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Improving the Reliability of IEEE 802.11s Based Wireless Mesh Networks for Smart Grid Systems Jaebeom Kim, Dabin Kim, Keun-Woo Lim, Young-Bae Ko, and Sang-Youm Lee Abstract: A challenge faced by smart grid systems is providing highly reliable transmissions to better serve different types of electrical applications and improve the energy efficiency of the system. Although wireless networking technologies can provide highspeed and cost-effective solutions, their performance may be impaired by various factors that affect the reliability of smart grid networks. Here, we first suggest the use of IEEE 802.11s-based wireless LAN mesh networks as high-speed wireless backbone networks for smart grid infrastructure to provide high scalability and flexibility while ensuring low installation and management costs. Thereafter, we analyze some vital problems of the IEEE 802.11s default routing protocol (named hybrid wireless mesh protocol; HWMP) from the perspective of transfer reliability, and propose appropriate solutions with a new routing method called HWMP-reliability enhancement to improve the routing reliability of 802.11s-based smart grid mesh networking. A simulation study using ns-3 was conducted to demonstrate the superiority of the proposed schemes. Index Terms: IEEE 802.11s, reliability, smart grid, wireless mesh networks (WMNs).
I. INTRODUCTION A smart grid is conceptualized as an electrical grid infrastructure integrated with IT communication systems to provide intelligent management of various power-related applications and services. In the near future, such smart grids are bound to connect vast areas of existing electrical systems, providing electrical services in a real-time manner and highly efficient resource management at the same time. As part of nation-wide plans for green IT projects, many countries have already made significant progress in establishing developmental smart grid structures and are on their way to realizing green homes and industries via the successful implementation of these smart grid systems [1], [2]. Following these global efforts, research in various areas of smart grid technologies, especially in the area of networking and communications, has also intensified. To better understand smart grid systems, the U.S. National Institute of Standards and Testing (NIST) defined a hierarchical information network structure for such systems [3]. A smart grid can consist of different types of networks, which can be broadly classified into home area networks (HANs), neighbor area netManuscript received May 15, 2012. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2012-003573). J. Kim, D. Kim, K.-W. Lim, and Y.-B. Ko are with the Graduate School of Information and Communication, Ajou University, Suwon, South Korea, email: {Jaebeom, dabin912, kwlim27}@uns.ajou.ac.kr,
[email protected]. S.-Y. Lee is with the Smart Energy Laboratory, Korea Electric Power Corporation Research Institute, Daejeon, South Korea, email:
[email protected]. Digital Object Identifier 10.1109/JCN.2012.00029
works (NANs), and wide area networks (WANs). A HAN focuses on small-scale data communication between devices inside typical households; a NAN is defined to provide a backbone for data that are transmitted from multiple HANs while also providing various services of its own. A WAN becomes the backbone system connecting HANs and NANs, providing highend transmission capabilities through different vast areas of the smart grid network. As various areas of a network are defined and classified according to their properties and requirements, different types of networking and communications systems are being recommended in each area as suitable means of communication. Means and protocols of communication through wired cables were prematurely considered as suitable candidates for realizing communication in HANs, NANs, and WANs. This is because smart grid systems must deal with various real-time electrical services and applications that may be directly relevant to financial and industrial activities, and wired line communications standards such as fiber and power line communication (PLC) were considered suitable for providing the high-capacity and high-reliability transmission standards that were required by these services. However, these wired systems have limitations in scalability and resiliency; the cost of installing lines and the complexity of management in harsh, extreme area as downtown and mountainous region made them undesirable in various deployment scenarios. Instead, wireless standards such as IEEE 802.11 and IEEE 802.15.4g smart utility network (SUN) [4] are now considered key communication technologies of smart grid systems. Although not as reliable as their wired counterparts, these wireless technologies can provide a high-speed and easyto-deploy backbone, increasing scalability, and cost reduction of overall smart grid systems. As a popular standardization effort, IEEE 802.15.4g has made outstanding progress in having its functions used in HANs, and plans to extend its capabilities to the area of NAN have been announced. IEEE 802.15.4g introduces several core functions, such as multi-physical (PHY) management and co-existence schemes, to provide the reliability needed in smart grid systems. Although these functions are necessary properties for the effective management of NANs, the standard also poses various problems. The most critical of them is that 802.15.4g is capable of only providing a maximum data rate of 1 Mb/s within the one hop range [4]. Furthermore, although IEEE 802.15.4g maintains co-existence measures, networks based on this standard may still suffer from heavy interference when deployed with more powerful networking systems such as IEEE 802.11. In contrast to NANs, which have outdoor deployment properties where the network may support a number of different applications and services in a multi-hop environment, 802.15.4g may only provide enough functionality in the area of an HAN. Instead, technologies such as wireless mesh networks
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(WMNs) based on WLAN technologies can be considered as candidates to provide a high-speed and easy-to-deploy wireless backbone for NANs [5]. WMNs can provide reliable wireless transmission methods in various types of applications, such as Internet service, disaster relief, military surveillance, and reconnaissance. In addition, WMNs can provide efficient backbone infrastructures for various green computing solutions such as smart grids. To support reliable and high-speed transmission for smart grid networks, IEEE 802.11s [6] in particular is a potential candidate for efficiently configuring mesh networks in NANs. Some unique functions of 802.11s are expected to support the various requirements of NANs in a smart grid. Firstly, IEEE 802.11s uses the enhanced distributed channel access (EDCA) scheme, originally defined in the IEEE 802.11 [7] standard to differentiate data traffic by priority and provide quality of service (QoS) for some time-critical data, which are typical requirements in a NAN. Also, the hybrid transmission policy and airtime cost metric in its default routing protocol, hybrid wireless mesh protocol (HWMP) are considered to be suited to static mesh networks, which is also a characteristic of NAN. Lastly, the root mesh station-to-mesh station (root mesh STA-to-mesh STA) association of the mesh network in 802.11s provides an ideal topology for NANs. For example, periodic upstream data generated by mesh STAs (NAN nodes) can be constantly transmitted through the root mesh STA and to the server that is wired to the gateway as a root mesh STA. However, even a 802.11s mesh may pose various problems if implemented in smart grid systems without any modifications. Such problems, including