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A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS

Year 2025, Volume: 24 Issue: 47, 318 - 348, 30.06.2025

Gözde Bayat , Eyüp Emre Ülkü , Buket Doğan

https://doi.org/10.55071/ticaretfbd.1514389

Abstract

In recent years, Wireless Sensor Networks (WSNs) have gained attention due to their real-time monitoring capabilities. These networks use low-power devices to collect and transmit data, becoming significant with the rise of 5G and the Internet of Things (IoT). Initially used for military purposes, WSNs have expanded into various sectors, particularly in smart agriculture, where they enhance efficiency through modern technology. By providing real-time data, WSNs help farmers optimize yields, reduce waste, and improve productivity, supporting the digital transformation of agriculture. Despite their advantages, WSNs face challenges such as routing, localization, energy efficiency, and coverage. This study provides a comprehensive survey of the coverage optimization problem in WSNs, focusing on meta-heuristic algorithms such as the Gray Wolf, Whale Swarm, Flower Pollination, and Cuckoo Algorithms. These algorithms are analyzed based on metrics like maximum coverage rate, energy consumption, and solution time. The survey highlights their potential to address challenges in WSN applications, particularly in agriculture and other domains, by optimizing sensor placement and improving network efficiency.

Keywords

Target coverage optimization Wireless sensor networks Meta-Heuristic optimization algorithms

