger network ference case the entire packets were corrupted. Based that contains 10 Zigbee nodes interfering with a single on their results and observations, solutions to mitigate WiFi station associated with an AP. Different scenarboth types of interference were proposed. ios were studied by changing the packet rate, packet An experimental as well as a simulation study of size, and network topology. Similar to [78], it was found IEEE 802.11g interference effect on Zigbee-based WBANs that interference of Zigbee slightly increased the PLR in a hospital room was introduced in [33]. The simuand the delay of the WiFi station. However, the Ziglation was based on real channel measurements from bee nodes experienced about 80% PLR, especially in hospital environment using a vector network analyzer the case where the AP was far and unable to sense the to correctly model the channel and to characterize WiFi Zigbee transmission. interference accurately in the simulation experiments. Effects of the distance between the nodes, the chanCoexistence with IEEE 802.11b/g networks was exnel frequency offset, and the transmit power level were plored in [42] [46] [70] [66]. [42] considered a WBAN considered. The same setup was implemented in a real composed of two nodes attached to the right arm and hospital room to validate the simulation results. under the right shin of a person who was moving durThe interference effect of IEEE 802.11g/n was exing the conducted experiments. The 16 channels of the plored in [77]. Distances between the nodes and their Zigbee standard were monitored. It was found that due antenna orientation were varied. The effect of IEEE to the high transmission power of the close WiFi sta802.11g on Zigbee nodes was found to be lower than tions, Zigbee channels experienced large PLR regardIEEE 802.11n due to its lower data rates and transless that their center frequency was far from the active mission power. In the overlapping channels the packet WiFi channels. Based on the conducted experiments, delivery rate (PDR) of the Zigbee nodes was almost the authors found that at any point of time there ex0%, while the highest PDR was around 60% for the ist more than one Zigbee channel that are not blocked, non-overlapping channels. This is due to the fact that even under severe 802.11b/g interference. Hence, chanthe high transmission power of IEEE 802.11n makes its nel switching can be utilized to select the channel that interference effect noticeable even outside its operating offers 0% PLR to be used for transmission. networks. A simulation based study of coexistence of Zigbee-based WBANs with IEEE 802.11b network was presented in [37]. A simple network setup was considered that consisted of an IEEE 802.11b network with a single station, a single AP, and a WBAN that is composed of two nodes. The PLR of the WBAN was found to be high, around 100%, especially when the WLAN was transferring multimedia data, i.e. with high constant bit rate. This was the case when WBAN was only able to detect packets from its type. However, the effect decreased when the WBAN was able to detect WLAN packets. Similar results were obtained with FTP traffic transferred over the IEEE 802.11b network, i.e. high WBAN PLR.

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frequency channels. For the nodes’ antenna orientation, the least interference was obtained with orientations of 90◦ and 180◦ and the maximum effect occurred at 0◦ orientation. Analytical Modeling-based Studies: Mathematical models for the effect of cross-interference on Zigbee-based WBANs were derived in [47] [88] [21] [116]. Howitt et al. [47] studied the effect of IEEE 802.15.4 devices on IEEE 802.11b WLANs. They developed an analytical model for the probability of packets collision in WLANs that is caused by the activity of Zigbee-based devices. The developed model was used to set an upper limit on the cluster size of zigbee-based nodes to reduce the interference on WiFi networks. However, this interference exists only when Zigbee nodes use sufficient high transmission power that causes noticeable interference with WLAN. Shin et al. computed the PLR mathematically for a Zigbee network under the interference of both WiFi and Bluetooth [88]. Simulation results validated the numerical results that were obtained from the mathematical model. The results verified that WiFi has larger effect on Zigbee networks compared to Bluetooth, this is due to the high transmission power of WiFi. The throughput of Zigbee networks under the presence of WiFi interference was analyzed in [21]. The model is based on Markov chain mathematical system and assumed that the Zigbee network has no effect on WiFi. The researchers found that the increase in the WiFi network packet rate caused a decrease in the Zigbee network throughput. Yuan et al. also analyzed the throughput of Zigbee network under the existence of 802.11b/g network [116]. The authors discussed the sufficient conditions for correct transmission of Zigbee data packet by considering two types of losses: the inhibition loss and the collision loss. The obtained simulation and experimental results were similar to other studies, the Zigbee throughput dropped severely in the present of WiFi activity which matches the developed mathematical model.

4.2 IEEE 802.15.6-based WBANs The performance of the new WBANs standard, i.e. IEEE 802.15.6, under the presence of other technologies in both the ISM and UWB frequency bands is also investigated by the researchers. However, little work is found in the literature where the performance has been analyzed based on different conditions and environments setup. This section reviews the previous findings on the coexistence issues of IEEE 802.15.6-based WBANs.


4.2.1 Mutual Interference in IEEE 802.15.6 WBANs Simulation-based Studies: Mutual interference was analyzed using simulations in [28]. Dotlic et al. [28] explored the operability of IEEE 802.15.6 standard in UWB with the objective of evaluating the performance of the suggested physical layer modulation schemes. Further, different types of receivers were considered, namely, duty-cycled sampling receiver and chirp receiver. Three co-located WBANs were configured to operate at different transmission power levels, two with equal levels and the third operates at a higher level. The results showed that under high traffic density the chirp receiver is more immune to interference than the sampling receiver where the PLR was around 1% for up to 10 co-located users. Testbed-based Studies: A simulation as well as a testbedbased study was presented in [23]. The authors measured the signal attenuation with different types of antennas gain for on-body medical sensor nodes distributed at different places on a human body. To study the interference caused by other interfering WBANs, a testbed that is composed of 10 persons with WBANs moving in an office was constructed, while no interference from other devices such as WiFi or Bluetooth existed. PLR was computed using simulations for different communication techniques including listen before talk (LBT), frequency hopping, and automatic repeat request (ARQ). Results showed that combining both LBT and ARQ gives the best performance for time division multiple access (TDMA) based communication. An empirical study of interference between multiple WBANs due to human movements in an indoor/outdoor environment was presented in [41]. The testbed consisted of five persons, each one represents a WBAN, who were moving inside an office. The impact of distance between the nodes and their orientation on interference were explored. It was reported that distance does not always has a large impact on interference, especially for mobile WBANs which was not the case with stationary WBANs. Orientation of transceivers had a large impact on SIR which was found to be larger for outdoor environments due to the lack of reflections and multipath effect that usually exists in indoor environments. Analytical Modeling-based Studies: X. Wang et al. [104] analyzed mutual interference mathematically . A Gamma distribution based model of co-channel interference between adjacent WBANs was developed. The interference model was exploited to find the minimum boundary distance between the nodes in any WBAN that preserves an acceptable Signal to Interference Ratio (SIR)


value. The exhibited simulation results validated the model and verified its efficiency. Zhang et al. [117] presented a mathematical model for the probability of collision and the mean SIR for cochannel interference in WBANs. The researchers considered different access techniques at the MAC layer including TDMA, FDMA (frequency division multiple access), FH (frequency hopping), and CDMA (code division multiple access) while varying the number of both the used frequency channels and the interfering nodes. Moreover, a simulation based study of these factors was also presented. The results in terms of BER and PLR showed that in non-coordinated WBANs TDMA has a similar performance to FDMA/FH which is better than CDMA. However, in coordinated (synchronized) networks FDMA/FH showed the best performance in terms of interference cancellation. 4.2.2 Cross Interference in IEEE 802.15.6 WBANs Simulation-based Studies: Cross interference in IEEE 802.15.6 WBANs with other technologies in both the UWB and ISM bands has been studied using simulations in [105] [28] [69]. Y. Wang et al. evaluated the performance of 802.15.6 WBAN under the interference of WiFi and Bluetooth [105]. The simulated network consisted of a monitor and four ECG electrodes that were sending data to the monitor. An interfering mobile station, which was set at first to be a WiFi station and then was replaced by a Bluetooth station, was exchanging FTP traffic with an AP. The distance between the mobile station and the WBAN was varied and the PLR in addition to the Mean Time To Fail (MTTF), i.e. time at which the network fail to transmit any packet correctly, were computed. As expected, it was found that PLR increased and MTTF decreased as the distance increases. The performance degradation was larger when WiFi station is used instead of Bluetooth station, this is because WiFi has higher signal power and occupies larger bandwidth. Beside studying mutual interference in IEEE 802.15.6 WBANs the authors in [28] also explored coexistence with WiMax devices in the UWB band. The interfering WiMax network was operating over small and large bandwidth. Both sampling and chirp receivers experienced higher PLR compared to the case of mutual interference alone. However, the chirp receiver performance became worse in wide bandwidth operation of WiMax while the sampling receiver operation was not affected by the bandwidth. WBANs coexistence with both WiFi and Zigbee was investigated in [69]. The simulated environment contained different node types that were deployed in a

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single room; five WBAN nodes distributed on a human body, two WiFi nodes, and five Zigbee nodes. The authors considered a channel model that accounts for both body propagation and mobility effects. Moreover, they studied the performance of different modulation schemes and medium access techniques. Analytical model of BER for all physical modulation techniques were derived used to compute PLR during simulations. Under MSK (Minimum Shift Keying) modulation the MAC of IEEE 802.15.6 performed better than the MAC of Zigbee networks. The modified MSK and Gaussian MSK (GMSK) modulation had similar PLR values regardless of interference. This is because the packets were lost due to the high sensitivity of the receiver in these schemes rather than interference. Both packets delay and energy consumption of the nodes slightly increased with interference compared with PLR which increased significantly. The IEEE 802.15.6 MAC showed better delay compared to IEEE 802.15.4 MAC but consumes more energy due to its longer medium listening period. Analytical Modeling-based Studies: Coexistence of IEEE 802.15.6 WBANs in the UWB has been studied mathematically in [45]. The authors studied the interference effect of both IEEE 802.15.4a and IEEE 802.15.4f on IEEE 802.15.6 WBAN devices. Analytical models of the transmitted signals, antenna power, signal-tointerference-plus-noise ratio (SINR), and the aggregated signal under interference were presented. However, the dynamic nature of the UWB made finding a closed form of BER or PLR unrealistic, thus, simulations were used to get numerical results on the interference effect. It was found that the mutual interference can be tolerated for up to 10 interfering WBAN nodes, i.e. BER was not affected. Moreover, interference from IEEE 802.15.4f devices was higher compared to IEEE 802.15.4a. 4.3 Low-Power WiFi based WBANs The usage of low-power WiFi for WBANs and WSNs has not been thoroughly investigated in previous research. The original operation of WiFi technology has been modified to be suitable for sensor networks requirements. Such modifications include low transmission power, duty cycling operation ...etc. This is done to achieve one of the main design requirements of WSNs, that is the maximum possible battery lifetime. Many products of WiFi enabled sensor nodes/devices are found in the market, e.g. [31] [81] [34], this implies that these devices are viable and attractive option for the industry. The research work in this area is either design or implementation of low-power WiFi sensor nodes, or performance evaluation of the ability of this technology to


