Mitigating Dead Node Impact on Coverage and Connectivity in Wireless Sensor Networks Using a Hybrid Approach

  • Omeed K. Khorsheed Department of Computer Science, Faculty of Science, Koya University, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region – F.R. Iraq https://orcid.org/0000-0003-0789-4150
Keywords: ABC algorithm, Coverage optimization, Dead nodes, Hybrid approach, Re-connectivity, SRCA algorithmm, Wireless sensor networks

Abstract

Wireless sensor networks’ coverage and efficient connectivity are pivotal for reliable data collection and communication. However, dead nodes, resulting from hardware failure or power depletion, can affect coverage and connectivity, leading to information loss and degraded performance. Previous research in the same context indicates the need for further investigation to achieve optimal trade-offs in network resource allocation. This research introduces a hybrid Artificial Bee Colony-Sequential Re-connectivity and Coverage Algorithm (ABC-SRCA) approach, combining the ABC algorithm with a developed SRCA. The ABC algorithm adjusts sensor node placement to maximize the coverage and minimize holes, while the SRCA algorithm restores connectivity by reconnecting the network when nodes fail. The approach uses probabilistic selection to explore various solutions, making the approach adaptive to diverse scenarios. The simulation outcomes indicate that the ABC-SRCA method enhances coverage accuracy by up to 30% compared to ABC and SRCA when they are used separately. In addition, the rate of connectivity error detection decreases by about 25%, highlighting the method’s effectiveness in dynamic network conditions. The approach also surpasses existing methods, including Genetic Algorithms and Sensing Radius Adaptive Coverage Control (SRACC), by achieving coverage level up to 98% while conserving resources. The ABC-SRCA achieves better energy consumption than Particle Swarm Optimization (PSO) and PSO Voronoi Diagram and achieves competent energy when compared with SRACC. The hybrid approach provides an effective solution for ensuring efficient and reliable network operations, supporting the successful deployment of WSNs in diverse applications.

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Author Biography

Omeed K. Khorsheed, Department of Computer Science, Faculty of Science, Koya University, Danielle Mitterrand Boulevard, Koya KOY45, Kurdistan Region – F.R. Iraq

Omeed K. Khorsheed is a Lecturer at the Department of Computer Science, Faculty of Science, Koya University. He holds a B.Sc. degree in Computer Science from Mustansiriyah University, a Higher Diploma in Computer Science from the Informatics Institute for Postgraduate Studies at the University of Technology, Iraq, and an M.Sc. degree in Computer Information Systems (CIS) from the College of Information Technology, Arab Academy for Banking and Financial Sciences, Amman, Jordan. His research interests include wireless sensor networks—particularly in the areas of localization, coverage, connectivity, sensor node deployment, target tracking, network lifetime, and energy consumption—as well as image processing, with a focus on image denoising and filtering.

References

Abdulzahra, A.M.K., Al-Qurabat, A.K.M., and Abdulzahra, S.A., 2023. Optimizing energy consumption in WSN-based IoT using unequal clustering and sleep scheduling methods. Internet of Things, 22, p.100765. DOI: https://doi.org/10.1016/j.iot.2023.100765

Adday, G.H., Subramaniam, S.K., Zukarnain, Z.A., and Samian, N., 2022. Fault tolerance structures in wireless sensor networks (WSNs): Survey, classification, and future directions. Sensors (Basel), 22(16), p.6041. DOI: https://doi.org/10.3390/s22166041

Adu-Manu, K.S., Engmann, F., Sarfo-Kantanka, G., Baiden, G.E., and Dulemordzi, B.A., 2022. WSN protocols and security challenges for environmental monitoring applications: A survey. Journal of Sensors, 2022(1), p.1628537. DOI: https://doi.org/10.1155/2022/1628537

Al-Fuhaidi, B., Mohsen, A.M., Ghazi, A., and Yousef, W.M., 2020. An efficient deployment model for maximizing coverage of heterogeneous wireless sensor network based on harmony search algorithm. Journal of Sensors, 2020(1), p.8818826. DOI: https://doi.org/10.1155/2020/8818826