those that already exist in the 802.11s mesh (e.g., route instability and inefficient route recovery) as well as new problems that may occur in the process of integrating 802.11s into the NAN (e.g., inaccurate link cost metric calculation, path error packet loss, and mishandling of latencytolerant smart grid data), must be resolved to guarantee reliability in smart grid networks. The main objective of this paper is to effectively increase the reliability of the smart grid system by resolving these existing (relating to the HWMP) and newly discovered problems. To achieve this, here we propose a reliability enhancement architecture for the existing HWMP routing protocol in the 802.11s mesh network, named HWMP-reliability enhancement (HWMP-RE), which is suitable for smart grid networks. To account for the problems, we modify the calculation of airtime link cost to stabilize route creation, and differentiate various applications in the NAN. The HWMP-RE also modifies the self-healing mechanism to reduce overhead and provides delay-tolerant functions in the network. We evaluate the performance of the HWMP-RE by using the ns-3 simulator, to demonstrate that they are beneficial in enhancing the reliability and feasibility of smart grid systems. II. BACKGROUND A. A Brief Overview of IEEE 802.11s Standards IEEE 802.11s—an 802.11 amendment for WLAN mesh networking—defines the architecture and protocols that are suitable for wireless multi-hop mesh networking environments [6]. The topology of 802.11s is built upon a root mesh STA that provides wireless connections to mesh STAs. Mesh STAs act as ter-
minals to client devices and provide various services via multihop transmission together with root mesh STAs and mesh STAs. Multi-hop routes are created using the HWMP, which is the default path selection protocol for 802.11s. The HWMP combines two types of routing modes: The on-demand mode and the proactive tree-building mode. The proactive tree-building mode is designed to work with the proactive path request (PREQ) or root announcement (RANN) mechanism intimated by root mesh STA. The proactive PREQ mechanism creates paths from the mesh STAs to the root, using only group-addressed communication. The RANN mechanism creates paths between the root mesh STA and each mesh STA, by using acknowledged communications. In the proactive tree-building mode of IEEE 802.11s, the root mesh STA floods the network with the proactive PREQ or RANN [6]. These messages are received, relayed and forwarded by all the sub nodes of the mesh network. Upon reception, each mesh STA calculates the airtime cost metric shown below: 1 Bt (1) Ca = O + r 1 − ef where O is the channel access overhead; Bt , the transmission frame size; r, the data rate; and ef , the error rate. Although the channel access overhead, transmission frame size, and data rate may vary depending on the type of PHY model used, the error rate can be calculated in various ways, and is only recommended in the standard. In general, airtime cost, as the name implies, represents the latency and error rate of a specific multihop path during the wireless transmission of data frames through the route. This information is calculated at each STA and accumulated inside the received RANN message. Each node will use the cumulative airtime cost information in the RANN message, as well as its own airtime cost calculation, to select the most efficient multi-hop route to the root mesh STA. Through periodic RANN transmissions, all nodes regularly update the best single multi-hop route to the root mesh STA. If, owing to transmission failure, the path to the root mesh STA is considered obsolete, a new path is discovered before the RANN period by using an on-demand path discovery algorithm identical to the traditional ad hoc on-demand distance vector (AODV) [8]. When a link is considered broken, the nodes that used the broken link are notified by using a path error message (PERR). Upon realizing that their multi-hop route to the mesh root STA is broken, mesh STAs initiate a reactive path discovery process. This process generates PREQ messages, i.e., broadcast messages transmitted from the source node to create new multihop routes to the destination. During this process, the airtime cost metric is also used to calculate the best route among the multiple paths that are available. Fig. 1 summarizes the architecture of the IEEE 802.11s HWMP routing protocol. As stated above [6], 802.11s defines not only traditional media access control (MAC) functions but also a multi-hop routing in the MAC layer by using cross-layer architecture. Data that arrive from the application layer or physical layer are stored at the HWMP queue. If the HWMP queue is not empty, the HWMP checks the destination address of the data frame and searches the next hop address in its own routing table for its transmission. The routing table is periodically updated by a proactive route-building mechanism. When a mesh STA
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receives a data packet from another mesh STA or application layer, it checks the destination address in the MAC frame. In the case of forwarding a packet, the mesh STA tries to find the next hop address in the routing table. If it does, the mesh STA transmits the frame by using a medium access mechanism, such as distributed coordination function (DCF) or EDCA. Otherwise, the mesh STA drops the frame and transmits control messages such as PERR and on-demand PREQ to rebuild the route. B. Related Approaches Thus far, several approaches have been proposed for using mesh networks in smart grid systems. Various manufacturing companies, such as Tropos Inc. [9] and Trilliant Inc. [10], have presented their own proprietary solutions for using wireless mesh networks in NANs and attempted to provide better management of energy resources. Although these propositions may appear unique and innovative, they do not actually present any design for a precise mesh technology architecture. Therefore, our work is distinct from these approaches in that it uses the IEEE 802.11s standard to efficiently structure the networking backbone of the smart grid. The routing scheme proposed in [11] focuses on mesh-based routing methods for large-scale advanced metering infrastructure (AMI) data transfer, with the aim of maximizing routing reliability and minimizing the overhead of control messages that may degrade network performance. Although mesh routing methods are proposed here, most of the work focuses on reliable routing for small-size data packets, considering AMI data transfer. In NANs, which require relatively high-speed transmission technologies with high standards of QoS support, the routing methods proposed in [11] may be unable to optimize its performance. Approaches such as [12]