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support the main requirements of WSNs applications, particularly the battery lifetime. Mendez et al. built a low-power WiFi based WSN to monitor an agricultural environment [71] where environmental conditions such as: humidity, light, temperature ... etc., were measured and reported to a server using IEEE 802.11g links. The usage of low-power WiFi modules for Internet or Web connectivity was investigated in [75] where different environments and security techniques were tested and the awake time periods were reported, the main concern was the power consumption. Tuzlo et al. [96] evaluated the feasibility of using lowpower WiFi for sensor networks empirically. The focus was to measure the energy consumption of the nodes with different network parameters, i.e. data rate, packet size, and wake-up frequency. The obtained results were compared with the results published for the 6LoWPAN (IPv6 Over Low Power WPAN) technology [73], where the former exhibits a lower energy consumption. An extended version of this work was presented in [97] where the effect of the used security techniques and MAC layer parameters on the battery lifetime were studied using the same network setup. Coexistence with heavy-loaded WiFi network has been studied empirically in [98]. Two different scenarios were considered; in the first one the low-power WiFi nodes and the standard WiFi nodes were associated with different APs, while in the second one they were associated to the same AP. The network performance was reported in terms of PDR and the round trip time (RTT) for different packet sizes and data rates. It was found that low-power WiFi nodes performed better in the first scenario, i.e. when they were associated with their own AP. However, they experienced more packet loss and larger packet delay when operating with standard WiFi nodes on the same AP. Moreover, it was found that in this case PDR and RTT had better values when the nodes were using low data rate, i.e. 1 Mbps, and small packet sizes. Further, using low data rate extends the connectivity range of the low-power WiFi nodes. The IEEE 802.11 standard is well-established and tested, however, its limitations and feasibility as a wireless technology for WBANs remain an open research area that calls for further investigation and analysis. As elaborated earlier, WBAN nodes can be attached to the patient body surface or implanted under the skin. The work available in the literature discussed the feasibility of low-power WiFi as a standalone sensor nodes (rather than being attached/implanted to a body) that can be used for typical WSNs applications such as: security systems, surveillance, environment monitoring ...etc [71] [75] [31]. Therefore, further research is re-

quired for WiFi-enabled sensors in the field of health applications including their effect on human body tissue, organs, temperature, signal attenuation through human body ...etc. In addition, examining which WiFi standard settings such as: automatic data rate adaptation, transmission power control, layers added headers on the data frames ...etc, are most suitable for WBAN applications is a critical issue. Other additional unnecessary WiFi functions may complicate the sensor design and consume more energy without tangible performance gain. Moreover, accurate energy consumption of this type of sensor nodes under the cyclic operation is an important issue and further empirical studies are required on this topic [96]. Finally, it was shown in [98] [97] that the used parameters values of the low power version of the IEEE 802.11 standard have a large effect on the network operation. The suitable transmission power level, the transmission rate, the packet generation rate, the packet size ...etc., are all critical parameters and their values have to be determined accurately based on the targeted health application, the network setup, and the surrounding environment. 4.4 Summery and Discussion Table 1 summarizes and compares the studies presented in this section according to the parameters and factors they used to study the coexistence problem and whether they are simulation, empirical, or mathematical modeling based studies. The considered factors in the studies are highly affected by the used method for conducting the performance analysis. However, some factors are not used in many studies since they have negligible effect on the results. For example, Table 1 shows that the distance was only considered in empirical studies and mathematical modeling but not in simulation studies. This is due to the fact that simulations are based on ideal environments, although they try to include models for channel error rate, medium propagation, and obstacles to make their environment more realistic, it is still far from real experiments. Moreover, adding excessive details to simulations makes them complicated, hard to debug and verify, and requires heavy computational resources. Further, having simulation model identical to reality is not possible, in their best case they may provide estimations of the actual results. Therefore, varying the distance with small values has negligible effect on the network performance. However, testbeds which are constructed in labs, hospitals, or other outdoor environments capture the actual aspects of the real world such as: interference from other types of networks including GSM networks, radio stations, and mi-

Survey of Wireless Technologies Coexistence in WBAN Table 1 Methods and Considered Factors in WBANs Coexistence Literature. Factor

Simulation

Distance

SIR Number of interfering nodes Asynchronism Level Antenna Orientation Frequency Channel spacing MAC layer Parameters and Techniques Packet rate

[28] [26] [45] [117]

Analytical Modeling

[74] [11] [42] [77] [41] [66] [74] [11] [33] [89]

[88] [116] [104]

[78] [33] [70] [91] [23]

[45] [23] [47] [88] [21] [24] [117]

[26] [25] [23] [117]

[37] [69] [23] [117]

[37]

Packet size Physical layer modulation scheme Receiver type Nodes mobility and human body

Testbed

[74] [77] [41] [23] [91] [33] [70] [23]

[78] [46] [98] [74] [11] [98]

[117]

[117]

[11] [89] [91] [66]

[21]

[28] [69]

[28] [23]

[41] [89]

[23]

crowave ovens, obstacles, mobility...etc. These interference sources cause the signal propagation and attenuation to be highly affected by the distance. As for mathematical modeling, the propagation models are available in the literature and can be used to reflect the effect of distance. However, the effect is more apparent in testbed scenarios due to the hidden interferences and noises. Similar to the distance, the same issue exists with SIR factor, the antenna orientation of the nodes, the frequency channel spacing, nodes’ mobility, and human body effect. They have palpable impact in testbed scenarios as compared to simulation and mathematical modeling. The antenna orientation, mobility of nodes, and the human body effect have not been studied mathematically. Moreover, many analytical modeling studies considered SIR more than frequency spacing. This is due to their complexity which makes it difficult to include their impact by mathematical equations.

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One observation about the studies that used testbeds is the small network sizes they usually consider. For example, most of the studies, such as [42] [46] [70] [66] [78] [74] [91], adopted simple scenarios consisting of two WBAN nodes and two interfering nodes, i.e. arranged as transmitter-receiver pairs. In other scenarios the studies considered humans moving with WBAN nodes distributed on their bodies, example are [41] [23] [89]. As shown in Table 1 only one study with testbed considered the effect of the number of interfering nodes. However, in simulation and mathematical models adding more nodes is much easier, hence its effect is extensively studied. Surprisingly, factors such as: packet size and data rate have been studied more using testbeds as compared to simulation and modeling studies. Other complex factors such as: the modulation scheme used at the physical layer, the receiver technology type, MAC layer related parameters, and the asynchronism level are mostly studied using simulation. The reason is that changing them in simulation, i.e. coding, is easier than testbeds which needs access to the firmware and hardware of the devices.

5 Interference Mitigation Solutions in WBANs In this section the main solutions that were proposed to mitigate both types of interference, i.e. mutual and cross interference, in WBANs are highlighted. According to the technique adopted by the solutions, we classify them into five categories including: time spacing, frequency spacing, code diversity, standards modification, standards adaptation, and finally hybrid solutions that combine more than one technique to enhance the performance. Figure 3 shows the taxonomy of the reported interference mitigation solutions. In what follows the main advantages and disadvantages of the solutions categories are discussed and examples on each category are presented in details.

5.1 Time Spacing In this category the proposed solutions are based on implementing TDMA technique at the MAC layer. The aim is to avoid simultaneous transmissions that may cause collisions either with packets from the same WBAN, nearby WBANs, or other nearby networks. Data packet rescheduling is used to avoid interference by scheduling transmissions into empty time slots. Since most WBAN technologies use TDMA mode in their standards, e.g. slotted mode in Zigbee or IEEE 802.15.6., thus, rescheduling may be efficient, especially when all WBANs are using the same standard. Consequently,


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Interference Mitigation Solutions Time

Frequency

Code

Standards

Standards

Hybrid

Spacing

Spacing

Diversity

Modification

Adaptation

Solutions

⇓

⇓

⇓

⇓

⇓

⇓

[56] [103] [27] [57]

[39] [63] [109] [111] [40]

[114] [36] [13]

[115] [66]

[46] [61] [106] [49] [51]

[89] [93]

Fig. 3 Taxonomy of WBANs interference mitigation solutions related work.

the solutions that utilize time spacing are more suitable for mutual interference rather than cross interference mitigation. For example, if a WBAN is interfering with a WiFi network then using time spacing is not effective especially when the contention based MAC protocol is used by WiFi. Moreover, a scalability problem may exist when the time frame is divided into time slots that are less than the number of nodes in a crowded network. Therefore, this may result in large data delivery delay which is a concern for critical medical applications. In addition, the rescheduling process may be complicated and requires expensive communications for WBAN nodes, which are equipped with limited batteries. Furthermore, the WBAN nodes need to know the schedule of the nearby WBANs for coordination which requires periodic exchange of information between the WBAN networks. Examples of the proposed solutions that belong to this category includes [56] and [103], other solutions can also be found in [27] and [57]. Cooperative scheduling of TDMA slots is used in [56] where an asynchronous internetwork interference avoidance scheme called AIIA was proposed. AIIA tries to reduce mutual interference between WBANs using a hybrid MAC approach that utilizes both Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism and TDMA. AIIA introduced a new superframe structure which contains two periods: contention-based period during which CSMA/CA is used, and scheduled period during which TDMA is used. Only the WBAN coordinator is required to run AIIA which is responsible of detecting the interferer WBANs and reschedule its TDMA slots based on the offset and busy periods of other WBANs. Thus, when a conflict is found, the coordinator relocates the scheduled period to a different location and the original location is moved to the contention-based period. The periods allocation table is exchanged between the co-

ordinators of nearby WBANs at the beginning of each superframe period to allow conflict-free scheduling. On the other hand, Wang et al. divided the scheduling problem into two phases: scheduling of data packets inside the WBAN itself (intra-scheduling) and scheduling of data packets that come from different WBANs (inter-scheduling) [103]. The required information for the scheduling algorithms are gathered by the coordinator of each WBAN and exchanged between each other periodically. For the intra-scheduling, the authors proposed an algorithm called horse racing scheduling where a WBAN designs its own scheduling strategy to maximize its benefit while assuming that other WBANs fixed their strategies. The same algorithm is used for inter-scheduling where coordinators of the WBANs become the master and send a superframe to schedule all nearby WBANs. This approach is similar to clustering approaches where each coordinator represents a cluster head. The proposed solution aims to maximize throughput while maintaining an acceptable level of fairness between interfering WBANs. 5.2 Frequency Spacing Frequency spacing solutions wisely manipulates the usage of the frequency channels that are available for WBANs. The solutions in this category implements channel assignment algorithms that target multi-channel networks. Based on the detected interference level the nodes are assigned different channels to reduce the impact of interference. The main problem in frequency spacing solutions is the limited number of available channels, especially, when interfering with networks that has large bandwidth frequency channels, e.g. a channel in WiFi may interfere with three or four channels in Zigbee. Moreover, there is no accurate methodology to determinate the level of interference based on SINR, RSSI, channel quality ...etc.


