The International Arab Journal of Information Technology (IAJIT)


Solving Point Coverage Problem in Wireless Sensor Networks Using Whale Optimization Algorithm

Todays, dynamic power management methods that decrease the energy use of sensor networks after their design and deployment are of paramount importance. In Wireless Sensor Networks (WSN), coverage and detection quality are one aspect of service quality and power consumption reduction aspect. The aim of the coverage problem is to monitor at least one node at each point in the targeted area and is divided into three categories: border, area, and point coverage. In point coverage, which is our interest, the problem is to cover specific points of the environment scattered on the surface of the environment; their position is decided on and called the goal. In this paper, a new metaheuristic algorithm based on Whale Optimization Algorithm (WOA) is proposed. The proposed algorithm tries to find the Best Solution (BS) based on three operations exploration, spiral attack, and siege attack. Several scenarios, including medium, hard and complex problems, are designed to evaluate the proposed technique, and it is compared to Genetic Algorithm (GA) and Ant Colony Optimization (ACO) based on time complexity criteria in providing a suitable coverage, network lifetime, energy consumption. The simulation results show that the proposed algorithm performs better than the compared ones in most scenarios.

[1] Bozorgi S., Rostami A., Hosseinabadi A., and Balas V., “A New Clustering Protocol for Energy Harvesting-Wireless Sensor Networks,” Computers and Electrical Engineering, vol. 64, pp. 233-247, 2017. 837 Solving Point Coverage Problem in Wireless Sensor Networks Using Whale ...

[2] Han T., Bozorgi S., Orang A., Hosseinabadi A., Sangaiah A., and Chen M., “A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes,” Sustainability, vol. 11, no. 3, pp. 1-26, 2019.

[3] Harizan S. and Kuila P., “Coverage and Connectivity Aware Energy Efficient Scheduling in Target Based Wireless Sensor Networks: an Improved Genetic Algorithm-Based Approach,” Wireless Networks, vol. 25, pp. 1995-2011, 2019.

[4] Lee J. and Lee J., “Ant-Colony-Based Scheduling Algorithm for Energy-Efficient Coverage of WSN,” IEEE Sensors Journal, vol. 12, no. 10, pp. 3036-3046, 2012.

[5] Mirjalili S. and Lewis A., “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51-67, 2016.

[6] Nguyen T., Phan T., Nguyen H., Aimtongkham P., and So-In C., “An Efficient Distributed Algorithm for Target-Coverage Preservation in Wireless Sensor Networks,” Peer-to-Peer Networking and Applications, vol. 14, pp. 453- 466, 2021.

[7] Rostami A., Badkoobe M., Mohanna F., Hosseinabadi A., and Balas V., “Imperialist Competition based Clustering Algorithm to Improve the Lifetime of Wireless Sensor Network,” in Proceedings of 7th International Workshop Soft Computing Applications, Arad, pp. 189-202, 2016.

[8] Rostami A., Badkoobe M., Mohanna F., keshavarz H., Hosseinabadi A., and Sangaiah A., “Survey on Clustering in Heterogeneous and Homogeneous Wireless Sensor Networks,” The Journal of Supercomputing, vol. 74, pp. 277-323, 2018.

[9] Rostami A., Bernety H., and Hosseinabadi A., “A Novel and Optimized Algorithm to Select Monitoring Sensors by GSA,” in Proceedings of 2nd International Conference on Control, Instrumentation and Automation, Shiraz, pp. 829-834, 2011.

[10] Sangaiah A., Bian G., Bozorgi S., Suraki M., Hosseinabadi A., and Shareh M., “A Novel Quality of Service Aware Web Services Composition using Biogeography-based Optimization Algorithm,” Soft Computing, vol. 24, pp. 8125-8137, 2020.

