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Optimizing Connected Target Coverage in Wireless Sensor Networks Using Self-Adaptive Differential Evolution

Wireless Sensor Networks (WSNs) are advanced communication technologies with many real-world applications such as monitoring of personal health, military surveillance, and forest wildfire; and tracking of moving objects. Coverage optimization and network connectivity are critical design issues for many WSNs. In this study, the connected target coverage optimization in WSNs is addressed and it is solved using the self-adaptive differential evolution algorithm (SADE) for the first time in literature. A simulation environment is set up to measure the performance of SADE for solving this problem. Based on the experimental settings employed, the numerical results show that SADE is highly successful for dealing with the connected target coverage problem and can produce a higher performance in comparison with other widely-used metaheuristic algorithms such as classical DE, ABC, and PSO.

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[1] A. Milenković, C. Otto, and E. Jovanov, “Wireless sensor networks for personal health monitoring: Issues and an implementation,” Computer Communications, vol. 29, no. 13–14, pp. 2521–2533, Aug. 2006.
[2] L. Lamont, M. Toulgoat, M. Deziel, and G. Patterson, “Tiered wireless sensor network architecture for military surveillance applications,” in The Fifth International Conference on Sensor Technologies and Applications, SENSORCOMM, 2011, pp. 288–294.
[3] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A Sybil attack detection scheme for a forest wildfire monitoring application,” Future Generation Computer Systems, vol. 80, pp. 613–626, Mar. 2018.
[4] W. Yi et al., “A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems,” Sensors, vol. 15, no. 12, pp. 31392–31427, Dec. 2015.
[5] Chih-Yu Lin, Wen-Chih Peng, and Yu-Chee Tseng, “Efficient in-network moving object tracking in wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 5, no. 8, pp. 1044–1056, Aug. 2006.
[6] S. Abdollahzadeh and N. J. Navimipour, “Deployment strategies in the wireless sensor network: A comprehensive review,” Computer Communications, vol. 91–92, pp. 1–16, Oct. 2016.
[7] I. Khoufi, P. Minet, A. Laouiti, and S. Mahfoudh, “Survey of deployment algorithms in wireless sensor networks: coverage and connectivity issues and challenges,” International Journal of Autonomous and Adaptive Communications Systems, vol. 10, no. 4, pp. 341–390, 2017.
[8] Yourim Yoon and Yong-Hyuk Kim, “An Efficient GeneticAlgorithm forMaximum Coverage Deployment in Wireless Sensor Networks,” IEEE Transactions on Cybernetics, vol. 43, no. 5, pp. 1473– 1483, Oct. 2013.
[9] T. E. Kalayci and A. Uğur, “Genetic Algorithm-Based Sensor Deployment with Area Priority,” Cybernetics and Systems, vol. 42[1] T. E, no. 8, pp. 605–620, Nov. 2011.
[10] S. Mnasri, A. Thaljaoui, N. Nasri, and T. Val, “A genetic algorithm-based approach to optimize the coverage and the localization in the wireless audio-sensors networks,” in 2015 International Symposium on Networks, Computers and Communications (ISNCC), 2015, pp. 1–6.
[11] S. K. Gupta, P. Kuila, and P. K. Jana, “Genetic algorithm approach for k -coverage and m -connected node placement in target based wireless sensor networks,” Computers & Electrical Engineering, vol. 56, pp. 544–556, Nov. 2016.
[12] X. Wang, S. Wang, and D. Bi, “Virtual Force-Directed Particle Swarm Optimization for Dynamic Deployment in Wireless Sensor Networks,” in Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 292– 303.
[13] Q. Ni, H. Du, Q. Pan, C. Cao, and Y. Zhai, “An improved dynamic deployment method for wireless sensor network based on multi-swarm particle swarm optimization,” Natural Computing, vol. 16, no. 1, pp. 5–13, Mar. 2017.
[14] X. Wang, S. Wang, J.-J. Ma, X. Wang, S. Wang, and J.-J. Ma, “An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment,” Sensors, vol. 7, no. 3, pp. 354–370, Mar. 2007.
[15] C. Ozturk, D. Karaboga, and B. Gorkemli, “Artificial bee colony algorithm for dynamic deployment of wireless sensor networks,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 20, no. 2, pp. 255–262, 2012.
[16] S. Kundu, S. Das, A. V. Vasilakos, and S. Biswas, “A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks,” Soft Computing, vol. 19, no. 3, pp. 637–659, Mar. 2015.
[17] N. Qin and J. Chen, “An area coverage algorithm for wireless sensor networks based on differential evolution,” International Journal of Distributed Sensor Networks, vol. 14, no. 8, p. 155014771879673, Aug. 2018.
[18] W.-H. Liao, Y. Kao, and R.-T. Wu, “Ant colony optimization based sensor deployment protocol for wireless sensor networks,” Expert Systems with Applications, vol. 38, no. 6, pp. 6599–6605, Jun. 2011.
[19] A. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124–141, Jul. 1999.
[20] A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, Apr. 2009.
[21] R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” J. Global Optim., vol. 11, no. 4, pp. 341–359, 1997.
[22] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” J. Global Optim., vol. 39, no. 3, pp. 459–471, Oct. 2007.
[23] M. Zambrano-Bigiarini, M. Clerc, and R. Rojas, “Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements,” in
2013 IEEE Congress on Evolutionary Computation, 2013, pp. 2337–2344.
[24] J. Zhang and A. C. Sanderson, “JADE: Self-adaptive differential evolution with fast and reliable convergence performance,” in 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 2251–2258.
[25] R. Tanabe and A. Fukunaga, “Success-history based parameter adaptation for Differential Evolution,” in 2013 IEEE Congress on Evolutionary Computation, CEC 2013, 2013, pp. 71–78.