Examples of solutions that adopted this technique are: [39] and [63], other studies can also be found in [109] [111] [40]. Han et al. presented a distributed interference mitigation mechanism that is based on dynamic channel selection for cluster tree Zigbee networks [39]. Interference is detected by sensing the channel or using packet error rate. If high level of interference is defined then the coordinator announces the start of multichannel operation mode in its cluster. In this mode, the nodes in the cluster use channel hopping and transmit data on different channels determined by the coordinator. Moreover, if interference is severe, causing the broadcasted beacons to be lost, then each node maintains a counter for the lost beacons and starts this mode when the counter reaches a threshold value. After monitoring the transmission quality, the coordinator finds the best channel, the one with the best quality, and assigns it as the new channel then return back to the single-channel operation mode. Channel scheduling is also used in [63] to reduce mutual interference between nodes that belong to the same network. One of the channels is assigned as a control channel and used by out-body devices to assign data transmission channels for in-body devices. This assignment is announced using beacon frames which are broadcasted to allow all the devices to know the assigned channels for all their neighbors. The assignment is only preformed for free channels so that out-body devices sense the channels regularly and update the list of free channels. These channels can be reserved and aggregated to be used as one wide band channel to increase the throughput and minimize the delay.

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not required which minimized the computation complexity. Based on that, codes for multi-sensors are computed and are used for transmission. Multi-user detection in CDMA networks was used for interference cancellation in [36] where both the Gaussian noise and Raleigh fading channels were considered. The decorrelator and the minimum mean square error (MMSE) receivers were tested to mitigate interference for synchronous and asynchronous receivers.

5.4 Standards Modification In this category the MAC mechanisms implemented by WBANs standards are studied and analyzed, then they are revised or restructured to enhance coexistence and mitigate interference. However, the market appliances must follow a standard. Thus, these solutions cannot be implemented before they are officially added to the standards.

Examples of the solutions enrolled in this category include [115] and [66]. The authors in [115] proposed several computation-inexpensive techniques to reduce mutual interference effect. The techniques are based on selecting the modulation scheme, the data rate, and the duty cycle operation, i.e. active and inactive periods, based on the interference level which is deduced from the measured SINR value. The goal is to reduce the interference level by selecting a low power operation, i.e. low data rate, long inactive period, efficient modulation scheme. Hence, the proposed work suggested different 5.3 Code Diversity criteria for this selection than the ones used by the stanThis category targets wireless networks that adopt CDMA dard. for data communication. The key idea is to choose orIn [66], beside classifying the interference problem thogonal codes to what is used by the interfering netbetween Zigbee and WiFi into two types: symmetric works to reduce the interference. Code diversity based and asymmetric, the researchers proposed different sosolutions have many challenges and concerns including lutions for each type based on the effect caused by intercomplexity and interference level estimation. Moreover, ference on Zigbee packets. Since the effect of symmetric they require exchange of information either between ininterference corrupts the header of the Zigbee packet, terfering networks or to announce new codes among they suggested the addition of multiple copies of the nodes that belong to the same network. header in the packet. Thus, the first detected header by the WiFi node, which will be corrupted, will cause Solutions proposed in [114] [36] are examples of code the node to back off. After that the packet will reach the diversity usage in interference mitigation, other proposreceiver correctly, i.e. correct header and payload. Howals are found in [13]. A parallel interference cancellation ever, since asymmetric interference may corrupt the the technique that utilizes DS-CDMA (Direct Sequenceentire packet, they used a forward error correction code CDMA) and targets mutual interference in WBANs to correct the corrupted bits which have bursty pattern. was presented in [114]. The interference level was esThese mechanisms are implemented using a protocol timated using a cyclic correlation of the received sigthat is added to the MAC layer of the Zigbee nodes. nal and interfere code. The channel quality status was


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5.5 Standards Adaptation The solutions under this category try to adapt with WBANs standards to reduce interference without modifying the standards. For example, some solutions modeled the white spaces in WiFi networks and exploited them for WBAN data transmission [49]. Others, send fake WiFi data packets with preamble duration sufficient for WBAN nodes transmission [106] or by sending fake RTS packets to reserve the medium and avoid nearby WiFi networks from interfering [46]. The latter assumed that the interfering network can hear WBAN transmission which is not realistic, especially with the low transmission power level used by WBANs. Moreover, these solutions may use a third party devices to implement the proposed techniques which require long period expensive computations to predict free durations or the interference pattern. In addition, hardware design constraints such as: antennas beamforming, multiradio nodes design, and and Multi-Input/Multi-Output (MIMO) capability [61] [51] may limit the usage of the proposed solutions. The standard adaptation techniques seem to be flexible and support contention based MAC protocols, thus many solutions appear to be promising such as those presented in: [46] [61] [106] [49]. Beside studying the coexistence problem between WBAN and WiFi networks, Hou et al. [46] proposed a solution to solve this issue. The solution suggested adding a jammer node to the network that sends fake CTS packets of specific duration, sufficient for Zigbee node data packet transmission, to prevent WLAN devices from sending data over the medium. On the other hand, fake WiFi packets were used in [106] where the authors proposed WiCop framework that tends to exploit the white spaces of interfering WiFi networks. In WiCop each WBAN has a policing node that implements WiCop framework. Before the start of WBAN activity interval the policing node broadcasts a fake WiFi packet that contains a preamble, physical header, and the packet duration, possibly without data. This packet forces nearby WiFi nodes to mute and WBAN nodes may exploit this interval for data transmission. Another mechanism that was also proposed in [106], is DSSS (Direct Sequence Spread Spectrum) nulling. A band-pass filter is used to reshape a DSSS jamming signal, which result from the continuous transmission of repeated WiFi preambles, to have smaller bandwidth that can jam the WiFi devices on a specified channel while not jamming all Zigbee channels in the same frequency band. Thus, the un-jammed Zigbee channels can still be used for communication by the WBAN.

White spaces in WiFi networks were also exploited in [49]. The interference between Zigbee and WiFi was studied empirically based on real life traffic traces and used to model white spaces in bursty WiFi networks. Also, they modeled the behavior of Zigbee links under WiFi interference where the probability of collision was analyzed mathematically. Based on that, the authors proposed a frame control protocol called WISE which predicts the white spaces in WiFi using the developed model and adjusts the frame size to fit into the available free time. Multiple-antenna and beamforming techniques were utilized in [61] where a cognitive smart grid network protocol has been proposed. Zigbee nodes monitor the transmission time of frames needed by WLAN devices which is used to compute the beamforming vector for the antennas. This vector guarantees a satisfactory rate for Zigbee nodes while sharing the spectrum with WLAN devices and allows data transmission with the interfering WiFi networks simultaneously. Nodes clustering and multi-radio nodes design were exploited in [51] to mitigate interference in Zigbee networks coexisted with WiFi networks. The nodes were grouped into clusters where Zigbee was used for intracluster communication and WiFi was used for intercluster communication. This was enabled by the usage of cluster heads that are equipped with dual radios one for Zigbee and another for WiFi. Data aggregation and delayed transmission were used to reduce interference between the two heterogeneous radios as well as with nearby nodes. 5.6 Hybrid Solutions Finally, some WBAN interference mitigation solutions proposed hybrid approaches, i.e. mix of the aforementioned categories. This is done to alleviate the main disadvantages of a technique while keeping most of its good features. Thus, the adopted technique is changed adaptively according to the network status and available resources. Examples of studies under this category are [89] and [93]. Silva et al. [89] studied coexistence between WBANs in which they assumed the existence of a fixed network with nodes that are capable of controlling and communicating with all WBANs around them. The fixed network composed of two modules: the interference prediction module and the resource arbitrator. The former estimates interference level based on the distance and RSSI. While the latter decides how to reduce the interference based on the local data from the operation of the different WBANs and the interference level. Reducing the interference is performed by having the interfering network coordinator implements one of


the following techniques: operate on different time slots, different frequency channels, or use different codes for CDMA. A distributed mutual interference mitigation method that is based on both data packets transmission and channel scheduling for beacon-enabled Zigbee networks was proposed in [93]. Each WBAN monitors the activity of all interfering WBANs in its vicinity. Based on the collected information the WBAN has two options: either to reschedule its transmissions to empty time slots, or to switch to another idle or less congested channel in case packets rescheduling is not possible. After rescheduling and to avoid the problem of incomplete neighbors list, carrier sensing is used to check the status of the medium before starting packets transmission.

6 System Model and Problem Definition This section defines the problem being investigated in the coexistence analysis of the different WBANs technologies. In addition, the system model, its parameters and assumptions are described in details. The goal of the analysis is to provide quantitative results for the performance of each WBAN technology (i.e., Zigbee, IEEE 802.15.6, and low-power WiFi) under the interference of high power WiFi devices. We assume a star topology based WBAN, where all nodes are associated with a master node. The nodes send data packets periodically to the master where each node is associated with one master that is assigned at the network configuration phase. In the analysis, only intra-network traffic is considered, i.e. between the nodes and the master, the inter-network traffic is not considered, i.e. accumulated data sent by the master to the medical staff possibly using wired or wireless connection with different band. As discussed earlier, three main standard technologies are considered in the analysis for the WBAN implementation: IEEE 802.15.4 (Zigbee), IEEE 802.15.6, and the low-power WiFi. The source of interference is assumed to be a standard WiFi network, we refer to it as std-WiFi in this paper, which is structured either as a Basic Service Set (BSS), where an arbitrary number of clients are associated with one AP, or as an Extended Service Set (ESS), where the clients are distributed among multiple APs [2]. The stdWiFi network is assumed to operate with IEEE 802.11g for the MAC and the physical layers. All nodes in the WBAN and std-WiFi networks are equipped with a single radio interface which is statically assigned a frequency channel. In addition, in our analysis the nodes are assumed to be static and mobility of nodes is not considered.

