Wireless Mesh Sensor Networks in Pervasive ... - Semantic Scholar

Wireless Mesh Sensor Networks in Pervasive Environment: a Reliable Architecture and Routing Protocol Feilong Tang, Minyi Guo, Minglu Li, Yanqin Yang, Daqiang Zhang and Yi Wang Department of Computer Science and Engineering, Shanghai Jiao Tong University {tang-fl, guo-my, li-ml}@cs.sjtu.edu.cn

Abstract Wireless mesh sensor network (WMSN) merges advantages of wireless mesh networks and wireless sensor networks, especially on scalability, robustness and balanced energy dissipation. Routing in WMSNs faces with more challenges than that in traditional sensor networks on account of multiple sink nodes and the mobility of nodes. This paper 1 focuses on two challenging problems. Firstly, we propose a reliable architecture of WMSNs by deploying multiple mobile mesh nodes in each sensor network to collect sensed data, which improves the scalability and performance of WMSNs. Also, we design a routing protocol characteristic to WMSNs. The routing protocol aims at maximizing the lifetime of sensor networks by reducing total energy consumption of a sensor network, as well as balancing energy usage among sensor nodes.

1. Introduction Pervasive computing is an emerging research field that brings in revolutionary paradigms for computing models in the 21st century. The goal of pervasive computing is to create ambient intelligence, reliable connectivity, and ubiquitous services in order to adapt to the associated context and activity. To make this envision a reality, various sensor networks have to be interconnected to collect context information, providing context-aware pervasive computing with adaptive capacity to dynamically changing environment. Wireless sensor networks (WSN) can help people to be aware of a lot of context information anytime anywhere by monitoring, sensing, collecting and 1

This paper was supported by 863 Program of China (Grant Nos.

2006AA01Z172 and 2006AA01Z199), National Natural Science Foundation of China (Grant Nos. 60533040 and 60473092) and Natural Science Foundation of Shanghai Municipality of China (05ZR14081).

2007 International Conference on Parallel Processing Workshops (ICPPW 2007) 0-7695-2934-8/07 $25.00 © 2007

processing the information of various environments and scattered objects. The flexibility, fault tolerance, high sensing, self-organization, fidelity, low-cost and rapid deployment characteristics of sensor networks are ideal to many new and exciting application areas such as military, environment monitoring, intelligent control, traffic management, medical treatment, manufacture industry, antiterrorism and so on. However, existing researches on WSNs are built on the network architecture (called flat architecture) such that all sensor nodes are homogeneous and send their data to a single sink node by multiple hops [1, 2]. On account of limited power, computing and memory of sensor nodes, such a flat architecture inherently has the following problems: x unbalance on energy consumption among nodes. Sensor nodes near the sink inevitably drain their energy ahead of other nodes far from the sink because the former forwards data for the entire sensor network, even if maximizing network lifetime based routing protocols[3] and mobile base stations [4] are used in the flat architecture of WSNs. x poor scalability. With the expansion of sensor networks, the average number of hops between a source sensor node to the single sink become more and more, resulting in too much energy consumption and transmission delay for data transmission. x poor robustness. Some sensor nodes potentially cannot send their data back to the sink node if their neighbor nodes do not work because of exhausted battery, bad environment and others. Owing to above limitations of traditional architecture of WSNs, wireless mesh sensor network (WMSN) is attracting more and more attentions from industry and academic communities as a possible way to improve the scalability, reliability and throughput of sensor networks and support the node mobility [5]. However, there has not yet a well-defined architectural

model of WMSNs. Also, there is a lack of energyefficient routing protocols for WMSNs at this time because routing is highly related to the network architecture. Neither of existing Internet and wireless mobile network routing protocols sufficiently address the combined and new requirements and issues of communications in WMSNs. This paper is set to address the above challenging issues, focusing on two major parts: (1) architectural model of WMSNs that greatly extend the functionalities of traditional sensor networks to suit for pervasive computing, and (2) energy-efficient routing protocol under the proposed architecture. Due to the page limit, we present the architecture and routing protocol in this paper, while the performance studies will be presented in a sequel paper. The remainder of this paper is organized as follows. We review related work in the next Section. In Section 3, we propose a reliable and scalable architecture for WMSNs. Section 4 presents a routing protocol aiming at maximizing the lifetime of sensor networks. Finally, Section 5 concludes the paper.

