Mesh is a network topology that achieves high throughput and stable intercommunication. With great potential, it is expected to be the key architecture of future networks. Wireless sensor networks are an active research area with numerous workshops and conferences arranged each year. The overall performance of a WSN highly depends on the energy consumption of the network. This paper designs a new routing metric for wireless mesh sensor networks. Results from simulation experiments reveal that the new metric algorithm improves the energy balance of the whole network and extends the lifetime of wireless mesh sensor networks (WMSNs). 1. Introduction Wireless sensor networks are one of the most rapidly evolving research and development fields for microelectronics. A wireless sensor network potentially comprises hundreds to thousands of nodes. These nodes are generally stationary after deployment, with the exception of a very small number of mobile sensor nodes, as shown in Figure 1. Wireless sensor networks characterize themselves in their distributed, dynamic, and self-organizing structure. Each node in the network can adapt itself based on environmental changes and physical conditions. Sensor nodes are expected to have low power consumption and simple structure characteristics, while possessing the ability of sensing, communicating, and computing. For conventional wireless networks, high degree of emphasis on mobility management and failure recovery is located in order to achieve high system performance. However, as the power of sensor nodes is usually supplied by battery with no continual maintenance and battery replenishment, to design a good protocol for WSNs, the first attribute that has to be considered is low energy consumption that could promise a long network lifetime. The recent advances of WSNs have made it feasible to realize low-cost embedded electric utility monitoring and diagnostic systems [1, 2]. In these systems, wireless multifunctional sensor nodes are installed on the critical equipment of the smart grid to monitor the parameters critical to each equipment’s condition. Such information enables the smart-grid system to respond to varying conditions in a more proactively and timely manner. In this regard, WSNs play a vital role in creating a highly reliable and self-healing smart electric power grid that rapidly responds to online events with appropriate actions. The existing and potential applications of WSNs on smart grid span a wide range, including wireless automatic meter reading (WAMR), remote system monitoring, and equipment fault
References
[1]
L. Lo Bello, O. Mirabella, and A. Raucea, “Design and implementation of an educational testbed for experiencing with industrial communication networks,” IEEE Transactions on Industrial Electronics, vol. 54, no. 6, pp. 3122–3133, 2007.
[2]
B. Lu and V. C. Gungor, “Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks,” IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4651–4659, 2009.
[3]
R. Karrer, A. Sabharwal, and E. Knightly, “Enabling large-scale wireless broadband: the case for TAPs,” in Proceedings of the Workshop on Hot Topics in Networks (HotNets '03), no. 1, pp. 27–32, Cambridge, Mass, USA, 2003.
[4]
V. Gambiroza, B. Sadeghi, and E. W. Knightly, “End-to-end performance and fairness in multihop wireless backhaul networks,” in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom '04), pp. 287–301, October 2004.
[5]
IEEE Std 802.11TM–2007, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications,” IEEE Computer Society, June 2007.
[6]
J. D. Camp and E. W. Knightly, “The IEEE 802.11s extended service set mesh networking standard,” IEEE Communications Magazine, vol. 46, no. 8, pp. 120–126, 2008.
[7]
M. U. Ilyas and H. Radha, “Increasing network lifetime of an IEEE 802.15.4 wireless sensor network by energy efficient routing,” in Proceedings of the IEEE International Conference on Communications (ICC '06), pp. 3978–3983, July 2006.
[8]
F. Zhang, H. Zhou, and X. Zhou, “A routing algorithm for zigbee network based on dynamic energy consumption decisive path,” in Proceedings of the International Conference on Computational Intelligence and Natural Computing (CINC '09), pp. 429–432, June 2009.
[9]
P. Sereiko, “Wireless Mesh Sensor Networks Enable Building Owners, Managers, and Contractors to Easily Monitor HVAC Performance Issues,” 2004, http://www.automatedbuildings.com/news/jun04/articles/sensicast/Sereiko.htm.
[10]
D. B. Johnson and D. A. Maltz, “Dynamic source routing in AdHoc wireless networks,” in Mobile Computing, vol. 353, Kluwer Academic, Boston, Mass, USA, 1996.
[11]
C. Perkins, “Ad-Hoc on-demand distance vector routing,” in Proceedings of the IEEE Military Communications Conference on Ad Hoc Networks (Milcom '97), 1997.
[12]
P. Kyasanur and N. Vaidya, “Multi-channel wireless networks: capacity and protocols,” Tech. Rep., University of Illinois at Urbana-Champaign, Urbana, Ill, USA, 2005.
[13]
H. Hassanein and A. Zhou, “Routing with load balancing in wireless ad hoc networks,” in Proceedings of the 4th ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (ACM MSWiM '01), pp. 89–96, July 2001.
[14]
C. Perkins, E. Belding-Royer, and S. Das, “Ad Hoc On-Demand Distance Vector (AODV) Routing,” IETF RFC 3561, July 2003.
[15]
D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, “A High-Throughput Path Metric for Multi-Hop Wireless Routing,” in Proceedings of the Ninth Annual International Conference on Mobile Computing and Networking (MobiCom '03), pp. 134–146, September 2003.
[16]
R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multi-hop wireless mesh networks,” in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom '04), pp. 114–128, October 2004.