17

Two ranges are associated with each node within any network, the transmission range T R and the interference range IR. Each node must be within the transmission range of its master node or AP to enable communication between them. Other nodes that exist within the interference range, which is typically twice to trice the transmission range, of either the node or its master/AP are able to sense all ongoing transmissions, hence, they cannot use the medium simultaneously. Table 2 summarizes the main notations used throughout the paper.

Table 2 Notations

Notation Meaning VB Set of nodes within the WBAN network. VL Set of nodes within the std-WiFi network. MB The master node in the WBAN network. ML The AP in the std-WiFi network. CB The operating frequency channel in the WBAN network. CL The operating frequency channel in the stdWiFi network. T RiB The average transmission range of node i, where i ∈ VB . IRiB The average interference range of node i, where i ∈ VB . The average transmission range of node i, T RiL where i ∈ VL . IRiL The average interference range of node i, where i ∈ VL . Rm Time needed to reserve the medium. Dxchg Time needed for a correct data packet exchange. Tp Transmission time of a data packet. Tack Transmission time of an acknowledgment. Ai Interarrival time of the ith data packet. d(u, v ) The Euclidean distance between nodes u and v .

Based on the aforementioned issues we define the problem of Fair Coexistence of WBAN and std-WiFi networks (FCWW) as follows: Problem Statement (FCWW): Given two interfering WBAN and std-WiFi networks modeled as an undirected graph G(M, V, E, C) where M is the master node or the AP, V is the set of nodes, E is the set of links between nodes, and C is the used frequency channel, find the best network settings to enable fair usage of the medium between the two networks while reducing the number of collisions, the number of retransmissions, and the waiting time to reserve the medium. In the next section we study the FCWW problem and derive an analytical model for the probability of a WBAN node to access the medium for transmission while co-


18

existing with std-WiFi network. The problem is then studied using extensive simulations.

7 Coexistence Analysis of WBANs and WiFi Networks In this section, the MAC operation of WBANs and WiFi is studied. We thoroughly analyze the timing aspect of these standards to estimate when a WBAN node is able to reserve the medium successfully and transmit its data to the master node. At first, an overview of WiFi MAC operation, particularly the IEEE 802.11g, is presented. Then, the WBANs standards MAC operation are discussed. The considered scenario in the mathematical analysis is similar to the network setup shown in Figure 4.

Fig. 4 Interfering WiFi and WBAN networks.

7.1 IEEE 802.11g WiFi MAC Operation

nc = 1, 2, ..., 13

packet. On the other hand, if the medium is found busy the node waits until the medium is sensed to be free for a DIFS period and starts the backoff process. In this process, the node picks a backoff period, i.e. number of backoff slots, uniformly from the interval [0, CW], where CW is the contention window size. The backoff timer will be decreased by one time slot as long as the medium is idle and will be frozen while the medium is busy. When the timer reaches zero the node starts transmission. The receiver upon receiving the data packet sends an acknowledgment (ACK) to the sender after a Short Inter-Frame Space (SIFS) period, only if the data packet is received correctly. If the sender does not receive an ACK picks another backoff after doubling the CW value period and then resend the data packet. When CW reaches the maximum allowed value it remains the same until being reset to the minimum value after a successful packet transmission, i.e. ACK is received. If the packet retry counter reaches its maximum allowed value then the data packet is discarded. The standard values of the IEEE 802.11g MAC and physical layer parameters are listed in Table 3.

Table 3 IEEE 802.11g Parameters

IEEE 802.11g [3] operates in the 2.4 GHz band, it has 13 frequency channels with a bandwidth of 22 MHz and channel spacing of 5 MHz. The center frequency fc of channel nc is computed as follows: fc = 2407 + 5.0 ∗ nc (M Hz),

Fig. 5 IEEE 802.11g MAC operation.

(1)

Since the channel spacing is smaller than the channel bandwidth, the 13 channels cannot be used simultaneously without overlapping. The most popular nonoverlapping set of channels used in std-WiFi networks are channels 1, 6, and 11. The IEEE 802.11g standard supports data rates up to 54 MHz with a coverage range of 100 to 150 m. For the medium access we consider the Distributed Coordination Function (DCF) MAC operation that uses CSMA/CA mechanism. Its basic operation is captured in Figure 5. When a node has a packet to transmit it senses the medium and if it is found to be idle for a DCF InterFrame Space (DIFS) duration then the node sends the

Parameter

Value

DIFS SIFS CW Size

28 µs 10 µs CWmin = 15 to CWmax = 1023 Slot 9 µs 20 dBm 2346 byte 192 µs (long), 96 µs (short)

Backoff Slot Transmit Power Max MAC Frame Size PLCP Preample and Header Tx ACK Data Rate Coverage Range Max No. of Retransmissions Per Packet

14 byte @ 1 Mbps 54 Mbps 100 to 150 m 7

The parameter values in Table 3 indicates that IEEE 802.11g standard has short slots and inter-frame space durations which increase the probability of reserving the medium. Moreover, the high transmission power


19

causes larger interference effect on nearby nodes and longer interference range. In the up-coming subsections we show how the differences in parameters values between the wireless technologies give preference to stdWiFi network in using the medium compared to other WBANs types.

additional challenge where these beacons could be lost. Moreover, the beacons loss probability increseases under the existence of an interfering network. Accordingly, only the unslotted CSMA/CA MAC mechanism is considered in the analysis. In the unslotted CSMA/CA protocol each node maintains two variables: a backoff exponent (BE) and a counter of the number of occurred backoffs (NB). When 7.2 IEEE 802.15.4-based WBANs and IEEE 802.11g a node has a packet to transmit it waits for a backoff MAC Interoperation period picked uniformly from the range [0, 2BE - 1]. The backoff timer will be decreased with time regardless of IEEE 802.15.4 [4] is defined to operate in two frequency the current state of the channel, i.e. busy or idle. When bands: the 868/915 MHz and the 2.4 GHz band. It this counter reaches zero the node checks whether the has 16 frequency channels (channels 11 to 26) in the channel is idle for a Clear Channel Assessment (CCA) 2.4 GHz band each with 2 MHz bandwidth. The chanperiod. If the channel is found to be idle the node sends nel spacing is 5 MHz, hence, the 16 channels are nonthe data packet. Otherwise, BE is increased by 1, if BE overlapping. The center frequency fc of channel nc is < BEmax , and also NB is increased by 1, after that the computed as follows: node picks another backoff period to repeat the CCA fc = 2405+5.0∗(nc −11) (M Hz), nc = 11, 12, ..., 26(2) process. When NB reaches its maximum allowed value the data packet is dropped. On the receiver side, upon As noted, Zigbee channels and WiFi channels are inthe reception of a correct data packet, the receiver sends terfering with each other in the 2.4 GHz band, Figure 6 an ACK to the sender after a SIFS period. The values illustrates this interference [46]. Despite the fact that of the main physical and MAC layers parameters of the the Zigbee channels are non-overlapping and a good channel assignment policy can distribute the nodes among IEEE 802.15.4 standard are listed in Table 4. these channels to avoid intra-network interference, still there is a high possibility to have Zigbee devices operate Table 4 IEEE 802.15.4 Parameters on the same frequency channel used by WiFi devices.

Fig. 6 Frequency channels of IEEE 802.11g and 802.15.4 in

Parameter

Value

SIFS CCA NB BE Backoff Slot Period Transmit Power Max MAC Frame Size PLCP Preample and Header Tx ACK Data Rate Coverage Range Max No. of Retransmissions per Packet

192 µs 128 µs 0 to 5 0 to 5 (default BEmin = 3). 320 µs 0 dBm 127 byte 192 µs 5 byte @ 250 Kbps 250 Kbps 10 to 20 m 3

the 2.4 GHz band.

The main objective of the Zigbee standard is to define a low-power/low-rate wireless networking paradigms. For this purpose, the supported data rate in the 2.4 GHz band is 250 Kbps with a coverage range of 10 to 20 m. As for the MAC layer, IEEE 802.15.4 defines two flavors of CSMA/CA; slotted and unslotted one. In the slotted version the network operation time is divided into equal time periods called superframes. The master node sends beacons periodically to synchronize all the nodes to operate within the superframe boundaries. Thus, this MAC technique introduces an

Compared with the parameters presented earlier for IEEE 802.11g, it is noted that Zigbee has longer time durations for the variables that control the medium access. Hence, std-WiFi devices are more likely to reserve the medium faster and more frequent than Zigbee devices. Figure 7 illustrates a situation where two nodes, interfering std-WiFi and Zigbee, are trying to reserve the medium. Let us assume that the medium is idle and the two nodes have started medium sensing together. Zigbee starts by a backoff duration which is much longer than a DIFS in std-WiFi, where one Zig-


20

bee backoff slot duration is 320 µs compared to 28 µs std-WiFi DIFS. Thus, it is more likely for the std-WiFi node to reserve the medium. Even if the std-WiFi node finds the medium busy after the DIFS since its backoff duration is shorter than the Zigbee backoff, at least initially (the std-WiFi backoff slot duration is only 9 µs), std-WiFi still have the privilege to reserve the medium. Moreover, despite the fact that the size of data packets in std-WiFi networks is much larger than those used by Zigbee nodes, std-WiFi transmission occupies the medium for shorter time due to its high data rate. Consequently, a std-WiFi node may complete transmission and reserve the medium for another packet while the Zigbee node is still performing the backoff process.

packet(s) to transmit) in which vL is either using the medium or trying to reserve it. The two states and the transitions between them are modeled in Figure 8.

Fig. 8 Std-WiFi node states.

