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Energy-efficient error coding and transmission for cognitive wireless body area network: Cognitive Wireless Body Area Network ARTICLE in INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS · MAY 2015 Impact Factor: 1.11 · DOI: 10.1002/dac.2985
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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS Int. J. Commun. Syst. (2015) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/dac.2985
Energy-efficient error coding and transmission for cognitive wireless body area network Najam ul Hasan, Waleed Ejaz, Mahin K. Atiq and Hyung Seok Kim*,† Department of Information and Communication Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, Korea
SUMMARY Wireless body area networks (WBANs) have been developed as human body monitoring systems to predict, diagnose and treat diseases. The two important concerns for WBAN are sensor lifetime and latency. Because the signal transmission in WBAN takes place in or around the human body, the fading and collision of the channel due to other existing wireless devices affect the packet error rate significantly. Hence, the lifetime and latency of the sensor are crucial. To mitigate these problems, a cognitive radio (CR) based WBAN can be a promising solution. In this paper, a cognitive forward error control mechanism for CR-based WBAN has been presented. Several issues in CR-based networks have been addressed so far to cope with energy and latency issues. However, the error control mechanism has not been investigated for CR networks. Furthermore, existing studies of the error control mechanism for wireless sensor networks cannot be applied to the CR network because of the opportunistic spectrum access mechanism (i.e. the intermittent availability of spectrum resources). The method presented in this paper adaptively selects the number of hops to the sink and adjusts the redundancy to minimize the expected latency and energy consumption. The mathematical analysis and numerical comparison show that the proposed method achieves better performance in terms of latency and energy efficiency in the multihop CR sensor network. Copyright © 2015 John Wiley & Sons, Ltd. Received 31 December 2014; Revised 6 April 2015; Accepted 06 April 2015 KEY WORDS: cognitive radio; wireless body are network; error control
1. INTRODUCTION Advances in wireless communication, wearable technology and biosensors, along with developments in embedded computing, have empowered the development of wireless body area networks (WBANs) [1]. In particular, WBAN has become increasingly popular in developing countries such as Nepal and India because WBAN-assisted healthcare is suitable for areas where the number of health specialists is relatively low [2]. A WBAN consists of various tiny sensors attached to, or implanted in or around, the patient’s body. These sensors acquire different vital parameters of the patient’s body such as temperature, pressure, oxygen level and electrocardiogram [3]. WBAN sets up a wireless network used for communication among those sensors in order to monitor vital signs of the body and transmit them to the healthcare professionals [4, 5]. The introduction of WBAN in healthcare offers a number of advantages including patient comfort by elimination of wires, improving effectiveness of caretakers by allowing universal patient monitoring, speedy recovery of patients by providing continuous health monitoring and real-time feedback to the medical personnel, improving quality of the measured data and recoding it over a longer period of time [6]. *Correspondence to: Hyung Seok Kim, Department of Information and Communication Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, Korea. † E-mail:
[email protected] Copyright © 2015 John Wiley & Sons, Ltd.