2. Related work In this Section, we review the background and work related to our research issues, i.e., the architecture and routing protocols in WSNs.

2.1 Architectural model of wireless sensor networks Existing researches on WSNs generally are built on flat architecture mentioned above, where hundreds of even thousands of sensors (randomly) distributed in a monitoring area self-organize as a sensor network with a single sink node for connecting to wired or wireless networks. Each of these scattered sensor nodes has the capabilities to collect data and route data back to the sink and further to the end users by multiple hops [1]. Such a flat architecture inherently is poor on scalability and robustness. Wireless mesh network[5] is a type of mobile wireless network that is decentralized, relatively inexpensive, and very reliable and resilient. The most important feature that distinguishes wireless mesh networks from other wireless networks is high robustness, which means that if one node drops out of the network, due to hardware failure or any other reasons, its neighbors simply find another route. Sereiko firstly proposed the concept of wireless mesh sensor network (WMSN) through deploying wireless routers to connect sensor networks [6].


2.2 Routing protocols for wireless sensor networks Many routing protocols have been specifically designed for WSNs. From the perspective of network architecture, these routing protocols generally fall into three categories: flat-based, hierarchical-based and location-based routing [7]. 2.2.1 Flat-based routing. In flat-based routing, all nodes are typically assigned equal roles or functionality. Flooding [1, 8] is a classical mechanism to relay data in sensor networks without the need for topology maintenance, but with several serious deficiencies such as implosion, overlap and resource blindness. Gossiping, a derivation of flooding, sends data to one randomly selected neighbor, which avoids implosion problem. SPIN [8] is a family of adaptive protocols and addresses the deficiencies of classic flooding by considering resource adaptation and data negotiation between nodes. Directed Diffusion [9] is a data-centric and application-aware paradigm in the sense that all data generated by sensor nodes is named by attributevalue pairs. Rumor routing is a variation of directed diffusion. It routes the queries to the nodes that have observed a particular event rather than flooding the entire network to retrieve information about the occurring events. MCFA exploits the fact that the direction of routing is always known, that is, towards the fixed external base-station. Hence, a sensor node need not have a unique ID nor maintain a routing table. Instead, each node maintains the least cost estimate from itself to the base-station. 2.2.2 Hierarchical-based routing. In hierarchical routing, sensor nodes play different roles in the network, where higher energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing in the proximity of the target. LEACH [10] is a 2-level hierarchical routing protocol which attempts to minimize global energy dissipation and distribute energy consumption evenly across all nodes. The nodes self-organize into local clusters with one node in each cluster acting as a cluster head. PEGASIS[11] is an enhancement over the LEACH protocol, where nodes need only communicate with their closest neighbors and they take turns in communicating with the sink. In TEEN [12], a cluster node sends a hard threshold and a soft

Combining wireless mesh networks and wireless sensor networks, we propose an architecture of wireless mesh sensor network (WMSN) by deploying multiple wireless mesh routers equipped with gateways in each sensor network, as shown in Fig.1. The mesh routers deployed in different sensor networks automatically interconnect to form a mesh network while are connected with Internet through powerful base stations. In the proposed architecture, there are three kinds of networks on three logical layers respectively: x Wireless sensor network for monitoring objects and reporting the objects’ information, x Wireless mesh network for transmitting sensed data in long-distance and reliable way, and x Internet for users to remotely access sensed data.

threshold to its members to meet time-critical sensing applications. 2.2.3 Location-based routing. In this kind of routing, sensor nodes are addressed by means of their locations, and route data using node positions. The distance between neighboring nodes can be estimated by means of incoming signal strengths or GPS (Global Positioning System). Relative coordinates of neighboring nodes can be obtained by exchanging such information between neighbors. GAF [13] and SPAN [14] are representations of this kind of routing protocols.