Two cases are considered; in the first one the stdWiFi node is able to sense the WBAN node transmission. This means that the distance between vL and vB is less than the interference range of vB , i.e. d(vL , vB ) < IRvB . In this case, if vB is transmitting then vL finds the medium busy and will not be able to transmit. In the second case the std-WiFi node is unable to hear the WBAN node transmission, i.e. d(vL , vB ) > IRvB . Thus, even if the medium is reserved by vB , vL cannot detect this situation and it may start transmission. A collision Fig. 7 IEEE 802.11g and IEEE 802.15.4 MAC interoperamay occur in this case, and vB packet will be corrupted. tion. However, this collision will not affect vL data packet, since it uses high transmission power and vB transThe probability of a WBAN node to access the medium mission will be considered as a tolerable white noise. As shown in Figure 7, and similar to [116], the Zigbee is mathematically analyzed in this section. Particularly, we are interested in the Successful Channel Access (SCA) node will have a successful transmission if the idle time denoted as tidle , which is the time during which the which is defined as follows:: std-WiFi node is not using the medium, satisfies the following condition: DEFINITION (SCA): A WBAN node will success Zigbee fully access the channel if it finds the medium idle,  Tbackof if d(vL , vB ) < IRvB  f + CCA ,   starts data packet transmission, and receives the ACK (3) tidle ≥ without having collisions caused by std-WiFi traffic. Zigbee  Tbackof  f + CCA + Tp + SIF S + Tack ,   if d(vL , vB ) > IRvB As previously elaborated, we considered a simple inFor simplification, we define the following variables: terfering network setup, shown in Figure 4. Initially, we assume that a WBAN node vB is affected by only Zigbee Zigbee Rm = Tbackof (4) f + CCA one WiFi node vL , i.e. within the area enclosed by the which is the time needed to reserve the medium for dashed circle in Figure 4. Thus, we do not consider a Zigbee node, and: the possible contention effect between WBAN nodes themselves. For simplicity, we assume error-free chanZigbee Dxchg = Tp + SIF S + Tack (5) nel and each packet is allowed to be sent only once. Also, we assume that the channel frequency used by vB which is the total time needed for a correct exchange of a data packet between two Zigbee nodes. This time is in the same band covered by the channel used by vL , includes the transmission time of a data packet at the i.e. CB ⊂ CL , and the propagation delay is neglected. The packets arrival process at vL is assumed to be a maximum allowed data rate, the transmission time of an ACK packet, in addition to the SIFS period the stationary Poisson arrival process in which the interarreceiver waits before sending the ACK. The std-WiFi rival time of data packets is modeled as an exponential distribution with a data packet arrival/generation node tidle includes the interarrival time of the ith data packet, Ai , and the time imposed by the IEEE 802.11g rate denoted as λ. Accordingly, vL has two possible MAC policy before reserving the medium, i.e. DIFS and states: state 0 (no packet to transmit), and state 1 (has


21

W iF i the backoff period, denoted as Rm . Thus, tidle value is given by the following equation: W iF i W iF i ∼ tidle ∼ = Ai + DIF S + Tbackof f = Ai + Rm

(6)

simultaneously. From Figure 9 it can be revealed that λef f ective ∼ = [0, 1800] for d(vL , vB ) < IRvB and around [0, 375] for d(vL , vB ) > IRvB .

By substituting equation 6 in equation 3 then the Zigbee node is able to reserve the medium if the minimum value of the interarrival time of vL data packets satisfies the following condition:  Zigbee W iF i Rm − Rm , if d(vL , vB ) < IRvB    Amin = (7) Zigbee i W iF i RZigbee + Dxchg − Rm ,    m if d(vL , vB ) > IRvB Accordingly, the probability of successful channel access PSCA is given by: Z ∞ min λe−λt = e−λAi (8) PSCA = Amin i

Equation 8 indicates that as λ increases the probability of successful channel access will decrease. This is expected since a more congested vL implies less available idle time for vB . We computed the values of Amin i for both cases of equation 7. For Zigbee parameters values we used the standard values listed in Table 4 and considered the average case for the number of backoff slots, i.e. BE = 3 and the number of backoff slots = 4. We also used the maximum MAC frame size, and for both the data packet and the ACK frames a PLCP preamble and header were added. Likewise, for stdWiFi we used the standard parameters values listed in Table 3. The average case is considered to compute the backoff period, CW = CWmin = 15 when sending a packet for the first time, thus, CWavg = 8. Based on this, Amin equals 1.308 ms for d(vL , vB ) < IRvB , and i 6.108 ms for d(vL , vB ) > IRvB . Thus, equation 8 is modified as follows: ( −3 e−1.308×10 λ , d(vL , vB ) < IRvB PSCA = (9) −3 e−6.108×10 λ , d(vL , vB ) > IRvB To analyze the effect of vL packets rate on vB operation, PSCA in equation 9 is plotted as a function of λ and the result is shown in Figure 9. As expected, the increase in λ has a negative effect on PSCA of vB where this effect is significant when vL lies outside IRvB due to the longer time vB needs to complete a correct data transmission. For example, as Figure 9 illustrates, when λ∼ = 400 packet/sec vB gets a chance of 60% to reserve the medium when vL is able to hear vB while it only has 10% chance when vL cannot hear vB . In order to compare the different WBAN technologies, we define the effective range of λ to be the range in which the value of PSCA drops from 1 to 0.1. That is the range at which vB can only deliver 10% of its generated data given that vL and vB always start the medium reservation process

Fig. 9 PSCA of Zigbee with one std-WiFi node

The analysis of PSCA is extended to include the effect of multiple std-WiFi interfering nodes that may exist around vB (in this case vL refers to the set of interfering nodes). Each node in vL experiences an arrival rate of data packets that is independent from the others. Hence, PSCA (n), where n is the size of vL , is given by: n Y min PSCA (n) = e−λj Ai (10) j=1

To analyze the effect of increasing number of interfering std-WiFi nodes, PSCA in equation 10 is plotted as a function of n and the result is shown in Figure 10. In this case, λ was set to 100 packet/sec and all nodes in vL are assumed to experience the same arrival rate. The reason of selecting a small value for λ is to make the impact of increasing n on performance degradation clearer as reflected on PSCA value. Figure 10 shows that PSCA drops from 0.88 for n = 1 to 0.27 as n reaches 10 in the case when all interfering vL nodes are able to hear vB . On the other hand, when the entire set of vL cannot detect vB then PSCA starts with 0.54 and drops to 0 as n reaches 8. Therefore, the impact of increasing n on PSCA is more significant than the impact of increasing λ. This is due to the fact that increasing the number of interfering nodes increases the probability of collision and the medium contention. 7.3 IEEE 802.15.6-based WBANs and IEEE 802.11g MAC Interoperation IEEE 802.15.6 [6] standard is defined to operate on different frequency bands which are grouped into three


22

Fig. 11 Frequency channels of IEEE 802.11g and 802.15.6 in

the 2.4 GHz band. Fig. 10 PSCA of Zigbee with n std-WiFi node

categories: The Narrowband (NB), the Ultra Wideband (UWB), and the Human Body Communication (HBC). NB includes the 400, 800, 900 MHz and the 2.3 and 2.4 GHz bands, whereas UWB uses the 3.1-11.2 GHz, and finally HBC occupies the frequencies in the ranges 10-50 MHz. As elaborated earlier, only the 2.4 GHz band is considered in this analysis. In this band, the IEEE 802.15.6 has 79 frequency channels with 1 MHz for the bandwidth and 1 MHz for the channel spacing. Hence, the 79 channels are tightly adjacent. The center frequency fc of channel nc is computed as follows: fc = 2402 + 1.0 ∗ nc (M Hz),

nc = 0, 1, ..., 78

(11)

As noted, and similar to the situation found in the Zigbee standard, the IEEE 802.15.6 channels and WiFi channels are interfering with each other. Moreover, there is a high probability for the IEEE 802.15.6 devices to operate on the same frequency band that is used by nearby WiFi devices, as illustrated in Figure 11. In addition, the adjacency of the IEEE 802.15.6 channel may cause an additional interference. As a result, coexistence of std-WiFi may create a serious problem for WBANs that are based on IEEE 802.15.6 standard, as will be shown later. The basic objective of IEEE 802.15.6 is to provide a low power communication standard developed specifically for WBANs applications while supporting high data rate as compared to Zigbee. In addition, this standard considers networking issues of both on-surface and implanted devices inside the human body. Moreover, it classifies the transferred data according to its importance from being urgent to regular periodic data, i.e. priority assignment. The supported data rate in the 2.4 GHz is 0.9714 Mbps and the coverage range is 2 to 5 m. For the MAC layer, IEEE 802.15.6 divides the operation time into superframes which is similar to the slotted CSMA/CA operation in Zigbee. Three modes of operation are available, the first is the beacon mode with

superframe boundaries where the master synchronize all nodes in its WBAN using periodic beacon signals. The superframe is further divided into multiple slots or phases with different access techniques. The second mode is the non-beacon mode in which the network operation is still divided into superframes. However, no synchronization exists in this mode and the entire superframe is driven by one medium access policy. The third mode is non-beacon mode, without superframe boundaries, where the network employs the ordinary CSMA/CA mechanism for medium access, which is the one considered in this analysis. In the IEEE 802.15.6 standard, when a node wants to send a packet it starts the medium reservation process by a random backoff duration randomly selected from the range [1, CW]. The minimum and maximum values of CW depend on the data priority where urgent data get lower range to enable fast access to the medium. The behavior of the backoff counter is characterized by two states; Lock (freeze) and unlock (decrement). Initially the backoff is locked and will not start decrementing till the medium is sensed idle for a pSIFS period. After that, the counter is decremented by 1 for every CSMA slot (or backoff slot), given that the channel is idle during that slot. On the other hand, it will be locked while the channel is busy and will be unlocked again if the unlock condition is met, i.e. idle medium for a pSIFS period. This process continues until the counter reaches zero and at that time the node will send its data packet. In case of transmission failure, i.e. no ACK is received, CW doubles in size for every even number of failures till it reaches CWmax where it remains constant. The data packet is dropped if the maximum allowed number of retransmission is reached, while CW will be reset to CWmin upon the reception of a valid ACK. The values of the main physical and MAC layers parameters of the IEEE 802.15.6 standard are summarized in Table 5.