N. HASAN ET AL.
Traditionally, in WBAN, the data transfer from the patient’s body to the monitoring room is carried out through a wireless system such as ZigBee [7]. For example, in 2011, IEEE 802.15 Task Group 6 approved the first draft of standard for WBAN using ZigBee [8]. However, certain challenges arise in WBAN with such underlying wireless communication systems that are further extrapolated by hardware constraints and unique requirements of the medical data. ZigBee operates in the Industrial, Scientific and Medical band. First, several other wireless systems such as 802.11b/ g/n and Bluetooth also operate in the Industrial, Scientific and Medical band; hence, they overlap with each other [9]. This may lead to harmful interference between the existing wireless systems, which results in higher energy consumption and latency. On the other hand, lower energy consumption and latency are the fundamental requirements of the WBAN for correct, accurate and effective tele-healthcare systems. The energy harvesting in WBAN is very critical because the sensors have limited energy resources available because they have a very small size (often less than 1 cm3) [10]. Furthermore, for some sensors, especially implanted sensors, it may be inconvenient to recharge or replace their batteries [11]. For the healthcare system, latency from the underlying wireless system used for WBAN should be minimal because a higher delay in reporting a life-threatening event can lead to a fatal result [12]. Second, a crucial aspect of WBAN is related to the medical data requirement. Medical data are critical and error and delay sensitive. Therefore, reliability of transmission is desired in order to guarantee that the monitored data are correctly received by the healthcare professionals. The reliability includes correct data delivery with low latency. The patients can be dangerous when a severe event is reported incorrectly or with higher delay [13]. In this paper, we argue that cognitive radio (CR) can be a promising solution to deal with challenges associated with WBAN. Therefore, the major focus of this study is the CR-based WBAN. CR resolves these issues by introducing the concept of dynamic spectrum access (DSA). In DSA, there are two kinds of users: licensed users (also known as primary users (PU)) and unlicensed users (also known as secondary users (SU)). SUs are also known as CR users. SUs are allowed to access both the licensed and unlicensed bands in an opportunistic manner, whereas PUs can only access the licensed band [14]. The key characteristic of SU differentiating it from PU is cognitive capability. The cognitive capability means that an SU can sense the spectrum, find a vacant spectrum band and make use of these bands by smartly reconfiguring its operating parameters [15]. The main difference between traditional WBAN and CR-based WBAN is that each sensor is armed with cognitive capability. Exploiting the potential of the cognitive capability in WBAN helps sensors to sense the spectrum and select the best available channel. Each sensor can exploit the spectrum-sensing property to select the channel, keeping in view the main objectives of WBAN, that is, energy efficiency and latency, and the major factors contributing to these objectives, that is, collisions, channel fading, etc. Selecting the channel with better channel conditions and fewer collisions with the PUs, the sensor node can decrease energy consumption and latency. Furthermore, after selecting the best available channel, each sensor can exploit the reconfigurability property to adapt its operating parameters such as power, frame size and error-correcting codes according to the selected channel condition in terms of channel fading and collision to meet the reliability and energy demands of the WBAN. In this paper, a cognitive error control mechanism is proposed, which uses both channel selection and parameter adaptation (error-correcting code) for multihop CR-based WBAN keeping in view the energy consumption and latency demands of the WBAN. According to the best of the author’s knowledge, this is the first article addressing the energyefficient error control aspect of CR-based networks. Previously, a number of other issues related to energy saving in CR-based networks have been addressed: energy-efficient distributed spectrum sensing and access [16, 17], energy-efficient channel management [18], dynamic spectrum allocation [19, 20], residual energy-aware channel assignment [21], energy-efficient packet size optimization [22] and optimal power allocation mechanisms [23]. Among other open research issues considering energy consumption, devising an appropriate energy-efficient error control mechanism is one of the problems that needs to be addressed. Wireless body area network is a wireless sensor network (WSN) that interconnects tiny sensor nodes in, on or around the human body. There is a rich literature available on error control Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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mechanisms for WBAN, for example, [24], but existing work on the energy-efficient error control mechanism for WSN cannot be directly applicable to CR-based WBAN because of two main reasons: first, the intermittent availability of spectrum and, second, the multi-frequency accessibility of CR-based WBAN. The error control mechanism in general can be divided into two categories: (1) forward error correction (FEC) and (2) automatic repeat request (ARQ). In FEC, error correcting codes are employed to combat bit errors due to channel imperfections by adding redundancy to the information packets before they are transmitted. This redundancy is used to detect and correct errors at the receiver. In ARQ, only the error detection capability is provided, and no attempt to correct any packet received in error is made; instead, it is requested that the packet received in error be retransmitted. Although ARQ is simple, it may cause extra energy consumption and increase latency because of the retransmission mechanism. The FEC scheme looks better for energyconstraint and delay-sensitive CR-based WBAN than ARQ under poor channel conditions. In the FEC scheme, a certain amount of