Project Number

FYL-2023-10950

References

  • Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40, 102–114.
  • Alia, O. M., & Al-Ajouri, A. (2017). Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sensors Journal, 17 (3), 882-896. (St 1)
  • Arivudainambi, D., Balaji, S., & Poorani, T. S. (2017). Sensor deployment for target coverage in underwater wireless sensor network. In Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN) (1-6). (St 2)
  • Aziz, N. A. B. A., Mohemmed, A. W., & Alias, M. Y. (2009). A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In 2009 International Conference on Networking, Sensing and Control (602-607). Okayama, Japan. (St 3)
  • Biswas, S., Das, R., & Chatterjee, P. (2018). Energy-efficient connected target coverage in multi-hop wireless sensor networks. In Bhattacharyya, S., Sen, S., Dutta, M., Biswas, P., Chattopadhyay, H. (Eds.) Industry Interactive Innovations in Science, Engineering and Technology Lecture Notes in Networks and Systems (11). Springer, Singapore. (St 4)
  • Chen, D. R., Chen, L. C., Chen, M. Y., & Hsu, M. Y. (2019). A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks. Computer Communications, 137, 15-31. (St 5)
  • Cheng, J., Fang, Y., & Jiang, N. (2023). Research on wireless sensor networks coverage based on fruit fly optimization algorithm. In 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 1109-1115. (St 6)
  • Chowdhury, A., & De, D. (2020). FIS-RGSO: Dynamic fuzzy inference system based reverse glowworm swarm optimization of energy and coverage in green mobile wireless sensor networks. Computer Communications, 163, 12–34. (St 7)
  • Chowdhury, A., & De, D. (2021). Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-glowworm swarm optimization-K-means algorithm. Ad Hoc Networks, 122, 102660. (St 8)
  • Deepa, A., & Venkataraman, R. (2021). Enhancing whale optimization algorithm with Levy flight for coverage optimization in wireless sensor networks. Computers & Electrical Engineering, 94, 107359. (St 9)
  • Deng, H., Liu, L., Fang, J., Qu, B. & Huang, Q. (2023). A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm. Mathematics and Computers in Simulation, 205, 794–817.
  • Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.
  • Fang, Q., & Sun, Y. R. (2017). Wireless sensor network coverage efficiency optimization simulation. Computer Simulation, 34(8), 297-301. (St 10)
  • Gu, W. (2020). An improved whale optimization algorithm with cultural mechanism for high-dimensional global optimization problems. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (1282-1286). Chongqing, China.
  • Gunjan. (2023). A review on multi-objective optimization in wireless sensor networks using nature inspired meta-heuristic algorithms. Neural Processing Letters, 55, 2587–2611.
  • Guo, Y., Liu, D., Chen, M., & Liu, Y. (2013). An energy-efficient coverage optimization method for wireless sensor networks based on multi-objective quantum-inspired cultural algorithm. In Proceedings of the ISNN’13: Proceedings of the 10th international conference on Advances in Neural Networks (349). Springer: Berlin/Heidelberg, Germany. (St 11)
  • Hanh, N. T., Hanh, P. T. H., Binh, H. T. T., & Nghia, N. D. (2016). Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks. In 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) (235-240). Hsinchu, Taiwan.
  • Jia, J., Chen, J., Chang, G., Wen, Y., & Song, J. (2009). Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Computers & Mathematics with Applications, 59, 1767–1775. (St 12)
  • Jiao, W., Tang, R., & Xu, Y. (2022). A coverage optimization algorithm for the wireless sensor network with random deployment by using an improved flower pollination algorithm. Forests, 13, 1690. (St 13)
  • Kapoor, R., & Sharma, S. (2023). Glowworm swarm optimization (GSO) based energy efficient clustered target coverage routing in wireless sensor networks (WSNs). International Journal of System Assurance Engineering and Management, 14(Suppl 2), 622–634. (St 14)
  • Keshmiri, H., & Bakhshi, H. (2020). A new 2-phase optimization-based guaranteed connected target coverage for wireless sensor networks. IEEE Sensors Journal, 20(13), 7472-7486. (St 15)
  • Khalaf, O. I., Abdulsahib, G. M., & Sabbar, B. M. (2020). Optimization of wireless sensor network coverage using the bee algorithm. Journal of Information Science and Engineering, 36, 377–386. (St 16)
  • Lee, J. W., Choi, B. S., & Lee, J. J. (2017). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7, 419-427. (St 17)
  • Li, J., Cui, L., & Zhang, B. (2010). Self-deployment by distance and orientation control for mobile sensor networks. In International Conference on Networking, Sensing and Control (ICNSC) (549–553). IEEE.
  • Li, D. (2022). Research on coverage holes repair in wireless sensor networks based on an improved artificial fish swarm algorithm. International Journal of Autonomous and Adaptive Communications Systems, 15(4), 312–330. https://doi.org/10.1504/ijaacs.2022.127412