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Table 5 IEEE 802.15.6 Parameters Parameter

Value

pSIFS CSMA Slot (Backoff Slot) CW Size

75 µs 145 µs

Transmit Power Max MAC Frame Size PLCP Preample Tx PLCP Header Tx ACK Data Rate Coverage Range Max No. of Retransmissions per Packet

CWmin = 4, CWmax = 8 (Medical data type, i.e. UP = 5) -10 dBm 264 byte 480 µs 331 µs 3 byte @ 121.4 Kbps 0.9714 Mbps 2 to 5 m 10 (master node packets), 2 (normal node packets)

Compared to Zigbee, IEEE 801.15.6 has smaller values for the backoff and pSIFS slots, thus it will have a higher chance to access the medium. On the other hand, it has much longer durations for the preamble and the PLCP header, however, this is accompanied by larger packet size limit and higher data rate compared to Zigbee. Compared to std-WiFi, IEEE 802.15.6 has longer time periods for the variables used by the MAC layer. Hence, a std-WiFi device has a higher priority for medium reservation. Figure 12 shows a case in which there are one std-WiFi node (vL ), and one WBAN node (vB ) that are interfering with each other and both have data packets to transmit. The medium is assumed to be idle and both nodes started medium sensing simultaneously. vB starts with a pSIFS period to check whether it is able to unlock the backoff counter or not. Since IEEE 802.15.6 pSIFS is longer than std-WiFi DIFS, vL will start transmission and vB ’s backoff counter will be locked. In another scenario, which is also illustrated in Figure 12, vL is forced to perform the backoff process, i.e. if the medium is busy during the DIFS. Despite the fact that the std-WiFi backoff duration is picked from larger CW range than IEEE 802.15.6, vL backoff counter will reach zero earlier than vB since its slot duration (9 µs) is much smaller than the CSMA slot used in IEEE 802.15.6 (145 µs). Thus, vL will successfully reserve the medium and vB is locked again and will be unable to access the medium as long as vL has packets to send. In what follows, PSCA for IEEE 802.15.6 WBAN nodes is examined with the same procedure that was used for Zigbee analysis. The same assumptions and node model that were previously used are applied here. Moreover, the same two cases are considered; vL is able and unable to sense vB transmission. From Figure 12 it is inferred that the IEEE 802.15.6 vB will have a suc-

Fig. 12 IEEE 802.11g and IEEE 802.15.6 MAC interopera-

tion.

cessful transmission if tidle of vL satisfies the following condition:  15.6 , if d(vL , vB ) < IRvB  Rm tidle ≥ (12)  15.6 15.6 Rm + Dxchg , if d(vL , vB ) > IRvB 15.6 Where Rm is the time needed to reserve the medium 15.6 and Dxchg is the complete data exchange time for an IEEE 802.15.6 node, which are computed as follows: 15.6 15.6 Rm = pSIF S + Tbackof f

(13)

15.6 Dxchg = Tp + SIF S + Tack

(14)

In this case, PSCA will be similar to that described in equation 9 with the difference in Amin values in both i cases. Based on the standard parameters values presented in Table 5 and considering the average case of the number of backoff slots, i.e. CW = 4 and number of backoff slots = 2, the maximum MAC frame size, and accounting for the addition of the PLCP preamble and header, the value of Amin is found to be 0.265 ms for i d(vL , vB ) < IRvB , and 4.134 ms for d(vL , vB ) > IRvB . Therefor, equation 8 is updated as follows: ( −3 e−0.265×10 λ , d(vL , vB ) < IRvB PSCA = (15) −3 e−4.334×10 λ , d(vL , vB ) > IRvB The function given by equation 15 is plotted with respect to λ and the result is shown in Figure 13. The finding is similar to that seen with the Zigbee analysis where larger arrival rate results in smaller PSCA value. However, the degradation in PSCA is slower with the IEEE 802.15.6 standard case, where vB in this case can tolerate larger values of λ. For example, λef f ective ∼ = [0, 9000] for d(vL , vB ) < IRvB , which is 5 times the Zigbee value, and about [0, 530] for d(vL , vB ) > IRvB , which is 1.4 times the Zigbee value. Thus, the performance of IEEE 802.15.6 standard is significantly better than Zigbee especially when vL is able to sense vB . The analysis of PSCA is also extended to include the effect of multiple std-WiFi interfering nodes. This relation is similar to that found in equation 10 with the difference in the value of Amin , the same values used in i equation 15 must be used. Figure 14 depicts the relation


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Fig. 13 PSCA of IEEE 802.15.6 with one std-WiFi node

between PSCA and the number of std-WiFi nodes for λ = 100 packet/sec which is constant for all n nodes in vL . As shown in the figure, the increased contention level resulted from having larger n negatively affects the chance of vB to access the medium. PSCA value drops from around 0.98 for n = 1 to 0.77 when n reaches 10 for d(vL , vB ) < IRvB , as compared to 0.88 and 0.27 with Zigbee (50% enhancement for the worst case). On the other hand, PSCA value starts with 0.64 and reaches 0 as n increases from 1 to 10 in case that all std-WiFi nodes are not able to sense vB , however, Zigbee reached 0 at n = 8. As illustrated, IEEE 802.15.6 offers great enhancement over Zigbee especially for the case when all nodes can sense each other.

ified to support low power operation and make it suitable for WSNs applications. It employs duty cycling operation, similar to that used by Zigbee networks, where the node goes to sleep mode for most of the time and wakes up only when there is a need to transmit data. The high data rate is an advantage in this case as it reduces the time needed to send the data and return back to the sleep mode. Furthermore, it uses the same CSMA/CA mechanism used by standard WiFi networks with a slight change in the parameters values to make it more suitable for low power operation. An interesting motivation of using this technology for WBANs is to reduce the interference effect of WiFi networks. Low-power WiFi networks proved their capability to deliver the best performance when using small data packets at low data rate while interfering with standard WiFi networks [98]. In what follows PSCA is analyzed for low-power WiFi WBAN nodes, similar to the approach adopted in the previous subsections. The same assumptions and node model are used in this analysis. Again, two cases are considered; the std-WiFi node is able to sense the WBAN node transmission and the case when it is not able to sense the WBAN transmission. vB will have a successful transmission if tidle of vL satisfies the following condition:  W iF i , if d(vL , vB ) < IRvB  Rm (16) tidle ≥  W iF i lp−W iF i + Dxchg , if d(vL , vB ) > IRvB Rm lp−W iF i Where Dxchg is the time needed to complete the data exchange for a low-power WiFi node and is computed according to the following equation: lp−W iF i Dxchg = Tp + SIF S + Tack

Fig. 14 PSCA of IEEE 802.15.6 with n std-WiFi node

7.4 Low-power WiFi-based WBANs and IEEE 802.11g MAC Interoperation The Low-power WiFi provides similar services to those provided by IEEE 802.11g, however, it has been mod-

(17)

As noted, both vB and vL need the same period of time to reserve the medium. This is due to the fact that the low-power WiFi have not modified the parameters used in the original operation of CSMA/CA mechanism. Thus, when vL is able to hear vB the setup appears as a single WiFi network where all nodes get a chance to access a medium controlled by the same protocol. This is the best coexistence situation with stdWiFi network where WBAN nodes are treated as WiFi nodes. Consequently, in this analysis we focus on the case when vL is not able to hear vB . Substituting equation 6 in equation 16 results the following equation: n lp−W iF i Amin = (18) Dxchg , d(vL , vB ) > IRvB i As for PSCA analysis, we used a packet size of 128 byte, a data rate of 1 Mbps, and 9 dBm for the transmission power level. The other parameters are the same as presented in Table 3 which are used by standard IEEE


25

802.11g devices. Hence, with the usage of short PLCP preamble and header, the minimum value of Amin equals i min 1.338 ms. Substituting Ai in equation 8 results the following: PSCA =

n

−3

e−1.338×10

λ

, d(vL , vB ) > IRvB

(19)

The function given by equation 19 is plotted with respect to λ as shown in Figure 15. Obviously, more condensed std-WiFi network implies less chance for vB to access the medium. Compared to both Zigbee and IEEE 802.15.6, low-power WiFi exhibits the best performance. As illustrated in Figure 15, λef f ective ∼ = [0, 1720] Fig. 16 P SCA of Low-power WiFi with n std-WiFi node when d(vL , vB ) > IRvB which is about 4.6 times Zigbee range and 3.2 times IEEE 802.15.6 range. This is due to the higher data rate and shorter time periods used 7.5 Other WBAN Communication Technologies by MAC and physical layers parameters as compared to Zigbee and IEEE 802.15.6. As discussed in the previous section, in the mathematical analysis only the three most common WBAN wireless technologies operating at 2.4 GHz were considered. However, there are other less common wireless technologies that were adopted by researcher for WBAN devices. In this section some of these wireless technologies are briefly described. ANT is a proprietary wireless technology designed for sensor networks [15] [18]. It uses lightweight protocol stack, very low power consumption, 1 Mbps data rate, operates at 2.4 GHz ISM band, and uses TDMA for medium access. Z-Wave (http://www.z-wave.com) is another proprietary wireless technology that also operates at 2.4 GHz ISM band. It is known to be simple, reliable, and uses low-power radio with good penetration properties [18]. Other wireless technologies that are Fig. 15 PSCA of Low-power WiFi with one std-WiFi node suitable for WBANs and operating at 2.4 GHz or other bands are discussed in [76] [18] [15]. On the other hand, a new form of wireless commuThe analysis of PSCA is extended to include the efnication known as IntraBody Communication (IBC) is fect of multiple std-WiFi interfering nodes, following recently getting more attention from researchers [85]. the same approach and assumptions presented in the IBC is non-RF wireless communication technique and is previous subsections. The value of Amin = 1.338 ms is i defined in the newly approved IEEE 80.2.15.6 WBANs substituted in equation 10. Figure 16 shows the relation protocol. It makes use of the human body as a transbetween PSCA and the number of std-WiFi interfering mission medium for the electrical signals. In [107] renodes. Figure 16 shows that as n increases the consearchers showed that IBC is likely to be a preferred tention level is increased causing a negative impact on technology as it can operate with (100 kbps) data rate. from around 0.87 for n = 1 to 0.26 when n reaches 10. The analysis presented in this section can be applied This is compared to dropping from 0.54 for n = 1 to 0 to analyze the coexistence of any WBAN using wireless for n = 8 with Zigbee. Whereas, it dropped from 0.64 technology, other than the three analyzed technologies, for n = 1 to 0 for n = 10 with IEEE 802.15.6 case. with any interfering network (other than std-WiFi) opHence, even in the worst case when low-power WiFi erating at the same band. Hence, one can use our prois not heard by std-Wifi network and with the largest posed approach by changing the parameters values acnumber of interfering nodes, low-power WiFi offers a cording to the wireless technology under study. chance of around 25% for vB to deliver its data.