redundancy is included in the packet. The error that can be corrected depends on (1) channel conditions and (2) amount of redundancy. More redundancy in a packet means increased overhead. Therefore, the amount of redundancy should be added in accordance with selected channel conditions. Because in CR-based WBAN a sensor can select a channel out of a number of idle channels available in the licensed spectrum band, each channel may have different channel conditions and PU occupancy rates, and hence, a fixed FEC scheme may not yield optimal results in CR-based WBAN, unlike in conventional WSN. Furthermore, for a channel with good channel conditions, ARQ may be much better than FEC in terms of energy efficiency and latency performance. Hence, a cognitive error correction scheme with a combination of ARQ and FEC needs to be considered for CR-based WBAN. Therefore, in this paper, we investigate the problem of the error control mechanisms for CR-based WBAN. Its aim is to devise cognitive error control mechanisms that provide reliable communication while minimizing the energy consumption and latency in multihop CR-based WBANs. In this paper, a cognitive forward error control (CFEC) mechanism is proposed and is the combination of ARQ and BCH block code for a multihop CR-based WBAN. It has two main functionalities: (1) it selects the best available route to the sink and (2) the error correcting code depending on the expected latency and energy consumption from the source to the sink. The major contributions are summarized as follows: • A CFEC mechanism for multihop CR-based WBAN is proposed. • Expression for expected energy consumption and latency for CR-based WBAN is derived. • The proposed scheme adopts a mechanism selecting the best route to the sink and chooses suitable error correcting code to lower the expected energy consumption and latency. • To show the significance of the proposed method for medical data transmission, numerical results have been presented. The remainder of the paper is organized as follows. Section 2 presents the system model for CR-based WBAN. Section 3 presents the method of deriving the expression for expected energy consumption and latency. Section 4 describes the proposed CFEC scheme. Section 5 presents the evaluation of CFEC and ARQ schemes. Finally, Section 6 concludes the paper. 2. SYSTEM MODEL 2.1. CR-based WBAN A CR-based WBAN system may consist of wearable or implantable sensor nodes that sense the biological information from the human body and transmit to a fusion or sink device via relay sensor nodes. Once the patient information is at the sink, it can be accessed by the medical research center for analysis, as well as by the medical specialist for the medication. These sensors that gather biological information from the human body are tiny and low power and detect medical signals such as electrocardiogram, photplethysmogram, electroencephalography, pulse rate, pressure and temperature [25]. As low power consumption, tiny size and low latency are three essential Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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requirements of the body area network to determine the life time of the sensor nodes, their suitability to be worn by the patient or implanted inside the human body and effective heathcare monitoring respectively, thus, it is desirable to use a wireless platform that will provide low power consumption and transmit over longer distance with less power. Keeping in view these stringent requirements of WBAN, each sensor is armed with CR, thus termed as CR-based WBAN. A typical graphical representation of CR-based WBAN is shown in Figure 1. With the help of CR, each sensor node can sense the spectrum to find out the available/empty channels. Those channels that are unoccupied by the PUs are considered as empty. Each sensor node can select the best channel that suits its quality of service requirements in terms of bandwidth, latency and error rate. Because CR can select any channel out of the available/empty channels, it can reconfigure its operating parameters such as transmission power, error coding scheme, modulation scheme, etc. in accordance with the conditions of the selected channel. 2.2. Nomenclature Table I lists the notations used in the paper with their description. 2.3. Network model This paper considers a multihop CR-based WBAN model as shown in Figure 2 because multihop relaying in sensor networks is becoming popular due to its advantage in terms of maximizing the network lifetime with smaller number of hops and lower transmission power [26]. The network model consists of two main groups of sensors: (1) sensors collecting biological information from the human body (data sensors), and (2) sensors receiving and forwarding the information (relaying sensors). Each patient may have a different number of sensors mounted on their body. It is assumed that relaying nodes do not collect any sort of data from the environment; they just relay the information. In addition, it is also assumed that the distance between any two groups (i.e., the data sensors to the relaying sensors, the relaying sensors to the relaying sensors or the relaying sensor to the
Figure 1. CR-based WBAN. Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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Table I. CR-based WBAN. Notations
Description
Ton Toff L H R pb PERcoll PERARQ PERAB PER E[N] E[PTX] tTX E[PRX] tRX Rc Ecomm n
Time duration during which a channel is occupied by PU Time duration during a channel is empty (unoccupied by PU) Length of the packet transmitted by the SU (sensor node) Header size of the packet transmitted by SU (sensor node) Data rate of SU over a channel Probability of bit error rate Packet error rate due to collision between SU and PU Packet error rate due to Pb, when ARQ mechanism is used Packet error rate due to Pb, when Hybrid ARQ and BCH is used Packet error rate for CFEC Expected number of transmission Expected Transmission power SU transmission time Expected reception power SU reception time Coding rate for BCH codes Energy consumption for one hop transmission. Total number of sensor nodes
Figure 2. Network model for multihop CR-based WBAN.