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focus on using IEEE 802.11s-based wireless mesh networking and improving its reliability for smart grid networks. Therefore, these approaches are intimately related to those we have taken, and focus on using multiple gateways to increase the reliability of the network. When a link is broken and a PERR message is transmitted to the source node, this node attempts to transmit its data towards another gateway to increase its transmission success ratio. Although our work also focuses on this link-breakage problem, we consider a method that can also be used in single and multiple gateway scenarios with less overhead than these related approaches. Several approaches focus on increasing the reliability of routing in traditional wireless networks. The authors in [13] propose multi-path routing methods by using data redundancy; packets are redundantly transmitted to multiple routes to improve the reliability of data transmission while multiple multi-hop routes are selected based on an interference-aware metric. Also, the authors in [13] analyze the effect of packet retransmission on increasing the reliability of wireless networks. Although these approaches focus on increasing the reliability of mesh networks, whether these claims are also valid in smart grid environments remains to be verified. To provide better transmission reliability for various services and applications in smart grid systems, we build on the concept of delay-tolerant networking (DTN) [14]–[16]. DTN methods are based on store-and-forward message exchanging to maintain data, even when the routes to the destination are temporarily broken. We attempt to use this mechanism to enhance the reliability for specific applications, and show that using this technology is highly effective in smart grid environments. III. PROBLEM STATEMENT Although IEEE 802.11s is a potential candidate for smart grid mesh networks, it has some problems that may cause negative effects in the system. The problems can be classified roughly into two types: Existing 802.11s problems that may worsen, and new problems that may emerge during the process of integrating 802.11s into the NAN. We define and analyze these problems before presenting our tailor-made algorithms designed to solve them directly. A. Potential Problems of HWMP Itself First, the traditional HWMP suffers from the problem of route instability [17], [18]. In a previous study [19], we pointed out that this problem causes a severe reduction of transmission reliability in a smart grid system. The problem occurs when the routing path of a node constantly changes during data transmission, even when the current route can be used without any severe problems. This phenomenon occurs mainly because of the proactive tree-building scheme of the HWMP. When a periodic RANN message is transmitted by the root mesh STA, each mesh STA calculates the multi-hop link cost towards the root mesh STA and selects the route with the lowest cost. However, the transmission of data and occasional transmission failure may degrade the link cost of the currently used path, while other routes that were not selected do not degrade because the frames required to calculate the link cost are not transmitted. Consequently, in the next interval for determining the routing path, the
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link costs of the other routes tend to be better than the link cost of the primary route when in fact they are not. Therefore, in the next periodic RANN process, another route is selected instead as the next primary route. This leads to more problems because the ongoing transmission of data immediately degrades the newly selected primary route. Owing to this inefficient route selection process, the primary path fluctuates every time a RANN process begins. Another problem is inefficient route recovery. In the current HWMP, when a path becomes broken, a PERR message is generated, and all nodes in the network are flooded with the message. In this process, a large number of frames may be dropped. The reason for this is that when a route is considered broken at an intermediate node, the node declares that it cannot transmit the data in its queue and drops them all at the routing module. This method could be effective for various multimedia streaming applications, where data for which the transmission deadline has passed can be dropped to save bandwidth for other streaming data. However, when considering the smart grid environment, this may be very inefficient because reliability is the foremost issue to consider. Therefore, to satisfy the reliability requirements of smart grid applications, the existing HWMP route recovery process must be properly adjusted to the smart grid network. B. Possible Problems of HWMP with Smart Grid When the original 802.11s technology is integrated into the smart grid network, several new problems can be anticipated. The first is the oversimplified calculation of airtime cost, which cannot consider the many properties and requirements of various smart grid applications. However, airtime cost calculation has a highly significant role in providing reliable transmission because the accuracy of the airtime cost routing metric helps mitigate the route instability problem and affects appropriate route decisions to mesh STAs for each smart grid application. In the IEEE 802.11s standard, the solution of how to calculate the error rate of a specific route is not specified. Therefore, to achieve a reliable network, a more sophisticated link quality estimation method that considers various data characteristics as well as time-variant link quality is required. The second problem is the mishandling of latency-tolerant smart grid data. Owing to the different requirements and properties for each smart grid application, there is a need to differentiate them in the networking layer. However, packets are dropped in the network layer regardless of their importance, simply because the route to the destination from the intermediate node is broken. Therefore, measures are needed to mitigate this problem as much as possible to guarantee reliable data transmission in the smart grid network. For example, general AMI data and periodical power quality measuring data have the characteristics of being time tolerant as long as they are successfully delivered. Therefore, if these data can be differentiated in the network and maintained even in the case of PERRs, they can be transmitted successfully to the destination only at the cost of higher but tolerable latencies. IV. RELIABILITY IMPROVEMENT IN IEEE 802.11s BASED SMART GRID MESH NETWORKS On the basis of two major approaches, the proposed HWMPRE architecture attempts to improve the reliability of 802.11s
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WLAN mesh networks. The aim of the first approach is to improve route stability, and that of the second is to mitigate the loss of important data packets. By achieving these objectives, various smart grid applications can be successfully transmitted in 802.11s. For the first approach, we propose a new method of calculating the airtime cost metric for the HWMP of a 802.11s standard, and a route instability prevention scheme to improve the reliability of route selection. For the second approach, we provide a reserved route use algorithm to prevent packet loss during link breakage, and design a latency-tolerant traffic management scheme to differentiate smart grid data efficiently. These approaches are implemented and installed in each STA as enhancement modules for the HWMP (Fig. 2). Mainly, we designed three new modules: the stable route selection, alternative route use, and latency-tolerant traffic management modules. By using these modules, the HWMP-RE guarantees higher transmission reliability of smart grid applications and services than the original HWMP. These modules are also compatible with the original HWMP and can be used together in a single network. A. Module for Stable Route Selection A.1 The Modified Airtime Cost Metric The majority of the data in a NAN are considered to be upstream, as in AMI, power quality monitoring, and substation surveillance. This means that the data are sensed and generated by grid nodes, and transmitted to a central server for further processing. Therefore, these properties must also be considered in calculating the airtime cost of the HWMP. To reflect this upstream property in the calculation, the stable route selection module uses the MAC retransmission count of each frame to calculate the failure rate of the network. This parameter accounts for all types of transmission failures, such as collisions and poor channel conditions, which cause an MAC retransmission [20]. In the 802.11 standard [7], a node attempts to transmit
KIM et al.: IMPROVING THE RELIABILITY OF IEEE 802.11S BASED WIRELESS MESH...