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8 Simulation Setup and Results This section introduces the WBAN simulation study that is performed in this paper. We used OPNET simulator to evaluate the performance of WBAN as standalone network and under the existence of another interfering network composed of IEEE 802.11 WiFi nodes. Specifically, we study two standards of WBANs; the IEEE 802.15.6 and low-power WiFi where we refer to the latter as LP-WiFi. Zigbee-based WBANs were extensively studied by researchers in the literature, e.g. [37] [33] [26] [25]. Further, [113] surveyed the studies of coexistence between IEEE 802.11 and IEEE 802.15.4based networks. Hence, it is not explored or simulated in this section. The IEEE 802.15.6 WBAN is formed by customizing the node’s module in OPNET according to the specifications described in [58]. For the implementation of the LP-WiFi WBAN, we adopted similar settings to that used in [98]. The nodes use 802.11g with a constant data rate of 1 Mbps and they are set to operate at 8 mW (≈9 dBm). The receiver sensitivity was set to be -85 dBm and the network operates with no RTS/CTS mechanism. It was shown in [98] that large packet sizes caused the network to consume more energy and affect its performance. Moreover, operating at higher data rates increases the packet loss probability. Hence, the packet size is fixed to be 40 bytes with a rate of 1 packet/ 5 seconds unless specified different rate in some scenarios. Both networks, i.e. IEEE 802.15.6 and LP-WiFi WBANs, consist of multiple nodes arranged in a star topology that covers a small area of 10×10 m. All nodes send their data to a master node which is the coordinator of the network. Finally, the nodes inside the interfering network, i.e. std-WiFi network, are operating with the following specifications: transmission power of 30 mW (≈ 15 dBm), IEEE 802.11g with data rate of 54 Mbps. The packet size is chosen to be 1440 bytes where the network operates under maximum offered load, i.e. at the maximum packet rate after which the packets will be lost, with a total of 1640 packets/sec for all nodes. For statistical validation all our OPNET simulations were repeated 30 times and the results were averaged with 95% confidence interval. During simulations the following performance metrics were adopted: – Average network traffic load: this is the actual traffic load of a node which represents the total bits per second transmitted by the node. It includes all layers headers, trailers, preamble, and most importantly retransmissions of data packets, if any. – Average number of retransmission attempts per data packet.


– Average number of backoff slots per node. – Average end-to-end packet delay. – Packet delivery success rate which reflects the network throughput The conducted simulation experiments fall into three different scenarios that reflect real-life situations. The details of these scenarios are as follows: – Scenario 1: In this scenario the WBAN operates alone without any interfering network. The number of nodes are increased from 10 up to 50 nodes, and up to 150 nodes in some experiments, by adding 10 nodes every round. Hence, this scenario studies mutual-interference issue of WBANs which basically affects its scalability. This situation may be encountered in hospitals where many patients are located in a small area. Each patient has a WBAN attached to his/her body for health status monitoring. When multiple patients are moving around, their WBANs will interfere with each other. In the simulations, this scenario is referred to as “WBAN-Alone” scenario. – Scenario 2: This scenario studies the impact of interference caused by a dense std-WiFi network on WBANs. The interfering network is composed of 20 WiFi nodes associated with one AP. Such situation can be faced in real life, for example, when a patient is sitting in a cafe or conference/class room surrounded by a large number of people who are connected to the same AP. Those users may be using heavy traffic Internet applications such as: video streaming, large file transfer ...etc. This scenario will be marked as “1APx20” in the simulations. – Scenario 3: It is likely to be surrounded by multiple APs with a moderate number of WiFi stations. For example, patients with WBAN nodes at home or work may be interfering with their WiFi networks, neighbors’ networks ...etc. Thus, this scenario tests the performance of WBANs with the existence of a distributed std-WiFi network composed of three access points with five WiFi nodes associated with each one. This scenario is referred to as “3APx5” in the simulation results. Similar to scenario 1, both scenarios 2 and 3 are performed with different network topologies and variable number of WBAN nodes. The following subsections discuss in details the simulation results for the three scenarios and explore their congruency with the results obtained from the mathematical analysis.


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(a) Average traffic load per node

(b) Average number of retransmissions per packet

(c) Average number of backoff slots per node

(d) Average end-to-end delay

Fig. 17 Impact of Std-WiFi on IEEE 802.15.6 WBAN

8.1 Coexistence of IEEE 802.15.6 WBANs with std-WiFi Networks We start by analyzing the performance of IEEE 802.15.6 WBANs under the three scenarios described earlier. Figure 17 (a) shows the average traffic load in bps handled by each node within the WBAN network in the three scenarios. The WBAN operating alone has the lowest traffic load as the network is likely to have the least number of retransmissions. In this case the interference will be minimal leading to minimal collisions and signal distortion, consequently, the probability of receiving packets correctly from the first transmission attempt is high. On the other hand, the WBAN with 1APx20 WiFi network has the highest traffic load compared to interference with 3APx5 WiFi network. This is due to the condensed WiFi network in 1APx20, which increases the probability of collisions and so retransmissions. On the other hand, the 3APx5 scenario has similar performance to WBAN alone operation when the size of the latter is small, i.e. 10 and 20 nodes. However, with large number of nodes mutual interference increases which affects the WBAN performance.

Figure 17 (b) verifies the results shown in Figure 17 (a), where it shows the average number of retransmissions per packet for the WBAN nodes with the three scenarios. As illustrated, the average number of retransmission is increased as the number of WBAN nodes is increased. This is expected since the WBAN nodes are using CSMA/CA [58] in their MAC layer. Hence, with more nodes in the network it is more likely to have collisions. Again, 1APx20 scenario shows the highest number of retransmissions due to its heavy interference caused by its high packet rate operation and large number of nodes. Figure 17 (c) shows the number of backoff time slots per WBAN node. The backoff time occurs when the nodes that attempt to transmit a packet find the medium busy. The scenario with 1APx20 nodes shows the highest number of backoff time slots. With one AP and 20 nodes operating at a very high packet rate the transmission media is expected to be busy most of the time, therefore, it is likely that a node sensing the medium for transmission will find it busy and forced to backoff. Moreover, std-WiFi has high transmission power level and its transmission is heard by

28

most WBAN nodes forcing them to backoff. The large number of backoff times is expected to lead to longer delays for the successfully received data packets which prints a negative impact on the network operation. In addition, Figure 17 (c) shows that the gap between WBAN-Alone and 3APx5 scenarios in terms of number of backoff slots is reduced as the number of WBAN nodes is increased. As illustrated, with 10 nodes 3APx5 has an average number of backoff slots equals to 10 times the WBAN-Alone scenario and ended up being around the double with 50 nodes. This confirms the fact that small network sizes are less affected with the interference caused by distributed std-WiFi since the internal network interference level is low. Hence, if an interfering network architecture and distribution are pre-engineered then spatial diversity may enhance coexistence between heterogeneous networks. The average packet end-to-end delay for the successfully received data packets at the master node is shown in Figure 17 (d). As expected, the delay is increased as the number of WBAN nodes is increased with the existence of WiFi interference. Again, the scenario 1APx20 shows the highest delay, which is justified by Figures 17 (b) and 17 (c). As elaborated in the previous section, the more retransmissions and longer backoff time in a busy medium leads to higher delay especially with the lock and unlock behavior of the backoff counter imposed by IEEE 802.15.6 standard. In Figure 17 (d), both 1APx20 and 3APx5 scenarios have comparable delay values. This is because the delay is only computed for successfully received packets, i.e. packets dropped after reaching the maximum number of retransmissions are not considered. Moreover, being unable to reserve the medium implies that the queue length at the node that contains the packets awaiting transmission is getting larger. This leads to packet drop at the fully occupied queue, also those dropped packets are not counted in the delay results.


Finally, the packet success rate of the three scenarios is found in Figure 18. The WBAN network showed 100% success rate when operating alone and with 3APx5 scenarios. This means that the coexistence of 3APx5 interfering network affected the retransmission, number of backoff slots, and packet delay of the WBAN network but not the throughput. However, the success rate dropped below 20% with the existence of 1APx20 WiFi network. In this case, as explained earlier, the medium is expected to be fully occupied with packets from the interfering network. This cause the WBAN nodes to backoff and retransmit for several times until they reach the maximum limit after which the packets are dropped. In addition, the probability of having a congested queue increases causing the packets to be dropped when the queue is full. Based on the results presented in Figure 17 and Figure 18, the operation of WBAN is highly affected by WiFi interference, especially for heavy loaded WiFi causing high packet loss rate, larger retransmission probability, and higher network traffic load. All these factors are expected to contribute in decreasing the WBAN network lifetime as more power consumed to successfully deliver data packets. The impact is significant upon the existence of large WiFi network compared to WBANalone and the coexistence of moderate size WiFi networks. However, given the common use of WiFi-enabled devices and the convenience of broadband networks, the tendency of using heavy-traffic applications is higher among daily WiFi users. Hence, real-life situation tend to be closer to the second scenario (1APx20). 8.2 Performance Evaluation of LP-WiFi WBANs This section analyzes the performance of LP-WiFi WBAN with different configurations to examine its efficiency for WBANs applications. As elaborated earlier, LPWiFi WBAN settings use low transmission power with small packet sizes and low data rate. Several simulation experiments were conducted to explore the validity of this setup where they included testing the LP-WiFi WBAN network with higher data rate, higher operating power level, and larger packet sizes. It was found that larger values lead to tangible negative impact on the network operation. For short, the results of these simulations are not included in the paper. 8.2.1 Mutual Interference in LP-WiFi WBANs

Fig. 18 Packets success rate.