3. An architecture of wireless mesh senor network

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Fig. 1. A scalable wireless mesh sensor network architecture.

Accordingly, a WMSN is composed of three kinds nodes: sensor node, wireless mesh gateway(WMG 2 ) and wireless mesh router(WMR). In particular, base stations are used to support the mobility of WMGs andWMRs, and connect wireless mesh network with Internet. Sensor nodes continuously or intermittently detect objects and then send data to the most appropriate WMG based on specified routing policies. WMGs work as sink nodes and gateways of low-level 2

We use WMG and gateway interchangingly in this paper.


wireless sensor networks, as well as routers of middlelevel wireless mesh network. By comparison, WMRs only serve as routers of wireless mesh network. WMGs and WMRs self-organize as the middle-level wireless mesh network for long-distance transmission and interconnect of sensor networks. Different networks in the architecture use different medium access control (MAC) and routing protocols. In general, wireless sensor networks use short-distance communication protocol (e.g., 802.15.4) while wireless mesh network uses long-distance transmission protocol (e.g., 802.11). More specifically, three kinds of nodes

respectively support different MAC protocols: sensor nodes only support 802.15.4; WMRs only support 802.11; and WMGs support both. The proposed architecture has the following advantages: x self-organization and self-configuration. Sensor nodes can automatically discover neighboring nodes in the same network and self-organize as sensor networks. On the other hand, WMGs and WMRs in a specific area automatically interconnect to form a mesh network. x highly salability. Firstly, whenever sensor nodes or WMGs (WMRs) enter networks, new network topology is automatically set up. Secondly, multiple gateways are deployed in a sensor network in the energy-efficient way; and sensor nodes send data to the best gateway, increasing network throughput and avoid the single point of failure. Finally, long-distance transmission among WMGs (WMRs) further reduces the average number of hops of transmission among different sensor networks. x highly reliability (by self-managing and selfhealing). In low-level sensor networks, if WMGi fails, routing protocols automatically forward sensed data to another WMGj(i Į j). Similarly, in middle-level wireless mesh network, if a WMG/WMR fails, traffic destined to the node will be redirected to its neighboring node. x rapid deployment and convenient setting. Hierarchical network architecture is automatically formed as long as sensors and WMGs (WMRs) power on, which not only simplifies installation and deployment of networks but also provides incremental network setting. x interconnection of heterogeneous sensor networks. Different types of sensor networks are interconnected only if they can communicate with WMGs. x interconnection of sensor networks with Internet, which significantly extend application areas of wireless sensor networks.

networks and propose a routing mechanism aiming at maximizing network lifetime. Let a set of gateways be distributed randomly in a sensor network, forming a mixed sensor network. The radio range of a sensor node only covers its immediate neighboring nodes. Further, let any sensor node Si (1İ i İ n) keep static while gateway(s) Gj (1 İ j İ m) discretely move within the range of its sensor network. The sensor network topology changes if any gateway moves to a different place. We define the period during which all gateways are static as a round so that during a round, the sensor network topology keeps unchanged. Network lifetime is the most important performance of sensor networks [15]. In this paper, we define network lifetime as the time when the first sensor node drains its energy. Firstly, we formally describe the routing protocol with the goal of maximizing network lifetime. Assume gateways have unrestricted energy. Ideally, maximizing lifetime of sensor networks needs to simultaneously satisfy the following two conditions: (1) total energy consumption of all sensors in a network ě Ei is minimal, where Ei is energy consumption of node Si; (2) differences between individual node’ energy consumption Ei(1İiİn) and average energy consumption

¦ (E E)

variance D2=

Routing highly depends on network architecture [2]. In the architecture discussed above, mesh network routing in middle layer has been well researched. In this Section, we focus on routing in low-level sensor