  • Liang, D., Shen, H., & Chen, L. (2021). Maximum target coverage problem in mobile wireless sensor networks. Sensors, 21, 184.
  • Liang, J., Tian, M., Liu, Y., et al. (2022). Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Scientific Reports, 12, 11687. (St 18)
  • Ling, H., Zhu, T., He, W., Luo, H., Wang, Q., & Jiang, Y. (2020). Coverage optimization of sensors under multiple constraints using the improved PSO algorithm. Mathematical Problems in Engineering, 2020, Article ID 8820907, 10 pages. (St 19)
  • Masoodi, I. S., Dar, M. A., & Banday, M. T. (2018). Energy efficient routing in WSNs based on dynamic cuckoo search algorithm. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (2514-2517). Bangalore, India. (St 20)
  • Narawade, V. E., & Kolekar, U. D. (2017). EACSRO: Epsilon constraint-based adaptive cuckoo search algorithm for rate optimized congestion avoidance and control in wireless sensor networks. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (715-720). Palladam, India. (St 21)
  • Ozdag, R., & Canayaz, M. (2021). A new metaheuristic approach based on orbit in the multi-objective optimization of wireless sensor networks. Wireless Networks, 27, 1-21.
  • Pitchaimanickam, B., & Murugaboopathi, G. (2020). A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Computing and Applications, 32, 7709–7723. (St 22)
  • Qadir, J., Ulaih, U., de Abajo, B. S., & Zapirain, B. G. (2020). Energy-aware and reliability based localization-free cooperative acoustic wireless sensor networks. IEEE Access, 8, 121366–121384.
  • Qi-wei, Z. (2009). Coverage optimization of wireless sensor network. Mechanical & Electrical Engineering Magazine.
  • Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. Journal of Supercomputing, 68(1), 1–48.
  • Roselin, I., & Latha, P. (2016). Energy efficient coverage using artificial bee colony optimization in wireless sensor networks. Journal of Scientific & Industrial Research, 75, 19–27. (St 23)
  • Sampathkumar, A., Mulerikkal, J., & Sivaram, M. (2020). Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wireless Networks, 26, 4227–4238. (St 24)
  • Sharma, N., & Gupta, V. (2022). A survey on applications, challenges, and meta-heuristic-based solutions in wireless sensor network. In EAI/Springer Innovations in Communication and Computing. Springer, Cham.
  • Singh, A., Sharma, S., & Singh, J. (2021). Nature-inspired algorithms for wireless sensor networks: A comprehensive survey. Computer Science Review, 39, 100342. (St 25)
  • Sun, W., Tang, M., Zhang, L., Huo, Z., & Shu, L. (2020). A survey of using swarm intelligence algorithms in IoT. Sensors, 20(5), 1420. (St 26)
  • Thirugnanasambandam, K., Raghav, R. S., Anguraj, D. K., et al. (2021). Multi-objective binary reinforced cuckoo search algorithm for solving connected coverage target based WSN with critical targets. Wireless Personal Communications, 121, 2301–2325. (St 27)
  • Tian, M., Bai, J., Li, J., Huang, M., Zhan, C., & Ren, L. (2021). Elite parallel cuckoo search algorithm for regional coverage control problem in high-density wireless sensor networks. In 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) (285-289). Manchester, United Kingdom. (St 28)
  • Wang, H., Li, K., & Pedrycz, W. (2020). An elite hybrid metaheuristic optimization algorithm for maximizing wireless sensor networks lifetime with a sink node. IEEE Sensors Journal, 20(10), 5634-5649. (St 29)
  • Wang, J., Zhu, D., Ding, Z., & Gong, Y. (2023). WSN coverage optimization based on improved sparrow search algorithm. In 2023 15th International Conference on Advanced Computational Intelligence (ICACI) (pp. 1-8). Seoul, Korea, Republic of. (St 30)
  • Wang, L., Li, C., Wang, H., Zhang, Y., & Liu, Z. (2020). MEP-PSO algorithm-based coverage optimization in directional sensor networks. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference (pp. 1-6). Taipei, Taiwan. (St 31)
  • Wang, L., Wu, W., Qi, J., & Jia, Z. (2018). Wireless sensor network coverage optimization based on whale group algorithm. Computer Science and Information Systems, 15, 569–583. (St 32)
  • Zhang, Y., Cao, L., Yue, Y., Cai, Y., & Hang, B. (2021). A novel coverage optimization strategy based on grey wolf algorithm optimized by simulated annealing for wireless sensor networks. Computational Intelligence and Neuroscience, 6688408. (St 33)
  • Zhao, Q., Li, C., Zhu, D., & Xie, C. (2022). Coverage optimization of wireless sensor networks using combinations of PSO and chaos optimization. Electronics, 11(6), 853. (St 34)
  • Zhu, F., & Wang, W. (2021). A coverage optimization method for WSNs based on the improved weed algorithm. Sensors, 21. (St 35)
  • Zhuang, Y., Wu, C., & Zhang, Y. (2017). A coverage hole recovery algorithm for wireless sensor networks based on cuckoo search. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) (1552-1557). Honolulu, HI, USA. (St 36)

