A Sleep Scheduling Algorithm for Target Tracking in Energy Harvesting Sensor Networks

Target tracking is a typical application in wireless sensor networks. Many target tracking algorithms have been proposed. However, the limited energy storage of sensors affect the performance of target tracking. The emerging energy harvesting technology can alleviate the energy limitation problem. At present, few work on target tracking in energy harvesting sensor networks has been done. In this paper, we study the problem of target tracking in energy harvesting sensor networks. A heuristic sleep scheduling algorithm is proposed. The algorithm takes the target speed and the amount of harvested energy in the target tracking region into account, which tries to put the nodes with more residual energy to work without modeling of the energy harvesting process. Simulation results show that the proposed algorithm can effectively utilize the harvested energy and extend network lifetime, compared with the algorithm that did not consider energy harvesting.

[1]  Honggang Wang,et al.  Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis , 2013, J. Netw. Comput. Appl..

[2]  Jie Gao,et al.  Differential Forms for Target Tracking and Aggregate Queries in Distributed Networks , 2013, IEEE/ACM Transactions on Networking.

[3]  Binoy Ravindran,et al.  Probability-Based Prediction and Sleep Scheduling for Energy-Efficient Target Tracking in Sensor Networks , 2013, IEEE Transactions on Mobile Computing.

[4]  Juan Feng,et al.  An Energy-Efficient Sleep Strategy for Target Tracking Sensor Networks , 2014 .

[5]  Toufik Ahmed,et al.  On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review , 2013, IEEE Communications Surveys & Tutorials.

[6]  Feng Zhao,et al.  Transmission scheduling for broadcasting with two energy-harvesting switching transmitters , 2013, IET Wirel. Sens. Syst..

[7]  Jiming Chen,et al.  Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity , 2013, IEEE Transactions on Wireless Communications.

[8]  Longbo Huang,et al.  Utility Optimal Scheduling in Energy-Harvesting Networks , 2010, IEEE/ACM Transactions on Networking.

[9]  Khalid A. Darabkh,et al.  Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks , 2012, J. Netw. Comput. Appl..

[10]  Alireza Seyedi,et al.  Harvesting Resource Allocation in Energy Harvesting Wireless Sensor Networks , 2013, ArXiv.

[11]  Hwee Pink Tan,et al.  Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors , 2013, Comput. Networks.

[12]  Gil Zussman,et al.  Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms , 2011, IEEE Transactions on Mobile Computing.

[13]  Andreas Decker.  Solar energy harvesting for autonomous field devices , 2014, IET Wirel. Sens. Syst..

[14]  Biplab Sikdar,et al.  An Analytical Approach to the Design of Energy Harvesting Wireless Sensor Nodes , 2013, IEEE Transactions on Wireless Communications.

[15]  Pramod K. Varshney,et al.  Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks , 2012, IEEE Signal Processing Letters.

[16]  Juan Feng,et al.  Smart Power Management and Delay Reduction for Target Tracking in Wireless Sensor Networks , 2014, J. Electr. Comput. Eng..

[17]  Yiannis Andreopoulos,et al.  Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[18]  Dimitrios D. Vergados,et al.  A survey on power control issues in wireless sensor networks , 2007, IEEE Communications Surveys & Tutorials.

[19]  Joseph A. Paradiso,et al.  Energy scavenging for mobile and wireless electronics , 2005, IEEE Pervasive Computing.

[20]  Shaojie Tang,et al.  EFCon: Energy flow control for sustainable wireless sensor networks , 2013, Ad Hoc Networks.

[21]  Juan Vicente Capella,et al.  Wireless sensor network with energy harvesting: modeling and simulation based on a practical architecture using real radiation levels , 2016, Concurr. Comput. Pract. Exp..

[22]  Petar M. Djuric,et al.  Likelihood Consensus and Its Application to Distributed Particle Filtering , 2011, IEEE Transactions on Signal Processing.

[23]  Zhao Hongwei,et al.  Hierarchically Coordinated Power Management for Target Tracking in Wireless Sensor Networks , 2013 .

[24]  U. Alvarado,et al.  Energy harvesting technologies for low‐power electronics , 2012, Trans. Emerg. Telecommun. Technol..