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where E r and Et : energy consumption for receiving and sending a packet respectively(here we do not consider energy consumption for data processing); xij : the number of packets sent from Si to Sj; and N(i): the set of neighboring nodes of Si. Ei in equation (2) is Si’ energy consumption for receiving and sending data during a round; T in equation (3) represents the number of packets generated by Si during a round. Equation (4) restricts Si sends data packets to the same gateway Gl during a round. Accurately resolving above goal is rather complex because it probably is a NP problem. In this Section, we propose a heuristic routing protocol, providing results approximate to above design goal. Similar to existing related work [16], we let m gateways only be deploy in a set of feasible places such that P={Pi: Pi is a feasible place in the network area}, m places of them are deployed gateways during a round in terms of energy-efficient criteria. Unlike those work which resets up routing tables at the beginning of each round, our principle is to accumulate routing tables round by round. After all places are deployed gateways, all sensor nodes queried previously route keep a routing table that contains the best gateway and corresponding path, without demand to set up new routing any more. If a gateway changes place in a new round, it only notifies all sensor nodes its new place. This approach significantly reduces delay and saves energy for routing discovery and maintenance. Let there be |P| feasible places to deploy gateways. Using this approach, each node keeps a routing table containing at most |P| entries, with an extra storage overhand (|P|m), which is acceptable under limited feasible places |P|. We describe the basic idea of the algorithm as follows. Set up routing table x Routing query. Sensor nodes, which need to send data but has not set up a routing table, query routing information by flooding a query packet RREQ with m destinations, i.e., all m gateways. x Response to routing query. For each pair nodes (Si, Gj), there generally are multiple different paths pathij(k). Thus, when Gj receives the first query packet from Si, it waits a given timeout to collect multiple paths. After the timeout, Gj calculates the shortest path between Si and Gj by the formula: pathij= Min (|pathij(k)|) for all k, where pathij denotes the shortest path between Si and Gj; |pathij(k)| is the number of hops in


pathij(k), and Min() is a function to solve the path with the least hops among all pathij(k). Finally, Gj returns pathij to Si in a routing query packet RRES. x Si receives m pathij (j=1,2,…, m) to m gateways respectively, stores them in local routing table, and takes the path with minimal hops as actual forwarding path. Update routing table by adding entries x At the beginning of a new round, moved gateways notify all sensor nodes in local network of their new places. Each node checks its own routing table when it sends data. If there does not exist an entry destined to the new places, the sensor node sets up new routing and selects the best path using the method described above. Note that unmoved gateways do not need to issue such a notification. x After a sensor node accumulates |P| entries in its local routing table, it henceforth only selects the best path from m entries corresponding to m deployed places during the current round, without need to query routing any more. In our current consideration, we define the shortest path as the best path. Data forwarding x Source sensor node Si encapsulates the first data packet along with the best path information, then sends the packet. Intermediate nodes forward the packet and cache the path. Succedent data packets are not attached the path information. To explain above procedure more clearly, we illustrate a process of routing setting and maintenance by an example with five feasible places and three gateways such that |P|=5 and m=3. Five places are represented by A,B,C,D and E respectively, by which we also denote gateways deployed at corresponding places. In routing table shown in Fig.2, route means the shortest path from Si to corresponding gateway, hops represents the number of hops on the corresponding shortest path. (1) In the first round, let gateways are deployed at places A, B and C. Sensor node Si discovers routing information (see Fig. 2(a) ), then selects the shortest path “-----,B” as its route, i.e., sends data packets to the gateway B along the specified path. (2) In the next round, let the gateway deployed previously at place B be moved to place D while gateways at places A and C are not moved. The shortest paths from Si to gateways A and C are unchanged because these gateways and all sensor nodes are located at the same places as those in last

round. However, routing destined to gateway D will be added to its routing table. Similarly, Si selects “-----,D” with 5 hops as the short path during current round. (3) In the third round, let gateway A be moved to place E while gateways C and D keep unmoved. By similar procedure, Si sets up incremental routing table shown in Fig.2(c). By comparison, Si still selects “----,D” with 5 hops as the short path. (4) After each feasible place has been deployed a gateway, Si completes its routing setting. Form then on, Si only needs to select the path with the least hops as its forwarding path from current m places during current round. Other sensor nodes similarly set up routing tables. Pi

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Fig.2. Routing table generation and maintenance of node Si.