its data frame until it receives an ACK frame from the next-hop node or until the retry limit is reached. Therefore, the number of attempted retransmissions to deliver a data frame can be used to calculate the failure rate of the current network as follows:
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where Bi is the size of the data frame i in bytes and Bmax is the biggest size of the data frame in the network, which we configure as 1500 bytes-the typical maximum transfer unit (MTU) size in the Internet. By using (3), we can consider the penalty of each data frame depending on its size. For example, frames with a size of 1500 bytes receive the minimum penalty value of 0.5, whereas smaller frames receive a penalty close to 1 (i.e., the maximum penalty). The total value of the error rate, which is always between 0 and 1, can be inserted for the error rate parameter in (1) to calculate the ultimate airtime cost. This approach, compared to those such as [20] for typical mesh networks, can be considered more beneficial in a smart grid environment.
HWMP, so that multiple route information in the current RANN interval, as well as information from the previous RANN interval, is stored by each node. To better understand route fluctuation prevention in the module, we define a set of RANN messages of identical sequence number traversing from the root mesh STA to all mesh STAs, as one RANN round. To provide enhanced route stability, the module receives information from the route management module of the HWMP and selects the primary route to the root mesh STA. Each mesh STA maintains and updates the airtime costs of all the RANN messages it receives in two consecutive RANN rounds: the previous round, and the current round. This is because a stable route among many candidates is selected based on the historical information of every link. Fig. 3 shows the processes for stable route selection. First, each STA calculates the link costs for all received RANN messages, and selects one path to be used for packet transmission in the current RANN round as well as multiple reserved routes having lost the competition to the primary route owing to their less efficient link cost. In the next RANN round, the link costs of the primary and reserve routes are stored as historical data, and new link costs are computed for new RANN messages. In the original HWMP, only current link costs are used to select the primary route, which results in route fluctuations. On the other hand, the HWMP-RE acquires current and historical link costs, and uses both to select the new primary route. The primary route is continuously maintained when it has a lower airtime cost than that of reserve routes. However, if the link cost of a reserve route is lower than the cost of the primary route, then the airtime costs of the previous RANN round are checked with the historical data. The link cost variation between the previous and current RANN rounds of the primary route is compared with that of the reserve route. If the variation of the primary route is higher than that of a reserve route with a better link cost, then the reserve route becomes the new primary route. However, if the link cost variation of the primary route is less than that of the reserve route, the primary route is maintained despite the good link cost of the reserve route. This is because we assume that the primary route does not fluctuate much compared with the other reserve routes and can thus be used more reliably. In addition, if the reserved route is considered far superior to the primary route, then the reserved route can be selected regardless of the variance. By using this method, we reduce the frequency of route fluctuations because the reserve routes are less likely to be selected as the new primary route. Furthermore, the conditions allow the route to be switched only if the link cost of the primary route is degraded excessively compared with the reserved routes.
A.2 The Route Fluctuation Prevention Scheme
B. Module for Reserved Route Use
Fluctuating routes in the smart grid environment may ultimately lead to a decrease in transmission reliability. To reduce the frequency of route fluctuations in the network, we propose a modification of the route selection method in the HWMP. Although a node may receive multiple RANN messages from multiple neighbor nodes, the current route selection method in the HWMP allows a node to maintain only one optimal route in the routing table. We have modified the algorithm through the stable route selection module and extended the route table of the
The most evident downside of the HWMP on-demand route discovery process is that when a PERR message is transmitted to the source node that initiated the data, the node will flood the network with broadcast PREQ messages. In this process, intermediate mesh STAs that can no longer transmit data discard all of their relaying packets to that destination. Furthermore, frequent creation of PERR and PREQ messages take up additional bandwidth that should be used instead for transmission of smart grid data-especially on-demand low-latency data that re-
ef =
P 1 Mi P i=1
Rmax
(2)
where Mi is the number of attempted MAC level retransmissions for frame i; P , the total number of successful and failed frame transmissions; and Rmax , the maximum allowed retransmission count. By using this metric, we place greater focus on the transmission status upstream. This is because, although a data frame may be successfully transmitted to another node, many MAC level retransmissions may be triggered in the course of achieving successful data transmission. We believe that this retransmission-adjusted failure value can be more accurate for smart grid environments, where precise calculation of the current radio status is needed for reliable data transmission. When considering a smart grid environment, we note that a single NAN may provide services to a varying number of applications; therefore, various types of data may be simultaneously transmitted inside the network. Given that these types may vary in frame size, treating the MAC retransmission level of each type identically would bias the results. We may safely assume that retransmission of a small frame reflects the criticality of the retransmission more than that of a large frame because smaller sized frames are less prone to bit errors. To penalize the airtime cost calculation according to the size of each data frame, we modify (2) as follows: P Bi Mi 1− Bmax + Bi (3) ef = i=1 P Rmax
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Fig. 3. Pseudocode for route selection in HWMP-RE.
