A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs
- 404 Downloads
Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs.
KeywordsWireless Sensor Network Particle Swarm Optimization (PSO) Coverage Sensor Deployment
This research work is supported by the NSFC (61772454), and by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education. It is also supported by Industrial Core Technology Development Program (10049079, Development of Mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE), Korea. Prof. Hye-jin Kim is the corresponding author.
- 13.Rakavi, A., Manikandan, M.S.K., Hariharan, K.: Grid based mobile sensor node deployment for improving area coverage in wireless sensor networks. In: 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1–5. IEEE (2015)Google Scholar
- 14.Li, F., Xiong, S., Wang, L.: Recovering coverage holes by using mobile sensors in wireless sensor networks. In: 2011 Seventh International Conference on Computational Intelligence and Security (CIS), pp. 746–749. IEEE (2011)Google Scholar
- 15.Abolhasan, M., Maali, Y., Rafiei, A., et al.: Distributed hybrid coverage hole recovery in wireless sensor networks. IEEE Sens. J. 16(23), 8640–8648 (2016)Google Scholar
- 16.Nguyen, D.T., Nguyen, N.P., Thai, M.T. et al.: An optimal algorithm for coverage hole healing in hybrid sensor networks. In: 201 7th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 494–499. IEEE (2011)Google Scholar
- 20.Derdour, Y., Kechar, B., Khelfi, M.F.: Using mobile data collectors to enhance energy efficiency and reliability in delay tolerant wireless sensor networks. J. Inf. Process. Syst. 12(2), 275–294 (2016)Google Scholar
- 21.Gupta, G.P., Misra, M., Garg, K.: An energy efficient distributed approach-based agent migration scheme for data aggregation in wireless sensor networks. J. Inf. Process. Syst. 11(1), 148–164 (2015)Google Scholar
- 24.Xiao, F., Sha, L.T., Yuan, Z.P. et al.: VulHunter: A Discovery for unknown Bugs based on Analysis for known patches in industry internet of things. IEEE Trans. Emerg. Top. Comput. https://doi.org/10.1109/TETC.2017.2754103 (Published online, 2017, 1–13)