MOBİL KABLOSUZ SENSÖR AĞLARINDA HEDEF KAPSAMA İÇİN YENİ NESİL OPTİMİZASYON ALGORİTMALARINA YÖNELİK KAPSAMLI BİR DERLEME

Year 2025, Volume: 24 Issue: 47, 318 - 348, 30.06.2025

Gözde Bayat , Eyüp Emre Ülkü , Buket Doğan

https://doi.org/10.55071/ticaretfbd.1514389

Abstract

Son yıllarda Kablosuz Sensör Ağları (WSN), gerçek zamanlı izleme yetenekleri sayesinde dikkat çekmektedir. Bu ağlar, düşük güçlü cihazlar kullanarak veri toplar ve iletir, 5G ve Nesnelerin İnterneti (IoT) ile birlikte daha da önemli hale gelmiştir. İlk olarak askeri amaçlarla kullanılan WSN’ler, günümüzde özellikle akıllı tarımda modern teknolojilerle verimliliği artırmak için yaygın bir şekilde kullanılmaktadır. Gerçek zamanlı veriler sağlayarak çiftçilerin verimliliği optimize etmesine, atıkları azaltmasına ve üretkenliği artırmasına yardımcı olmakta ve tarımın dijital dönüşümünü desteklemektedir. Avantajlarına rağmen, WSN’ler yönlendirme, konumlandırma, enerji verimliliği ve kapsama gibi zorluklarla karşılaşmaktadır. Bu çalışma, WSN'lerdeki kapsama optimizasyon problemini ele alan kapsamlı bir derleme sunmaktadır. Gri Kurt, Balina Sürüsü, Çiçek Tozlaşma ve Guguk Kuşu Algoritmaları gibi meta-sezgisel algoritmalar, maksimum kapsama oranı, enerji tüketimi ve çözüm süresi gibi metrikler temelinde analiz edilmiştir. Çalışma, bu algoritmaların özellikle tarım ve diğer alanlardaki uygulamalarda karşılaşılan zorlukları ele almadaki potansiyelini, sensör yerleşimini optimize ederek ve ağ verimliliğini artırarak vurgulamaktadır.

Keywords

Hedef kapsama optimizasyon Kablosuz algılayıcı ağlar Meta-sezgisel optimizasyon algoritmaları

Supporting Institution

Marmara Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

FYL-2023-10950

Thanks

Marmara Üniversitesi Bilimsel Araştırma Projeleri Birimi'ne verdikleri değerli destekten dolayı teşekkür ederiz.