quire more bandwidth and transmission opportunities. We aim to suppress the creation of PERR messages and limit the triggering of the on-demand route discovery process, so that fewer additional control packets are generated in the network. To do this, we build on the assumption from the route fluctuation prevention algorithm that each node maintains a primary route and a reserve route to the root mesh STA. As long as all nodes maintain such a reserve route, it can be used instead when the primary route is broken. This is achieved by using another route table-the reserve route table-that maintains the same structure as the primary route table but instead records the second best reserve route to the root mesh STA. The reserve route table is recorded whenever the primary route is also recorded, i.e., when a proactive RANN or on-demand PREQ broadcast is received. To use the reserve route stored in the reserved route use module, we employ the local path recovery method when a route becomes broken. Upon link breakage, the intermediate mesh STA with the broken link searches its reserve route table to check whether it maintains another route to the root mesh STA. If it does, the mesh STA will use the most stable one among reserve routes instead of the primary route to transmit its data to the root mesh STA. By using this method, the overhead from ondemand route discovery does not occur, allowing data packets to be transmitted more reliably to the root mesh STA. Even if a node discovers that it does not maintain a reserve route, a PERR message is not transmitted directly to the root mesh STA. Instead, the a PERR message is generated but only transmitted to one-hop neighboring mesh STAs that were using the broken link, by backtracking. Upon receipt of the PERR message, the mesh STA searches its reserve route table to see if another route to the root mesh STA exists. If the reserve route is valid, then it is used instead to support continuous multi-hop transmission. If a route is still not found, the above process is continued until the PERR message eventually reaches the source node. Only then does the source node initiate the on-demand route discovery process to find a new route to the root mesh STA. Our proposed reserve route use scheme can perform better while producing less packet overhead because generating and finding reserve routes induce no extra network transmission overhead,. Only in the worst case, when no reserved routes are found, does the proposed scheme perform identically to the original HWMP. C. Module for Delay-Tolerant Traffic Management If a node’s primary route is broken and no reserve route exists, then the corresponding node must declare that its links to the destination are completely broken and drop all packets in
the queue headed towards that destination. This is done to free up bandwidth for other packets that may be destined elsewhere. However, in smart grid environments, we wish to manage the system so that some of the important packets are not dropped. For example, consider delay-tolerant data such as periodic AMI and periodic power quality data. These data should be faulttolerant so as to provide maximum reliability and quality of service to users. On the other hand, data such as video surveillance monitoring and other low-priority data can be sacrificed to provide greater bandwidth for more important applications. Despite the broken link, we wish to manage and maintain these important data. To do this we employ a delay-tolerant traffic management method. The delay-tolerant traffic management module is based on the assumption that AMI and power quality data are not entirely time-stringent. For example, we assume that AMI packets are transmitted every 15 s while power quality packets are transmitted every 3 s. As long as these packets are transmitted successfully before the next packet arrives, they should be transmitted, instead of dropped, at a cost of longer latency. The main focus of the module is to use an extra queue in the routing layer to store packets generated from latency-tolerant traffic. By using this module, the packets are not dropped immediately, even when transmission of a packet fails or a link is broken. These packets are stored in the latency-tolerant queue until the next RANN period is initiated and a valid route to the destination is resolved for the corresponding mesh STA. However, multimedia packets and delay-stringent packets such as video surveillance are dropped because these data become useless if they fail to meet their deadline. Consequently, the reliability of only those packets that require high reliability can be increased to improve overall network performance. For our delay-tolerant traffic management scheme to work, methods that differentiate the packets according to their applications and services in the MAC layer are required. The IEEE 802.11s scheme has its QoS support basis on the 802.11e HCF EDCA, which provides 4 prioritized queues for each node AC_VO, AC_VI, AC_BE, and AC_BK in order of priority from the highest to the lowest. By using these queues, we can differentiate data priority. To guarantee packet transmission priority for requested AMI and power quality data, we assign the highest QoS type id (AC_VO). On the other hand, a low-priority packet such as video surveillance is assigned the lowest QoS type id (AC_BK). To separate latency-tolerant traffic and timestringent data, we use a reserved bit of the QoS control field to assign a latency-tolerant tag [7]. The delay-tolerant traffic management module selectively stores the data packets, which are dropped in the MAC layer after checking the latency-tolerant tag. By using a priority mapping method and latency-tolerant traffic tag, we can thus differentiate packets of latency-stringent traffic from that of latency-tolerant traffic, and drop only those packets that can be discarded. V. PERFORMANCE EVALUATION A. Simulation Environment The performance of the proposed scheme is evaluated via the ns-3 simulator. The ns-3 simulator has capabilities for designing
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Table 1. Simulation environment.
Table 2. Smart grid application set.