sink) is known. This assumption is made to observe the isolated advantages of the proposed error control mechanism. Taking into account latency minimization and energy consumption in CRbased WBAN, the data sensors can reach the sink via single or multiple hops. Each sensor either data collector or relay node can access any empty channel in an opportunistic manner and can configure its operating parameters according to the selected channel. In the presented approach, CFEC, the sensors can select alternative routes to the sink, as well as change the error coding mechanism. The basic aim of the CFEC is to deliver the data to the sink with lower latency and minimum energy consumption. These goals can be achieved by reducing the number of hops to the sink and the number of retransmissions. The retransmission occurs because of either the error in the channel or the collisions among the nodes. Therefore, to reduce collision and error, besides choosing the best available channel, the sensor can adaptively choose the error correcting code and the amount of redundancy. Therefore, while selecting the route and channel code, the CFEC needs to consider two models: (1) the PU activity model and (2) the channel path loss model. 2.4. PU activity model In CR network as mentioned earlier, there are two types of users, that is, SU and PU. In CR-based WBAN, all the sensors, whether collecting biological information or relaying the information, work Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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as SU, and every radio other than these sensors will be considered as PU. SU can occupy a channel only if it is idle, that is, unoccupied by PU. If an SU communicates over a channel and a PU comes back over the channel, collision occurs between SU and PU, hence loss in the SU data packet. While selecting a channel for SU communication, we need to take the PU activity into consideration. The PU activity is modeled as an ON/OFF process. An ON state means that channel is occupied by PU, whereas an OFF state means that a channel is unused. Let Toff and Ton be the mean OFF and ON duration, respectively, then the probability of PU absence (Proff) and probability of PU presence (Pron) can be stated as follows: Proff ¼
T off T on þ T off
(1)
Pr on ¼
T on T on þ T off
(2)
If a PU appears during SU communication on a channel, this will lead to a collision between SU and PU. It is assumed that even if a collision between SU and PU occurs just for the duration of a single bit, the whole packet is considered to be corrupted. To account for the error due to collision between PU and SU, we need to compute the probability of collision occurrence, also known as packet error rate (PER) due to collision between SU and PU, denoted by PERcoll. According to [11], the PERcoll can be calculated: PERcoll ¼ 1 e
LþH RT
off
(3)
where L is size of the packet, H is the size of the packet header, R is the data rate of SU transmission and Toff is the mean time when the channel is empty.
