Error Analysis of Scheduling Sleeping Nodes in Wireless Sensor Networks

The sleeping technique is one of the most popular ways to save energy of battery powered sensor nodes. Many existing researches on sleeping technique are based on the pre-knowledge of sensor nodes deployment, e.g., a known probability distribution of sensor nodes in the target sensing field. Thus, whether or not a scheduling sleeping scheme has a good performance mostly depend upon the pre-knowledge of sensor nodes deployment. In this paper, we show the discrepancy of scheme performances including energy consumption and network lifetime based on inaccurate pre-knowledge of sensor deployment. Through the analytical studies, we conclude that the discrepancy is very large and can not be neglected. We hence propose a distribution-free approach to study energy consumption. In our approach, no assumption of probability distribution of sensor node deployment is needed. The proposed approach has yielded good estimation of network energy consumption.

[1]  Zhen Liu,et al.  Maximum lifetime routing in wireless ad-hoc networks , 2004, IEEE INFOCOM 2004.

[2]  Deborah Estrin,et al.  Adaptive Energy-Conserving Routing for Multihop Ad Hoc Networks , 2000 .

[3]  Yang Xiao Energy-efficient scheduling and MAC for sensor networks, WPANs, WLANs, and WMANs , 2006, Comput. Commun..

[4]  Xiaojiang Du,et al.  Weaving a Proper Net to Catch Large Objects , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[5]  Xiaojiang Du,et al.  An Optimal Sensor Network for Intrusion Detection , 2009, 2009 IEEE International Conference on Communications.

[6]  Mohamed F. Younis,et al.  Energy-aware routing in cluster-based sensor networks , 2002, Proceedings. 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems.

[7]  Yang Xiao,et al.  Modeling Detection Metrics in Randomized Scheduling Algorithm in Wireless Sensor Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[8]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[9]  Yang Xiao,et al.  WSN10-3: Maximizing Network Lifetime under QoS Constraints in Wireless Sensor Networks , 2006, IEEE Globecom 2006.

[10]  Ying Zhang,et al.  Asymptotic Coverage and Detection in Randomized Scheduling Algorithm in Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Communications.

[11]  Xiaojiang Du,et al.  Three Dimensional Intrusion Objects Detection under Randomized Scheduling Algorithm in Sensor Networks , 2008, 2008 The 4th International Conference on Mobile Ad-hoc and Sensor Networks.

[12]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[13]  Yang Xiao,et al.  Lightweight Deployment-Aware Scheduling for Wireless Sensor Networks , 2005, Mob. Networks Appl..

[14]  Zhi-Quan Luo,et al.  A Distributed Algorithm with Linear Convergence for Maximum Lifetime Routing in Wireless Sensor Networks , 2005 .

[15]  Yunghsiang Sam Han,et al.  Scheduling Sleeping Nodes in High Density Cluster-based Sensor Networks , 2005, Mob. Networks Appl..

[16]  Yang Xiao,et al.  IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, PAPER ID: TPDS-0307-0605.R1 1 Random Coverage with Guaranteed Connectivity: Joint Scheduling for Wireless Sensor Networks , 2022 .

[17]  Christos G. Cassandras,et al.  On maximum lifetime routing in Wireless Sensor Networks , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[18]  Yi Pan,et al.  On optimizing energy consumption for mobile handsets , 2004, IEEE Transactions on Vehicular Technology.

[19]  Yang Xiao,et al.  A Survey of Energy-Efficient Scheduling Mechanisms in Sensor Networks , 2006, Mob. Networks Appl..