References

  • Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40, 102–114.
  • Alia, O. M., & Al-Ajouri, A. (2017). Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sensors Journal, 17 (3), 882-896. (St 1)
  • Arivudainambi, D., Balaji, S., & Poorani, T. S. (2017). Sensor deployment for target coverage in underwater wireless sensor network. In Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN) (1-6). (St 2)
  • Aziz, N. A. B. A., Mohemmed, A. W., & Alias, M. Y. (2009). A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram. In 2009 International Conference on Networking, Sensing and Control (602-607). Okayama, Japan. (St 3)
  • Biswas, S., Das, R., & Chatterjee, P. (2018). Energy-efficient connected target coverage in multi-hop wireless sensor networks. In Bhattacharyya, S., Sen, S., Dutta, M., Biswas, P., Chattopadhyay, H. (Eds.) Industry Interactive Innovations in Science, Engineering and Technology Lecture Notes in Networks and Systems (11). Springer, Singapore. (St 4)
  • Chen, D. R., Chen, L. C., Chen, M. Y., & Hsu, M. Y. (2019). A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks. Computer Communications, 137, 15-31. (St 5)
  • Cheng, J., Fang, Y., & Jiang, N. (2023). Research on wireless sensor networks coverage based on fruit fly optimization algorithm. In 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), 1109-1115. (St 6)
  • Chowdhury, A., & De, D. (2020). FIS-RGSO: Dynamic fuzzy inference system based reverse glowworm swarm optimization of energy and coverage in green mobile wireless sensor networks. Computer Communications, 163, 12–34. (St 7)
  • Chowdhury, A., & De, D. (2021). Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-glowworm swarm optimization-K-means algorithm. Ad Hoc Networks, 122, 102660. (St 8)
  • Deepa, A., & Venkataraman, R. (2021). Enhancing whale optimization algorithm with Levy flight for coverage optimization in wireless sensor networks. Computers & Electrical Engineering, 94, 107359. (St 9)
  • Deng, H., Liu, L., Fang, J., Qu, B. & Huang, Q. (2023). A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm. Mathematics and Computers in Simulation, 205, 794–817.
  • Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.
  • Fang, Q., & Sun, Y. R. (2017). Wireless sensor network coverage efficiency optimization simulation. Computer Simulation, 34(8), 297-301. (St 10)
  • Gu, W. (2020). An improved whale optimization algorithm with cultural mechanism for high-dimensional global optimization problems. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (1282-1286). Chongqing, China.
  • Gunjan. (2023). A review on multi-objective optimization in wireless sensor networks using nature inspired meta-heuristic algorithms. Neural Processing Letters, 55, 2587–2611.
  • Guo, Y., Liu, D., Chen, M., & Liu, Y. (2013). An energy-efficient coverage optimization method for wireless sensor networks based on multi-objective quantum-inspired cultural algorithm. In Proceedings of the ISNN’13: Proceedings of the 10th international conference on Advances in Neural Networks (349). Springer: Berlin/Heidelberg, Germany. (St 11)
  • Hanh, N. T., Hanh, P. T. H., Binh, H. T. T., & Nghia, N. D. (2016). Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks. In 2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) (235-240). Hsinchu, Taiwan.
  • Jia, J., Chen, J., Chang, G., Wen, Y., & Song, J. (2009). Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Computers & Mathematics with Applications, 59, 1767–1775. (St 12)
  • Jiao, W., Tang, R., & Xu, Y. (2022). A coverage optimization algorithm for the wireless sensor network with random deployment by using an improved flower pollination algorithm. Forests, 13, 1690. (St 13)
  • Kapoor, R., & Sharma, S. (2023). Glowworm swarm optimization (GSO) based energy efficient clustered target coverage routing in wireless sensor networks (WSNs). International Journal of System Assurance Engineering and Management, 14(Suppl 2), 622–634. (St 14)
  • Keshmiri, H., & Bakhshi, H. (2020). A new 2-phase optimization-based guaranteed connected target coverage for wireless sensor networks. IEEE Sensors Journal, 20(13), 7472-7486. (St 15)
  • Khalaf, O. I., Abdulsahib, G. M., & Sabbar, B. M. (2020). Optimization of wireless sensor network coverage using the bee algorithm. Journal of Information Science and Engineering, 36, 377–386. (St 16)
  • Lee, J. W., Choi, B. S., & Lee, J. J. (2017). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7, 419-427. (St 17)
  • Li, J., Cui, L., & Zhang, B. (2010). Self-deployment by distance and orientation control for mobile sensor networks. In International Conference on Networking, Sensing and Control (ICNSC) (549–553). IEEE.