2.5. Channel path loss model Path loss represents the gradual power reduction in the electromagnetic signal as the signal propagates through the wireless channel. In order to measure the signal to noise ratio (SNR) between the transmitter and receiver, we need to estimate the path loss. Several path loss models have been previously established for approximating signal attenuation; in this study, the standard model used in [27] is chosen. According to [27], the relationship between transmitted power (Pt) and received power (Pr) can be expressed as follows: Pr ¼ Pt K
γ d0 d
(4)
where γ is the path loss exponent, K is the constant and d0 is the reference distance. In [28], the assumption is that K, d0 and γ are the same for all hops. After calculating the SNR for the channel between data sensors and the first level of relaying sensors, the SNR of channel between two nodes that are j hops away from each other is γ SNRj ¼ SNR δj
(5)
where j = 1, 2… at node nj and δj ¼ 1j . Next, PER is derived with the channel path loss model. The modulation scheme for this study is BPSK. The probability of bit error rate (pb) for BPSK for a given SNR can be represented as Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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pffiffiffiffiffiffiffiffiffiffiffiffi pBPSK ¼ Q 2SNR : b
(6)
For ARQ, the cyclic redundancy check is usually used. Assuming the detection of all packet errors, the PER of a single transmission for a packet of length L bits can be computed as PERARQ ¼ 1 ð1 pb ÞLþH
(7)
CFEC is a combination of ARQ and FEC. Hence, BCH codes are chosen along with ARQ. In [13], the author mentioned that for both moderate and high SNR it outperforms other block codes. BCH belongs to a category of block codes. BCH block codes are represented by (n, k, t), where n is the block length, k is the payload length and t is the error-correcting capability in bits. For a particular combined ARQ and BCH code with (n, k, t) for a given value of pb, the PERAB (the subscript AB refers to combined ARQ and BCH) can be computed as PERAB
w n ε n nε ¼ 1 1 ∑ε¼tþ1 p b ð1 p b Þ ε
where w is the number of code words in the packet, that is, w ¼
LþH
k
(8)
.
3. LATENCY AND ENERGY CONSUMPTION ANALYSIS Unlike WBAN, in CR-based WBAN, while in transmission, the packet may be corrupted not only due to the path loss but also due to collision with PU. The method to estimate the PER due to both the path loss model and PU activity is given in the previous section. In this section, we describe how to compute the expected latency and energy consumption. Both the latency and energy consumption depend on PER. 3.1. Latency analysis In general, latency depends on a number of parameters, such as nodes processing capability, communication protocol, number of hops and number of retransmissions. The latency depends greatly on how the retransmissions are handled in a network. The main focus of this study is to show the performance of the error control mechanism for CR-based WBAN; hence, for simplicity, we consider the latency model in terms of the number of retransmissions. Because CFEC is a combination of the ARQ and FEC scheme, a retransmission occurs whenever a packet is unrecoverable by the FEC. The transmission count can be computed from the PER. In CR-based WBAN, PER depends on PERcoll and PERAB. Both PERcoll and PERAB are i.i.d; hence, the overall PER can be written as PER ¼ 1 ð1 PERcoll Þð1 PERAB Þ
(9)
In [14], the author stated that based on PER, the number of expected transmissions denoted by E[N] can be computed as E ½N ¼
1 1 PER
(10)
To calculate the expected total number of transmissions from the source to the sink, say E[Ntot], the expected values of transmission E[N] for each hop along the path from source to sink are summed up. Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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3.2. Energy consumption analysis When a sender sends data to a receiver, the energy consumed for communication is Ecomm ¼ ETX þ E RX
(11)
where ETX is the energy consumed by the transmitter and ERX is the energy consumed by the receiver. Ignoring energy consumption for encoding and decoding, in CFEC the energy consumed over one hop can be written as E comm ¼ E ½N ðE ½PTX t TX þ E ½PRX t RX Þ
(12)
where E[N] is the expected number of transmissions, E[PTX] is the expected transmission power, tTX is the transmission time, E[PRX] is the expected reception power and tRX is the reception time. For simplicity, it is assumed that both tTX and tRX are t. Furthermore, t can be expressed in terms of the number of bits transmitted in a packet multiplied by the time needed to transmit a single bit (tb). For a packet of length L, header H and a given BCH code, t can be written as
t TX ¼ t RX ¼ t ¼ ðRc L þ H Þt b
(13)
where Rc is the coding rate for a particular BCH code with (n, k, t) and can be expressed as Rc ¼ k = n
(14)
Ecomm ¼ E½N ðE½PTX þ PRX ðRc L þ H Þt b
(15)
By using (13), (12) can be written as
In this study, to compare between ARQ and CFEC, Ecomm of (15) is normalized with the energy consumption of a single-hop transmission of an uncoded packet E[PTX + PRX](L + H), and (15) is rewritten as Ecomm ¼ E ½N