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of the network, we also show that we can provide better support for routing the critical data that require high reliability. The video surveillance data are live streaming feeds which report the physical status of power devices, and are thus constantly generated. However, these data are considered less important than AMI and power quality data, whose power-related services are much more critical in smart grid systems. Overall, these smart grid applications can be differentiated in the MAC layer by using multiple queues defined in the IEEE 802.11e EDCA scheme. For example, on-demand data that require high-reliability transmission, compared to other traffic, are queued in the highest priority queue with the latency-tolerant tag bit set; in contrast, the least important video surveillance data are queued in the lowest priority queue without the latency-tolerant tag bit. All applications are generated with the gateway (root mesh STA) as their destination, which was configured with constant-bit-rate (CBR) traffic. This is to reflect the smart grid environment, where much of the data from various parts of the network are collected and processed by a central server connected to the root mesh STAs. B. Simulation Results
various application designs and networking topologies, making it ideal for simulating our proposed scheme under a smart grid environment. The IEEE 802.11s codes implemented in the simulator were modified and used to compare our scheme with the original version of the code. The mesh STAs are equipped with one 802.11a transmission device, with the maximum transmission rate configured to 54 Mb/s. In the MAC layer, each node maintains four transmission queues according to the EDCA scheme in 802.11e. The original HWMP was used as the routing scheme, with the addition of HWMP-RE to enhance its reliability. Details of the simulation environment are shown in Table 1. A NAN node (mesh STA) transmits data by using the configured smart grid application set shown in Table 2. We have configured the applications in the ns-3 according to the smart grid environment installed by KEPRI [21] and the SG Network System Requirements Specification v4.1-draft3 [22]. In Table 2, the AMI data can be thought of as data from HAN nodes, whereas the power quality and video surveillance data are from the NAN applications. Periodic AMI, power quality, and management data are reported to the root mesh STA at every specified interval. According to the classification defined in [22] and [23], these periodic data can be classified as time-controlled traffic data that also have time-tolerant traffic properties (several seconds to hours). Therefore, although these data can be given lower access priority compared to requested data, they nevertheless must be transmitted with maximal reliability. Through simulations, we show that our delay-tolerant traffic management scheme can suitably support this type of data and improve its overall reliability. The requested AMI and power quality data are transmitted only upon request by the central server. By reducing the control packet overhead and instability
In the simulation scenario, the nodes were laid out on the network in a grid topology, and simulations were performed by using 9, 16, 25, 36, and 49 NAN nodes. Two root mesh STAs were deployed in the network at each corner of the grid. The amount of traffic generated in this environment naturally causes congestion in the network when the number of nodes gradually increases. Various parameters are measured and analyzed to evaluate the proposed scheme. The average packet delivery ratio represents the total number of data packets received by the root mesh STA node divided by the overall number of packets transmitted by all NAN devices in the network. We consider this factor as the most critical for satisfying the transmission reliability requirements of the smart grid system. The average end-to-end delay, which represents the average time required for a packet to reach the root mesh STA from the source node (a mesh STA), is also measured for each protocol. The average number of generated PERR/PREQs represents the overhead induced owing to link breakage and route recovery in the network. The average throughput indicates the total amount of data received by both root mesh STAs in each second. The HWMP-RE, which uses all algorithms as explained above, is compared with the original HWMP scheme and the enhancement algorithms previously proposed in [19]: early embodiments of the airtime cost metric calculation scheme and the route instability prevention scheme. We designate the other schemes for a smart grid network in a multigate routing algorithm based on the backpressure approach [12] and the IEEE 802.11s HWMP. To ensure fair evaluations, all algorithms are implemented in the ns-3 simulator. Fig. 4 shows the reliability of the three schemes as measured by the delivery ratio. All three methods guarantee nearly 95–98% reliability when 9 nodes are deployed in the network. However, the delivery ratio declines when more NAN devices are deployed, and congestion starts to occur in the network. Owing to poor route selection and route instability, the decline of the ratio in the original HWMP is steeper than in the proposed scheme, showing that our scheme guaran-
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Fig. 4. Packet delivery ratio comparison.
Fig. 6. Number of PERR/on-demand PREQ generation comparison.
Fig. 5. End-to-end delay comparison.
Fig. 7. Throughput comparison (Mb/s).
tees higher reliability. This is because the original HWMP cannot use better routes frequently owing to greater route fluctuation, which causes more packets to be transmitted through links with higher cost. On the other hand, the HWMP-RE can provide a higher delivery ratio even with severe congestion in the network, because better decisions are made in route selection by using the proposed route stability algorithm. The multigate routing algorithm cannot support delay-tolerant traffic management. This implies that interference from PERR and PREQ control packets is induced, and more packets are dropped because of link breakage. Consequently, the performance is inferior to that of HWMP-RE. Fig. 5 shows that better end-to-end delay is also guaranteed for the proposed scheme until 49 NAN devices are deployed in the network. This is because the selection of better routes due to prevention of fluctuation allows faster data transmission, whereas the routes selected by the original HWMP cause longer delays to route recovery, which leads to far more time consumed for transmission at each hop. Although the original HWMP still provides acceptable delays, our modifications further improve the latency ratings of each packet. When 49 nodes are deployed in the network for the HWMP-RE, the delay-tolerant traffic management scheme actually increases the end-to-end delay of delay-tolerant applications. However, this comes at the cost of much higher transmission reliability, although the induced end-to-end delay is still acceptable as smartgrid traffic permits large delays from a few seconds to several hours [22].