[11] Saemi B., Hosseinabadi A., Kardgar M., Balas V., and Ebadi H., “Nature Inspired Partitioning Clustering Algorithms: A Review and Analysis,” in Proceedings of 7th International Workshop Soft Computing Applications, Arad, pp. 96-116, 2016.

[12] Sangaiah A., Sadeghilalimi M., Hosseinabadi A., and Zhang W., “Energy Consumption in Point- Coverage Wireless Sensor Networks via Bat Algorithm,” IEEE Access, vol. 7, pp. 180258- 180269, 2019.

[13] Samara G. and Aljaidi M., “Aware-Routing Protocol using Best First Search Algorithm in Wireless Sensor,” The International Arab Journal of Information Technology, vol. 15, no. 34, pp. 592-598, 2018.

[14] Tavakkolai H., Yadollahi N., Yadollahi M., Hosseinabadi A., Rezaei P., and Kardgar M., “Sensor Selection Wireless Multimedia Sensor Network using Gravitational Search Algorithm,” Indian Journal of Science and Technology, vol. 8, no. 14, pp. 1-6, 2015.

[15] Tian D. and Georganas N., “A Node Scheduling Scheme for Energy Conservation in Large Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 3, no. 2, pp. 271-290, 2003.

[16] Wu H., Li Q., Zhu H., Han X., Li Y., and Yang B., “Directional Sensor Placement in Vegetable Greenhouse for Maximizing Target Coverage Without Occlusion,” Wireless Networks, vol. 26, pp. 4677-4687, 2020.

[17] Wang X., Xing G., Zhang Y., Lu C., Pless R., and Gill C., “Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks,” in Proceedings of ACM Conference on Embedded Networked Sensor System, New York, pp. 28-39, 2003.

[18] Wang W., Sinivasan V., Chua K., and Wang B., “Energy-efficient Coverage for Target Detection in Wireless Sensor Networks,” in Proceedings of 6th International Symposium on Information Processing in Sensor Networks, Cambridge, pp. 313-322, 2007.

[19] Xu Y. and Yao X., “A GA Approach to the Optimal Placement of Sensors in Wireless Sensor Networks with Obstacles and Preferences,” in Proceedings of 3rd IEEE Consumer Communications and Networking Conference, Las Vegas, pp. 127-131, 2006.

[20] Yarinezhad R. and Hashemi S., “A Sensor Deployment Approach for Target Coverage Problem in Wireless Sensor Networks,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-16, 2020.

[21] Zameni M., Rezaei A., and Farzinvash L., “Two- Phase Node Deployment for Target Coverage in Rechargeable WSNs Using Genetic Algorithm and Integer Linear Programming,” The Journal of Supercomputing, vol. 77, no. 12, pp. 4172- 4200, 2021. 838 The International Arab Journal of Information Technology, Vol. 18, No. 6, November 2021 Mahnaz Toloueiashtian received the M.Sc. in Software Engineering, from Islamic Azad University Ayatollah Amoli, Iran, 2016. She is currently a Ph.D. Candidate in Computer Engineering from the Islamic Azad University, Babol Branch, Babol, Iran. She researches interests are in WSN, Cloud Computing, and Image Processing. Mehdi Golsorkhtabaramiri received his M.Sc. in Computer Systems Architecture Engineering. He received his Ph.D. in Computer Engineering from the Science and Research Branch, Islamic Azad University, Tehran, Iran. From 2011 until now, he has been working as a faculty member at the Islamic Azad University, Babol Branch, Iran. His current research interests include Radio Frequency Identification (RFID) systems, Wireless Sensor Network (WSN), and Wireless Communications. Seyed Yaser Bozorgi Rad was born in Iran in 1980. He graduated in PhD of Computer Science from University Teknology Malaysia in 2012. His degree in Master of Science is in Information Technology from UTM as well. Software Projects management, Advanced Networks Security, Computing Solutions and Distributed Systems are some of his interested research areas. Moreover, He has been a faculty member of IAU Babol branch since 2014.