L=Rc þ H LþH
(16)
4. COGNITIVE FORWARD ERROR CONTROL MECHANISM Generally, current state-of-the-art error control techniques can be classified into two categories: ARQ and FEC. In ARQ, a packet is retransmitted if it is found to have an error. Such packets are retransmitted until they are received error-free. The additional cost in terms of energy and latency in ARQ is due to the number of retransmissions, which is the major drawback of the method. In FEC, a certain amount of redundancy is added to the packet and allows for a certain number of corrupted bits to be corrected at the receiver side. Its major drawback is the cost of redundancy, which increases packet size and also introduces encoding and decoding costs. In CR network, because nodes have to frequently switch among different channels, adaptive network operation is necessary for coping with channels that have varying conditions in terms of PU occupancy and path loss. Hence, in this study, the aim is to devise an adaptive error-coding mechanism for CR-based WBAN. Adaptive error-coding allows the code strength and complexity to be varied according to the channel condition; hence, CFEC is an error-coding scheme that is a hybrid of ARQ and Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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BCH. Figure 3 shows the flow chart for the proposed CFEC mechanism. The steps for selecting the optimal number of hops and error-coding code for CFEC are as follows: (1) (2) (3) (4) (5) (6) (7) (8)
A source node sends a route request message. Destination sends a route reply message. Multiple copies of the route reply message are received from different possible routes. Repeat steps 5 to 9 for each possible route. Using the path loss model, estimate the SNR of each available channel. Using the PU activity model, estimate the mean Toff time for each available channel. Compute PER for both ARQ and various BCH codes. Compute latency by using PER as computed in step 3 across different available routes as well as codes. (9) Determine expected energy consumption by the computed latency model developed in step 4. (10) Based on the computed energy for various routes and coding options, select the combination of route and code with the minimum expected energy consumption.
5. NUMERICAL RESULTS The purpose of this study is to research the performance of ARQ and CFEC, with respect to energy efficiency and latency in CR-based WBAN via numerical evaluations. The effect of varying SNR and PU activity on their performance is shown.
Figure 3. Flow diagram for cognitive forward error control (CFEC). Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
N. HASAN ET AL.
5.1. Evaluation setup and parameters setting For the purpose of evaluation, we have used a simplified network model shown in Figure 4. There are a total of n sensor nodes in the CR-based WBAN including one data collector sensor (denoted by S), n 2 relay nodes (denoted by R) and one sink node (denoted by D). It is assumed that the data are only generated by the data collector node, whereas relay nodes can only relay the information to the sink. Each sensor node can use either an ARQ or an FEC mechanism through a set of 70 BCH codes presented by [29]. Using the CFEC scheme, each sensor can select the optimal route and the most appropriate error correcting code in order to minimize the expected latency and energy consumption. The values chosen for the different parameters in the majority of the evaluations are presented in Table II, unless otherwise specified. It is assumed that the distance d between any two sensor nodes is known and SNR (denoted by y) is only dependent on the distance between those sensor nodes. Figure 5 shows the effect of varying y on the expected energy expenditure for ARQ and CFEC. We considered that at maximum, the sink node D is four hops away from the source node S. To show the impact of SNR, y is varied, which denotes the SNR between two nodes with minimum distance. The PU activity is assumed to be symmetric across each link, and Toff is set to 0.8. It can be noted that the normalized expected total energy consumption of CFEC is never greater than that of ARQ. Moreover, it can also be observed that for bad channels with y < 7 dB, CFEC outperforms ARQ and for y < 5 dB, the expected energy consumption of ARQ, reaches extreme values because of the great number of retransmissions. Figure 6 shows the effect of expected energy consumption by varying the PU activity. The Toff time is varied from 0 to 1. Multiple curves have been drawn to show the impact of variation in y as well. It is shown that with increasing Toff time, the expected energy consumption decreases because SU has more opportunities to transmit and less collision between SU and PU occurs, thereby resulting in fewer retransmissions. Furthermore, it is also shown that very low Toff time results in so ds,13 ds,12
ds,11
S
d11,13
R11
R12
R13
R21
R22
R23
R31
R32
R33
D
Figure 4. Network scenario used for evaluation.