5. Conclusions We have proposed an architecture for WMSNs, and designed a routing protocol maximizing lifetime of WMSNs. The proposed three-layer architecture is selforganized, self-healing, and thus reliable. The distinguishing feature of the architecture is that it significantly improves the scalability of sensor networks by introducing mesh nodes for long-distance transmission. Our routing protocol was designed for mixed sensor networks with multiple-gateway in WMSNs, which merges the advantages of table-driven and demand-driven routing protocols.

6. References [1] I.F., Akyildiz, W. Su, Y.Sankarasubramaniam and E. Cayirci, “A survey on sensor networks”, IEEE Communications Magazine, Vol.40, No.8, 2002, pp. 102-114. [2] K. Akkaya and M.Younis, “A survey on routing protocols for wireless sensor networks”, Ad Hoc Networks, Vol. 3, 2005, pp.325-349.


[3]

J.Park and S.Sahni, “An online heuristic for maximum lifetime routing in wireless sensor networks”, IEEE Transactions on Computers, Vol. 55, No. 8, 2006, pp. 1048-1056. [4] H.M.Ammari and S.K. Das, “Data dissemination to mobile sinks in wireless sensor networks: an information theoretic approach”, Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005. [5] I.F. Akyildiz, X.D. Wang and W.L. Wang, “Wireless mesh networks: a survey”, Computer Networks 47, 2005, pp. 445̢ 487. [6] P.Sereiko, “Wireless Mesh Sensor Networks Enable Building Owners, Managers, and Contractors to Easily Monitor HVAC Performance Issues”, http://www.automatedbuildings.com/ news/ jun04/articles/sensicast/Sereiko.htm. [7] J.N.Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey,” IEEE Wireless Commun., vol. 11, no. 6, Dec. 2004, pp. 6–28. [8] W. R. Heinzelman, J. Kulik and H. Balakrishnan, “Adaptive Protocols for Information Dissemination in Wireless Sensor Networks”, Proc. of ACM MobiCom 99, Seattle, WA, 1999, pp. 174-185. [9] C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed Diffusion: A scalable and roubst communication paradigm for sensor networks”, Proceedings of ACM MobiCom, 2000, pp. 5667. [10] R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “LEACH: Energy-efficient Communication protocol for wireless microsensor networks”, Proceedings of Hawaii International Conference on System sciences, 2000, pp. 3005~3014. [11] S. Lindsey, C. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information Systems”, IEEE Aerospace Conference Proceedings, Vol. 3, 2002, pp. 1125-1130. [12] A. Manjeshwar and D.P. Agrawal, “TEEN: A routing protocol for enhanced efficiency in wireless sensor networks”, Proceedings of the 15th Parallel and Distributed Processing Symposium, San Francisco: IEEE Computer Society, 2001, pp. 2009~2015. [13] Y. Xu, J. Heidemann and D. Estrin, “Geography-informed Energy Conservation for Ad-hoc Routing”, Proceedings of the Seventh Annual ACM/IEEE International Conference on Mobile Computing and Networking, 2001, pp. 70-84. [14] B. Chen, K. Jamieson, H. Balakrishnan and R. Morris, “SPAN: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks”, Wireless Networks, Vol. 8, No. 5, Page(s): 481-494, September 2002. [15] Y.T. Hou, Y. Shi, J.P. Pan and S.F. Midkiff, “Maximizing the Lifetime of Wireless Sensor Networks through Optimal SingleSession Flow Routing”, IEEE Transactions on mobile computing, Vol.5, No.9, 2006, pp. 1255-1266. [16] S.R. Gandham, M. Dawande, R. Prakash and S. Venkatesan, “Energy efficient schemes for wireless sensor networks with multiple mobile base stations”, Proceedings of IEEE GLOBECOM2003, December 2003, pp. 377-381.