Aljubori, M.H.H., Khalilpour Akram, V., and Challenger, M., 2022. Improving the deployment of WSNs by localized detection of covered redundant nodes in industry 4.0 applications. Sensors (Basel), 22(3), p.942. DOI: https://doi.org/10.3390/s22030942

Amer, D.A., Soliman, S.A., Hassan, A.F., and Zamel, A.A., 2024. Enhancing connectivity and coverage in wireless sensor networks: Ahybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement. Neural Computing and Applications, 36(34), pp.21671-21702. DOI: https://doi.org/10.1007/s00521-024-10315-x

Baradaran, A.A., and Navi, K., 2020. HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks. Fuzzy Sets and Systems, 389, pp.114-144. DOI: https://doi.org/10.1016/j.fss.2019.11.015

Bhat, S.J., and Santhosh, K.V., 2022. A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm. Peer-to-Peer Networking and Applications, 15(3), pp.1473-1485. DOI: https://doi.org/10.1007/s12083-022-01302-x

Das, S., and Debbarma, M.K., 2023. CHPT: An improved coverage-hole patching technique based on tree-center in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 14, pp.5873-5884. DOI: https://doi.org/10.1007/s12652-020-02038-3

Guo, J., and Jafarkhani, H., 2019. Movement-efficient sensor deployment in wireless sensor networks with limited communication range. IEEE Transactions on Wireless Communications, 18(7), pp.3469-3484. DOI: https://doi.org/10.1109/TWC.2019.2914199

Guo, J., Sun, Y., Liu, T., Li, Y., and Fei, T., 2025. An optimization coverage strategy for wireless sensor network nodes based on path loss and false alarm probability. Sensors (Basel), 25(2), p.396. DOI: https://doi.org/10.3390/s25020396

Gutiérrez, S., and Ponce, H., 2019. An intelligent failure detection on a wireless sensor network for indoor climate conditions. Sensors (Basel), 19(4), p.854. DOI: https://doi.org/10.3390/s19040854

Jain, J.K., 2020. A coherent approach for dynamic cluster-based routing and coverage hole detection and recovery in bi-layered WSN-IoT. Wireless Personal Communications, 114(1), pp.519-543. DOI: https://doi.org/10.1007/s11277-020-07377-0

Khatir, S., Tiachacht, S., Le Thanh, C., Ghandourah, E., Mirjalili, S., and Wahab, M.A., 2021. An improved artificial neural network using arithmetic optimization algorithm for damage assessment in FGM composite plates. Composite Structures, 273, p.114287. DOI: https://doi.org/10.1016/j.compstruct.2021.114287

Khedr, A.M., Osamy, W., and Salim, A., 2018. Distributed coverage hole detection and recovery scheme for heterogeneous wireless sensor networks. Computer Communications, 124, pp.61-75. DOI: https://doi.org/10.1016/j.comcom.2018.04.002

Kuthadi, V.M., Selvaraj, R., Baskar, S., Shakeel, P.M., and Ranjan, A., 2022. Optimized energy management model on data distributing framework of wireless sensor network in IoT system. Wireless Personal Communications, 127(2), pp.1377-1403. DOI: https://doi.org/10.1007/s11277-021-08583-0

Lai, Y.H., Cheong, S.H., Zhang, H., and Si, Y.W., 2022. Coverage hole detection in WSN with force-directed algorithm and transfer learning. Applied Intelligence, 52(5), pp.5435-5456. DOI: https://doi.org/10.1007/s10489-021-02714-7

Ling, H., Zhu, T., He, W., Luo, H., Wang, Q., and Jiang, Y., 2020. Coverage optimization of sensors under multiple constraints using the improved PSO algorithm. Mathematical Problems in Engineering, 2020(1), p.8820907. DOI: https://doi.org/10.1155/2020/8820907