Table II. Parameter setting. Parameters
Values
L N H Toff R Γ FEC Modulation Toffi, i = 1, 2, 3…
512 bits 11 30 bits 0.8 8 kbps 2 BCH(n, k, t) BPSK Mean idle time for links in ith route
Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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Figure 5. Normalized expected energy expenditure vs. SNR.
Figure 6. Normalized expected energy expenditure vs. Toff time for different values of SNR.
many collisions that the expected energy expenditure becomes extremely high, because of a huge number of collisions between SU and PU. It is also shown that channels with good SNR lead to less expected energy consumption. Hence, channel selection also plays an important role in energy conservation in CR, which is beyond the scope of this study. CFEC outperforms ARQ for varying Toff time quite convincingly. Figure 7 shows the effect of the PU activity on expected energy consumption. Multiple curves show the impact of the bandwidth of channel. The result shows that the decreased bandwidth of the channel leads SU to take more time to transmit information and hence also causes more collision, resulting in more energy consumption. Table III shows the number of hops required in the route selected from node A to D under varying SNR for a number of scenarios with different PU activity across each link in each route. The number of hops is related to the latency. If a node reaches the destination with fewer hops, less delay will be incurred. As mentioned earlier, there are three different routes in terms of the number of hops: a route with one, two and three hops from source node A to destination node D, respectively. Four different scenarios, based on PU activity, are shown in Table II. Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
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Figure 7. Normalized expected energy expenditure vs. Toff time for different values of data rate.
Table III. Number of hops with varying SNR (y) and PU activity Toff time. 3-hop route PU activity Toff1 = 1, Toff2 = 1, Toff3 = 1 Toff1 = 0.1, Toff2 = 1, Toff3 = 1 Toff1 = 1, Toff2 = 0.1, Toff3 = 1 Toff1 = 1, Toff2 = 1, Toff3 = 0.1
2-hop route
1-hop route
ARQ
CFEC
ARQ
CFEC
y < 19 dB y < 19 dB ∀y y < 19 dB
y = 1 dB y = 1 dB y = 1 dB y = 1 dB
y > 19 dB y > 14
1 dB < y < 4 dB 1 dB < y < 4 y = 3 dB 1 dB < y < 5 dB
y > 19
ARQ
CFEC
y ≥ 4 dB y ≥ 4 dB y ≥ 4 dB y ≥ 5 dB
Scenario 1 (Toff1 = 1, Toff2 = 1, Toff3 = 1): This implies that all channels are free from PU activ ity; hence, the PER, due to the collision with SU and PU, is zero. Scenario 2 (Toff1 = 0.1, Toff2 = 1, Toff3 = 1): This indicates that the link along the route with three hops has PU. The links along the other routes are free from PU and SU collisions. Scenario 3 (Toff1 = 1, Toff2 = 0.1, Toff3 = 1): This means the route with two hops has only PER, due to the collision between SU and PU, as the time duration available for SU is just 0.1, whereas other routes are free from PU activity. Scenario 4 (Toff1 = 1, Toff2 = 1, Toff3 = 0.1): This implies that the route with one hop has only PU and the rest of the routes are free from PU and SU collisions. For each scenario, CFEC outperforms ARQ. For all scenarios where a one-hop route is free from PU activity, CFEC adopts this route for y ≥ 4 dB, whilst in the fourth scenario CFEC adopts this route at y ≥ 8 dB. For all considered scenarios, ARQ adopts either the routes with three or two hops and conversely never adopts a route with one hop; therefore, the results illustrate the supremacy of CFEC, in terms of latency over ARQ. Figure 