The usage of low-power WiFi for WBANs was suggested recently, as noted in Figure 2 a few number of research effort are found in this field. For this purpose, in this subsection we provide an extensive study of this


type of networks among other WBANs’ technologies. One of the objectives of this paper is to study the feasibility of using low-power WiFi for WBAN applications. Therefore, at first, mutual interference and scalability of LP-WiFi WBAN is studied without the existence of interference from outer networks. Figure 19 (a) shows the throughput for the LPWiFi WBAN network. The throughput presents the total data packets received successfully at the AP in Kbit/sec where the nodes are sending at a rate of 10 packets/sec to put the system under heavy load. The number of nodes is increased from 10 to 150 nodes. The figure shows that the overall throughput reached a peak at a certain load, for a network size of around 80 nodes, and saturates after that. This behavior is similar to the one shown in [29] for IEEE 802.11 networks. This is also confirmed by Figure 19 (b) which shows the total traffic sent and received by the network. Again, a saturation point is reached at around 80 nodes network size after which the network become incapable to handle extra traffic. A very important aspect that must be taken into consideration during the operation of LP-WiFi WBAN is the offset of the nodes. That is, to have a delay between the startup times of the nodes transmission. If the network operates without offset all nodes will attempt to transmit at the same time leading to a high collisions probability which is increased with the increase of the network size. This is due to the nature of WBANs applications where nodes are reporting data periodically to the master node. Therefore, a packet from each node is received every time period. Simulation experiments were conducted to clarify the importance of offset and its effect on the network performance. Figure 19 (c) shows the average number of retransmissions per packet for the LP-WiFi WBAN with and without offset. As expected, the results show that without offset the number of retransmissions is large where approximately each packet needs to be transmitted twice to be received correctly at the master node. This value is also increased as the number of nodes is increased, i.e. more mutual interference effect. On the other hand, the LP-WiFi WBAN nodes almost have no retransmissions when offset is used. This is due to the fact that offset yields time spacing while using the opportunistic contention-based access technique, i.e. CSMA/CA, at the same time. This prevents the nodes from starting their transmissions simultaneously and exploit the medium to its full capacity. Figure 19 (d) shows the average number of backoff time slots per node with and without offset. Again in this case, using offset significantly reduces the number of backoff slots which is not affected by the network size. This confirms the time

29

spacing effect which gives a fair chance for all nodes to access the medium. However, without offset the number of backoff slots is high and increases as the number of nodes in the LP-WiFi WBAN is increased. Hence, offset usage not only saves the power of WBAN nodes due to the reduced retransmission, i.e. lower load, it also reduces the packets end-to-end delay, as depicted in Figure 20, which is a every critical issue in medical applications.

Fig. 20 Packet Delay for LP-WiFi WBAN

8.2.2 Coexistence of LP-WiFi WBANs with Std-WiFi This section studies the cross interference effect between std-WiFi and LP-WiFi WBANs. Figure 21 (a) shows the average traffic load in bps handled by each node within the LP-WiFi WBAN network in the three scenarios described previously, i.e. WBAN-Alone, 1APx20, and 3APx5. As observed from the figure, the average traffic load for the LP-WiFi WBAN with the existence of the interfering std-WiFi is higher than WBAN-Alone case. Again, cross interference increases the chance of packets retransmission, which in turn increase the load imposed on the nodes. As expected, the effect is larger for the 1APx20 scenario as a result of the severe congestion it introduces on the medium which also increases as the WBAN network size increases, i.e. mutual interference is observable in congested mediums. The average number of retransmission attempts per packet sent by the LP-WiFi WBAN is shown in Figure 21 (b). The WBAN operating alone scenario has negligible number of retransmissions even with large network size. However, with the existence of std-WiFi interference, i.e. in 1APx20 or 3APx5 scenarios, the number of retransmissions increases as the network size increases. Compared to IEEE 802.15.6 WBAN with similar scenarios, i.e. Figure 17 (b), the LP-WiFi WBAN was less affected with the std-WiFi and experienced


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(a) Total network throughput

(b) Total traffic sent and received

(c) Average number of retransmissions per packet

(d) Average number of backoff slots per node

Fig. 19 LP-WiFi WBAN performance

less number of retransmissions. For example, the IEEE 802.15.6 WBAN experienced a number of retransmission in the range [2.75, 4.75] for the 1APx20 scenario, whereas the LP-WiFi WBAN range is [0.75, 2.5] for the same scenario. Figure 21 (c) shows the total number of backoff time slots per node. The existence of the std-WiFi increases the number of backoff slots for the WBAN nodes. As compared to Figure 17 (c), LP-WiFi WBAN has lower values for the backoff slots which are around 10 for the WBAN-Alone, [500, 5000] 3APx5, and [600, 5000] for 1APx20. These ranges are compared to [90, 900], [1000, 2000], and [30000, 100000] with the IEEE 802.15.6 for the three scenarios, respectively. As observed, LP-WiFi WBAN offers a great room of enhancement on both the number of retransmissions and backoff slots which is reflected on the end-to-end packets delay as shown Figure 21 (d). Moreover, in this case the existence of the std-WiFi resulted in an increase in the LP-WiFi WBAN average packets delay. The std-WiFi nodes are operating at a very high packet rate, large packet sizes, high transmission power, and high channel speed, hence, it is exacting for the LP-WiFi WBAN to compete on the

shared media with the std-WiFi. However, still the delay value in its worse case is half the delay with IEEE 802.15.6 WBANs, shown previously which makes LPWiFi more suitable for WBAN applications.

Fig. 22 Packet success rate for the LP-WiFi WBAN

Figure 22 shows the packet success rate for the three scenarios. The LP-WiFi WBAN showed 100% success rate when operating alone and with 3APx5 scenarios.


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(a) Average traffic load per node

(b) Average number of retransmissions per packet

(c) Backoff slots for the LP-WiFi WBAN

(d) Packet end-to-end delay

Fig. 21 Impact of Std-WiFi on LP-WiFi WBAN

The network in this case managed to successfully deliver all the packets. However, the success rate is slightly dropped with the coexistence of 1APx20 interfering stdWiFi. The LP-WiFi WBAN that consists of 40 nodes managed to achieve a success rate higher than 80% with the existence of std-WiFi. Compared to the IEEE 802.15.6, as elaborated in Figure 18, the WBAN success rate was below 20% for all the cases.

In summary, Figure 21 and Figure 22 showed that a std-WiFi operating at high data rate have limited negative impact on the operation of a LP-WiFi WBAN. This impact does not prevent the LP-WiFi WBAN from successfully delivering its packets. Moreover, the fact that LP-WiFi WBAN has high packet success rate with a relatively small end-to-end delay implies that it is a promising option for WBAN while coexisting with stdWiFi devices. Based on the results presented in this subsection and the previous one, low-power WiFi is a better choice compared to IEEE 802.15.6 standard for WBAN for solving coexistence issues with WiFi.

8.2.3 Interoperability of LP-WiFi WBANs and Std-WiFi Interoperability with std-WiFi is one of many advantages of using low-power WiFi for WBANs. This section studies the scenario of having LP-WiFi WBAN nodes operating with the same AP (or coordinator) of the stdWiFi network. We added 5 std-WiFi nodes associated with the same AP used by the LP-WiFi WBAN nodes. Figure 23 (a) shows the average number of retransmissions per packet for the LP-WiFi WBAN nodes. While operating alone, the LP-WiFi WBAN did not practice any retransmissions. With the 5 std-WiFi nodes joining the AP the number of retransmissions increased to reach 1.3 at its upper limit. This number is also increased with the increase in the WBAN network size due to the increased mutual interference and increased competition between nodes communicating with the same AP. Similarly, Figure 23 (b) shows the average number of backoff slots per node for the WBAN nodes. With the 5 std-WiFi nodes joining the AP the number of backoff slots per node increased from 10 to the range of [90,


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(a) Average retransmissions per packet

(b) Number of backoff slots per node

Fig. 23 Performance of LP-WiFi WBAN and Std-WiFi on one AP

900], which is also increased as the number of WBAN nodes is increased. Figure 24 shows the average end-to-end packet delay. The interference from the std-WiFi increases the delay of the LP-WiFi WBAN data packets. The packet success rate was also tested. The simulation showed that the packet success rate was 100% for both the LP-WiFi WBAN alone and with the 5 std-WiFi nodes. Hence, the cross interference affected the number of retransmissions and the backoff slots which in turn affect the packet delivery delay but not the network throughput.

Fig. 24 Average end-to-end delay

The results shown in Figure 23 and Figure 24 imply that LP-WiFi WBAN can interoperate with std-WiFi nodes using the same AP. The addition of std-WiFi did not disrupt the operation of the WBAN network. However, the effect is expected to be greater if the number of std-WiFi nodes is increased. The fact that LP-WiFi WBAN can utilize the readily available WiFi infrastructure paves the way to the integration of LP-WiFi WBAN with the cloud computing allowing them to pro-

cess and store their huge amount of collected data. Also, it can offer the backward compatibility option with the already available devices at the market.

9 Conclusions and Future Work Coexistence may be disruptive for WBANs applications causing delay or packet loss which becomes a serious issue especially in critical situations such as medical systems where reporting patient data may be life-saving. This paper provided a comprehensive study and indepth analysis of coexistence issues and mitigation solutions in WBAN technologies. Three main WBAN wireless technologies: Zigbee, IEEE 802.15.6, and Low-power WiFi were addressed. A thorough survey of state-ofthe art research in WBAN coexistence issues was conducted. The survey classified, discussed, and compared the studies according to the parameters used to analyze the coexistence problem. Solutions suggested by the studies were then classified according to the followed techniques and concomitant shortcomings were identified. Moreover, the wireless technologies coexistence in WBANs was mathematically analyzed and formulas were derived for the probability of successful channel access under the interference of neighboring networks. Extensive simulations were also conducted using OPNET to evaluate the coexistence of IEEE 802.15.6 WBAN with std-WiFi networks, and the performance of LP-WiFi WBANs. The results of the mathematical analysis and the simulation were discussed and the impact of the interfering network on the different wireless technologies was analyzed. The results showed that the interfering std-WiFi network has an impact on the performance of WBAN and may disrupt its operation. Consequently, using LP-WiFi for WBANs is a feasible and


promising option when compared to Zigbee and IEEE 802.15.6 standards. That is, LP-WiFi showed higher resistance against interference from std-WiFi. In addition, the capability of LP-WiFi to interoperate with std-WiFi paves the road to integrate WBANs with the network cloud. Consistent with the realm of this survey, few issues remain open for future research such as identifying the source of interference (e.g., coexistence versus malicious attacks such as MAC misbehaving and jamming attacks) which requires an in-depth study to analyze the interference. Finding optimal interference mitigation solutions is another open issue that calls for intensive investigation. That is, current solutions suggest standard modifications or inclusion of devices to overcome interference problems. A universal solution that is distributed, simple, light, and capable of managing the coexistence of the majority of wireless technologies is highly needed. Finally, the LP-WiFi performance where coexistence issues are of utmost criticality is to be thoroughly investigated in future research. Particularly, more empirical studies are needed to prove the efficiency of LP-WiFi for medical applications where sensors are deployed in living human tissues and special environments.

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