As Fig. 6 shows, fewer PERR/on-demand PREQ control messages are generated for the HWMP-RE, with a reduction of up to 65% when 25 nodes are deployed in the network. This may be due to three reasons. The first is that more accurate calculation of the link cost metric allows better selection of routes, which results in fewer failed transmission packets. The second is that the reduction in route fluctuation further increases the reliability of the HWMP-RE. The third and main reason is that the use of alternative routes greatly reduces the actual generation of PERR packets. Consequently, we see from Fig. 7 that the average throughput of the HWMP-RE is higher than that of the other schemes, as the bandwidth potentially wasted by the additional control packet overhead is greatly reduced. Although the HWMP-RE greatly enhances the reliability of the original HWMP, packet drops still occur and control packets are not fully reduced. This is due to limitations not of the routing protocol itself but mainly in the capacity of the 802.11a hardware specification, which provides up to 54 Mb/s. To provide even better performance, high-performing protocols such as 802.11n, 802.11ac, and multi-channel multi-interface environments can be used to increase the overall capacity of the network. Fig. 8 compares various factors regarding reliability in more detail. With 36 nodes in the network, we analyzed the transmission reliability of each application. Fig. 8 shows the performance of each scheme when the acceptable delivery ratio, which is the threshold for determining whether certain amount of traffic transmitted by a node is transmitted reliably, is pre-
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Fig. 8. Reliability comparison in terms of packet delivery.
Fig. 11. Packet delivery ratio of HWMP in various applications.
Fig. 9. Delay comparison in terms of deadline meet ratio.
Fig. 12. Packet delivery ratio of multigate routing in various applications.
Fig. 10. Packet delivery ratio of HWMP-RE in various applications.
configured The reliable traffic ratio on the y-axis represents the percentage of traffic in which the packet delivery ratio exceeds the threshold. As seen in the graph, over 90% of the applications had a delivery ratio of over 95% for the HWMP-RE. On the other hand, for the other schemes, less than 70% of the applications exceed the 95% threshold. This indicates that more applications are serviced with higher reliability when the HWMPRE is used. Hence, the HWMP-RE meets the needed reliability requirements better than the other schemes. Fig. 9 compares the three schemes when a certain deadline meet ratio is configured. For example, we define an application
as reliable if the overall end-to-end delay of the application is under the specified deadline, which is shown on the x-axis of Fig. 9. Owing to congestion, the end-to-end delay of each application is seriously degraded as packets are queued and the nodes cannot freely transmit their data because of interference from other nodes. Even under such an environment, the HWMPRE manages nearly 90% of its applications with a delay of less than 10 ms. As the required threshold becomes more stringent, differences between the two schemes start to emerge because the original HWMP cannot use alternative routes in the intermediate node. For this reason, the original HWMP creates serious control packet overhead, which induces even more congestion in the network and eventually degrades its performance. Although the multigate routing algorithm can also use alternative paths, its selection of alternative routes is based on a per-hop metric, which does not consider the end-to-end link cost. Figs. 10–12 examine each application environment used in the simulation. The performance of the periodic, on-demand, and video surveillance data are analyzed for the HWMP-RE, multigate routing mechanism, and original HWMP. Importantly, we note that for the HWMP-RE, the performance of video surveillance data transmission does not improve relative to the multigate routing algorithm and original HWMP. This is because when the network is congested, some packets must be sacrificed to provide reliable transmission for other smart grid applications. From Figs. 10–12, the video surveillance data maintain similar transmission ratios in relation to other applications.
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However, in the HWMP-RE, although the performance of all applications is increased, the CBR traffic as video surveillance data are transmitted with the least reliability (Fig. 10). This shows that our schemes, such as delay-tolerant traffic management, drop the video surveillance data while retaining more important data for more reliable transmission. This demonstrates that the HWMP-RE provides a better differentiated service that supports higher reliability for more important smart grid applications and services. Overall, the HWMP-RE may provide highly reliable wireless data transmission for smart grid environments by making various amendments to the original 802.11s HWMP. Our simple but efficient schemes for integrating the 802.11s HWMP with the smart grid may guarantee high reliability and provide sufficient differentiated services for various smart grid applications. Although the end-to-end delays of some applications increase, the changes are within tolerable limits of the requirements of these smart grid applications.