8 illustrates the impact of both PU activity and channel condition on the expected energy consumption. The value of y is varied from 1 to 25 dB. The PU activity represented by Toff time is assumed to be the same over each available channel and is varied from 0 to 1. The result indicates that the lower the y is, the higher the value of PER, which can be attributed to the corruption in the packet during propagation. As channel condition improves, low PER, due to fewer corrupted packets, leads to less expected energy consumption, because of fewer retransmissions. Furthermore, the result shows that with increasing Toff time, PER, due to collision between SU and PU, decreases, which results in less energy consumption. Finally, the result shows that CFEC outperforms ARQ for a given Toff time and y across multiple Copyright © 2015 John Wiley & Sons, Ltd.
Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
COGNITIVE WIRELESS BODY AREA NETWORK
Figure 8. Expected energy consumption versus Toff and SNR.
available routes and is much more necessary to select the suitable route and error-correcting code in an adaptive manner. 6. CONCLUSION This study shows the significant advantages of CFEC in terms of packet loss, latency and expected energy consumption in multihop CR-based WBANs. CFEC has less energy consumption as compared with ARQ. Furthermore, with equal constraint on energy consumption, CFEC is able to use fewer hops, which indicates the significance of CFEC in reducing the latency as compared with ARQ. This suggests that if the channel condition is known in terms of SNR and PU activity, CFEC is the most feasible choice. A possible way to benefit from CFEC is to estimate the SNR and maintain the history of usage for each channel in order to measure the mean idle time. ACKNOWLEDGEMENTS
This research was supported by the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center) support program (NIPA-2013-H0401-13-1003) supervised by the NIPA (National IT Industry Promotion Agency). It was partially supported by Seoul R&BD Program (SS110012C0214831). REFERENCES 1. Movassaghi S, Abolhasan M, Lipman J, Smith D, Jamalipur A. Wireless Body area networks: a survey. IEEE Communication Surveys & Tutorials 2011; 16(3): 1658–1686. 2. Mishra SK, Kapoor L, Singh IP. Telemedicine in India: current scenario and the future. Telemedicine and e-Health 2009; 15(6): 568–575. 3. Latré B, Braem B, Moerman I, Blondia C, Demeester P. A survey on wireless body area networks. Wireless Networks 2011; 17(1): 1–18. 4. Cao H, Leung V, Chow C, Chan H. Enabling technologies for wireless body area networks: a survey and outlook. IEEE Communications Magazine 2009; 47(12): 84–93. 5. Zhen B, Li HB, Kohno R. Networking issues in medical implant communications. International Journal of Multimedia and Ubiquitous Engineering 2009; 4(1): 23–38. 6. Park S, Jayaraman S. Enhancing the quality of life through wearable technology. IEEE Engineering in Medicine and Biology Magazine 2003; 22(3): 41–48. 7. Rehmani MH, Lohier S, Rachedi A. In Channel bonding in cognitive radio wireless sensor networks. IEEE International Conference on Selected Topics Mobile and Wireless Networking (iCOST), Avignon, 2012; 72–76. 8. Timmons NF, Scanlon WG. In Analysis of the performance of IEEE 802.15. 4 for medical sensor body area networking. IEEE Conference on Sensor and ad hoc communications and networks (SECON), Santa Clara, California, USA, 2004; 16–24. Copyright © 2015 John Wiley & Sons, Ltd.
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Int. J. Commun. Syst. (2015) DOI: 10.1002/dac
