Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption.

[1]  Jiming Chen,et al.  Energy-Efficient Intrusion Detection with a Barrier of Probabilistic Sensors: Global and Local , 2013, IEEE Transactions on Wireless Communications.

[2]  Jiming Chen,et al.  Barrier coverage in wireless sensor networks: From lined-based to curve-based deployment , 2013, 2013 Proceedings IEEE INFOCOM.

[3]  Walid Osamy,et al.  Minimum perimeter coverage of query regions in a heterogeneous wireless sensor network , 2011, Inf. Sci..

[4]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[5]  Dharma P. Agrawal,et al.  Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution , 2008, IEEE Transactions on Mobile Computing.

[6]  Eduardo G. Carrano,et al.  A Hybrid Multiobjective Evolutionary Approach for Improving the Performance of Wireless Sensor Networks , 2011, IEEE Sensors Journal.

[7]  Abdul Samad Ismail,et al.  Solving Target Coverage Problem Using Cover Sets in Wireless Sensor Networks Based on Learning Automata , 2014, Wirel. Pers. Commun..

[8]  Yong-Hyuk Kim,et al.  An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks , 2013, IEEE Transactions on Cybernetics.

[9]  Siba K. Udgata,et al.  Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[10]  Stefano Chessa,et al.  Efficient detection of composite events in Wireless Sensor Networks: Design and evaluation , 2011, 2011 IEEE Symposium on Computers and Communications (ISCC).