Lu, X., Wei, Y., Wu, Q., Yang, C., Li, D., Zhang, L., and Zhou, Y., 2022. Acoverage hole patching algorithm for heterogeneous wireless sensor networks. Electronics, 11(21), p.3563. DOI: https://doi.org/10.3390/electronics11213563

Lu, Z., Wang, C., Wang, P., and Xu, W., 2025. 3D deployment optimization of wireless sensor networks for heterogeneous functional nodes. Sensors (Basel), 25(5), p.1366. DOI: https://doi.org/10.3390/s25051366

Satyanarayana, P., Mahalakshmi, T., Sivakami, R., Alahmari, S.A., Rajeyyagari, S., and Asadi, S. 2023. A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks. Materials Today: Proceedings, 80, pp.1717-1722. DOI: https://doi.org/10.1016/j.matpr.2021.05.355

Siamantas, G., and Kandris, D., 2024. Particle swarm optimization for k-coverage and 1-connectivity in wireless sensor networks. Electronics, 13(23), p.4841. DOI: https://doi.org/10.3390/electronics13234841

Velavalapalli, V.S., Ramamurthy, A., and Satyanarayana, G.M., 2024. Detection and correction of node failures in wireless sensor networks. International Journal of Gender, Science and Technology, 13(2), pp.1-6.

Wang, J., Ju, C., Gao, Y., Sangaiah, A.K., and Kim, G.J., 2018. A PSO based energy efficient coverage control algorithm for wireless sensor networks. Computers, Materials and Continua, 56(3), pp.433-446.

Wang, S., Wang, Y., Li, D., and Zhao, Q., 2023. Distributed relative localization algorithms for multi-robot networks: A survey. Sensors (Basel), 23(5), p.2399. DOI: https://doi.org/10.3390/s23052399

Wang, Z., Tian, L., Wu, W., Lin, L., Li, Z., and Tong, Y., 2022. A metaheuristic algorithm for coverage enhancement of wireless sensor networks. Wireless Communications and Mobile Computing, 2022(1), p.7732989. DOI: https://doi.org/10.1155/2022/7732989

Yan, F., Ma, W., Shen, F., Xia, W., and Shen, L., 2020. Connectivity based k-coverage hole detection in wireless sensor networks. Mobile Networks and Applications, 25, pp.783-793. DOI: https://doi.org/10.1007/s11036-019-01301-y

Yue, Y., Cao, L., and Luo, Z., 2019. Hybrid artificial bee colony algorithm for improving the coverage and connectivity of wireless sensor networks. Wireless Personal Communications, 108, pp.1719-1732. DOI: https://doi.org/10.1007/s11277-019-06492-x

Zeng, C., Qin, T., Tan, W., Lin, C., Zhu, Z., Yang, J., and Yuan, S., 2023. Coverage optimization of heterogeneous wireless sensor network based on improved wild horse optimizer. Biomimetics (Basel), 8(1), p.70. DOI: https://doi.org/10.3390/biomimetics8010070

Zhang, D.G., Chen, L., Zhang, J., Chen, J., Zhang, T., Tang, Y.M., and Qiu, J.N., 2020a. A multi-path routing protocol based on link lifetime and energy consumption prediction for mobile edge computing. IEEE Access, 8, pp.69058-69071. DOI: https://doi.org/10.1109/ACCESS.2020.2986078

Zhang, J., Chu, H., and Feng, X., 2020b. Efficient coverage hole detection algorithm based on the simplified rips complex in wireless sensor networks. Journal of Sensors, 2020(1), p.3236970. DOI: https://doi.org/10.1155/2020/3236970

Published
2025-04-17
How to Cite
Khorsheed, O. K. (2025) “Mitigating Dead Node Impact on Coverage and Connectivity in Wireless Sensor Networks Using a Hybrid Approach”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 13(1), pp. 131-143. doi: 10.14500/aro.11710.