[5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
VI. CONCLUSION In this paper, we propose the design and architecture of a highly reliable HWMP protocol called the HWMP-RE for IEEE 802.11s. The main contribution of the HWMP-RE is that it resolves the problems of the original HWMP in smart grid network systems by using four different methods: 1) A modification of route selection mechanism to reduce route fluctuations, 2) a local route recovery mechanism by using alternative routes, 3) a calculation method of airtime cost metric that considers smart grid data characteristics, and 4) a latency-tolerant packet transmission scheme regarding smart grid service differentiation. Through ns-3 simulations, we have verified that the proposed HWMP-RE protocol shows higher performance than other existing schemes. The HWMP-RE is easily implemented in wireless mesh devices based on IEEE 802.11s, and takes advantage of its compatibility with mesh devices based on other standards. Hence, the HWMP-RE may be highly suitable for a NAN system that requires a highly reliable communication infrastructure. Furthermore, it can also be applied to other information network domains, such as WANs and HANs, as well as NANs. In particular, in the case of NANs, wireless mesh networks combined with the emerging IEEE 802.11ac can support very high throughput and reliable infrastructure to transmit important data from HANs to WANs. In addition, in the case of WANs, new technologies such as IEEE 802.11ah or IEEE 802.11af with wireless mesh networks can provide highly reliable infrastructure by covering wide areas with multi-hop communication and supporting data aggregation technology. REFERENCES [1] [2] [3] [4]
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Y. Zhang, R. Yu, S. L. Xie, W. Q. Yao, Y. Xiao, and M. Guizani, “Home M2M networks: Architectures, standards, and QoS improvement,” IEEE Commun. Mag., vol. 49, pp. 44–52, Apr. 2011. IEEE 802.11s, “Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 10: Mesh networking,” 2011. IEEE 802.11, “Wireless LAN medium access control (MAC) and physical layer (PHY) specification,” 2012. C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” RFC 3561, 2003. Tropos inc., “Tropos gridComT M : A wireless distribution area network for smart grids,” A TROPOS NETWORKS WHITE PAPER, 2009. Trilliant inc., Trilliant White Papers. [Online]. Available: http://www.trilli antinc.com/library-files/white-papers/HAN_white-paper.pdf T. Iwao, K. Yamada, M. Yura, Y. Nakaya, A. Cardenas, S. Lee, and R. Masuoka, “Dynamic data rorwarding in wireless mesh networks,” in Proc. IEEE SmartGridComm, Oct. 2010, pp 385–390. H. Gharavi and B. Hu, “Multigate communication network for smart grid,” Proc. IEEE, vol. 99, pp. 1028–1045, June 2010. A. Chan, S. J. Lee, X. Cheng, S. Banerjee, and P. Mohapatra, “The impact of link-layer retransmissions on video streaming in wireless mesh networks,” in Proc. ACM WICON, Maui, Hawaii, Nov. 2008, pp. 1–9. J. Whitbeck and V. Conan, “Hybrid DTN-MANET routing for dense and highly dynamic wireless networks,” Comput. Commun., vol. 33, pp. 1483– 1492, Aug. 2009. J. Ott, D. Kutscher, and C. Dwertmann, “Integrating DTN and MANET routing,” in Proc. ACM SIGCOMM Workshop, Pisa, Italy, Sept. 2006, pp. 221–228. K. Fall, “A delay-tolerant network architecture for challenged Internets,” in Proc. ACM SIGCOMM, Karlsruhe, Germany, Aug. 2003, pp. 27–34. K. Ramachandran, I. Sheriff, E. Royer, and K. Almeroth, “Routing stability in static wireless mesh networks,” in Proc. PAM, Louvain-la-neuve, Belgium, Apr. 2007, pp. 73–82. R. G. Garroppo, S. Giordano, and L. Tavanti, “Implementation frameworks for IEEE 802.11s systems,” Comput. Commun., vol. 33, pp. 336– 349, Feb. 2010. J. Jung, K. W. Lim, J. B. Kim, Y. B. Ko, Y. H. Kim, and S. Y. Lee, “Improving IEEE 802.11s wireless mesh networks for reliable routing in the smart grid infrastructure,” in Proc. IEEE ICC WorkShop, Kyoto, Japan, June 2011, pp. 1–5. D. Guo, J. Li, M. Song, and J. Song, “A novel cross-layer fouting algorithm in wireless mesh network,” in Proc. IEEE ICPCA, Port Elizabeth, South Africa, Oct. 2011, pp. 262–266. N. Myoung, Y. Kim, and S. Lee, “The design of communication infrastructures for smart DAS and AMI,” in Proc. IEEE ICTC, Nov. 2010, pp. 461–462. OpenSG users group, SG network system requirements specification v4.1draft3. [Online]. Available: http://www.osgug.ucaiug.org G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. Johnson, “M2M: From mobile to embedded Internet,” IEEE Commun. Mag., vol. 49, pp. 36–43, Apr. 2011.
Jaebeom Kim received his B.S. degree in Computer Engineering from the Korea Polytechnic University, Korea, in 2010. He is currently a Ph.D. student in the School of Information and Computer Engineering of Ajou University, Korea. His research interests are in the areas of wireless mesh networks, wireless LAN, and smart grid communications.
Dabin Kim received her B.S. degree in Computer Science from Seoul Women’s University, Korea, in 2010. She is currently a Ph.D. student in the School of Information and Computer Engineering of Ajou University, Korea. Her research interests are in the area of wireless mesh networks, wireless LAN, and content centric network.
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Keun-Woo Lim received his B.S. and M.S. in the School of Information and Computer Engineering of Ajou University, Korea, in 2007 and 2009 respectively. He is currently studying for his Ph.D. degree of Computer Engineering in Ajou University. His research interests are in the areas of wireless mesh networks and wireless sensor networks.
Young-Bae Ko is currently a Professor in the School of Information and Computer Engineering of Ajou University, Korea, leading the ubiquitous networked systems (UbiNeS) laboratory. He was also a Visiting Professor of Coordinated Science Lab at University of Illinois, Urbana Champaign (UIUC) for the 2008– 2009 academic year. Prior to joining Ajou University in 2002, he was with the IBM T. J. Watson Research Center, Hawthorne, New York, as a Research Staff member in the Department of Ubiquitous Networking and Security. He received his Ph.D. degree in computer science from Texas A&M University, and B.S. and M.B.A. degrees from Ajou University. His research interests are in the areas of mobile computing and wireless networking. In particular, he is actively working on mobile ad hoc networks, wireless mesh sensor networks, and various ubiquitous networked system issues. He was the recipient of a best paper award from ACM Mobicom 1998. He has served on the program committees of several conferences and workshops. He also serves on the editorial board of ACM Mobile.
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Sang-Youm Lee received his M.S. degree in Information and Communications Engineering from ChungNam National University, Korea, in 2007 and B.S degree in Electrical Engineering from Dong-Guk University, Korea, in 1996. He is currently a Senior Research Engineer in KEPCO Research Institute in Korea. His research interests are in the area of communication systems and smart grid.