  • Li, D. (2022). Research on coverage holes repair in wireless sensor networks based on an improved artificial fish swarm algorithm. International Journal of Autonomous and Adaptive Communications Systems, 15(4), 312–330. https://doi.org/10.1504/ijaacs.2022.127412
  • Liang, D., Shen, H., & Chen, L. (2021). Maximum target coverage problem in mobile wireless sensor networks. Sensors, 21, 184.
  • Liang, J., Tian, M., Liu, Y., et al. (2022). Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm. Scientific Reports, 12, 11687. (St 18)
  • Ling, H., Zhu, T., He, W., Luo, H., Wang, Q., & Jiang, Y. (2020). Coverage optimization of sensors under multiple constraints using the improved PSO algorithm. Mathematical Problems in Engineering, 2020, Article ID 8820907, 10 pages. (St 19)
  • Masoodi, I. S., Dar, M. A., & Banday, M. T. (2018). Energy efficient routing in WSNs based on dynamic cuckoo search algorithm. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (2514-2517). Bangalore, India. (St 20)
  • Narawade, V. E., & Kolekar, U. D. (2017). EACSRO: Epsilon constraint-based adaptive cuckoo search algorithm for rate optimized congestion avoidance and control in wireless sensor networks. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (715-720). Palladam, India. (St 21)
  • Ozdag, R., & Canayaz, M. (2021). A new metaheuristic approach based on orbit in the multi-objective optimization of wireless sensor networks. Wireless Networks, 27, 1-21.
  • Pitchaimanickam, B., & Murugaboopathi, G. (2020). A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Computing and Applications, 32, 7709–7723. (St 22)
  • Qadir, J., Ulaih, U., de Abajo, B. S., & Zapirain, B. G. (2020). Energy-aware and reliability based localization-free cooperative acoustic wireless sensor networks. IEEE Access, 8, 121366–121384.
  • Qi-wei, Z. (2009). Coverage optimization of wireless sensor network. Mechanical & Electrical Engineering Magazine.
  • Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. Journal of Supercomputing, 68(1), 1–48.
  • Roselin, I., & Latha, P. (2016). Energy efficient coverage using artificial bee colony optimization in wireless sensor networks. Journal of Scientific & Industrial Research, 75, 19–27. (St 23)
  • Sampathkumar, A., Mulerikkal, J., & Sivaram, M. (2020). Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wireless Networks, 26, 4227–4238. (St 24)
  • Sharma, N., & Gupta, V. (2022). A survey on applications, challenges, and meta-heuristic-based solutions in wireless sensor network. In EAI/Springer Innovations in Communication and Computing. Springer, Cham.
  • Singh, A., Sharma, S., & Singh, J. (2021). Nature-inspired algorithms for wireless sensor networks: A comprehensive survey. Computer Science Review, 39, 100342. (St 25)
  • Sun, W., Tang, M., Zhang, L., Huo, Z., & Shu, L. (2020). A survey of using swarm intelligence algorithms in IoT. Sensors, 20(5), 1420. (St 26)
  • Thirugnanasambandam, K., Raghav, R. S., Anguraj, D. K., et al. (2021). Multi-objective binary reinforced cuckoo search algorithm for solving connected coverage target based WSN with critical targets. Wireless Personal Communications, 121, 2301–2325. (St 27)
  • Tian, M., Bai, J., Li, J., Huang, M., Zhan, C., & Ren, L. (2021). Elite parallel cuckoo search algorithm for regional coverage control problem in high-density wireless sensor networks. In 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) (285-289). Manchester, United Kingdom. (St 28)
  • Wang, H., Li, K., & Pedrycz, W. (2020). An elite hybrid metaheuristic optimization algorithm for maximizing wireless sensor networks lifetime with a sink node. IEEE Sensors Journal, 20(10), 5634-5649. (St 29)
  • Wang, J., Zhu, D., Ding, Z., & Gong, Y. (2023). WSN coverage optimization based on improved sparrow search algorithm. In 2023 15th International Conference on Advanced Computational Intelligence (ICACI) (pp. 1-8). Seoul, Korea, Republic of. (St 30)
  • Wang, L., Li, C., Wang, H., Zhang, Y., & Liu, Z. (2020). MEP-PSO algorithm-based coverage optimization in directional sensor networks. In GLOBECOM 2020 - 2020 IEEE Global Communications Conference (pp. 1-6). Taipei, Taiwan. (St 31)
  • Wang, L., Wu, W., Qi, J., & Jia, Z. (2018). Wireless sensor network coverage optimization based on whale group algorithm. Computer Science and Information Systems, 15, 569–583. (St 32)
  • Zhang, Y., Cao, L., Yue, Y., Cai, Y., & Hang, B. (2021). A novel coverage optimization strategy based on grey wolf algorithm optimized by simulated annealing for wireless sensor networks. Computational Intelligence and Neuroscience, 6688408. (St 33)
  • Zhao, Q., Li, C., Zhu, D., & Xie, C. (2022). Coverage optimization of wireless sensor networks using combinations of PSO and chaos optimization. Electronics, 11(6), 853. (St 34)
  • Zhu, F., & Wang, W. (2021). A coverage optimization method for WSNs based on the improved weed algorithm. Sensors, 21. (St 35)
  • Zhuang, Y., Wu, C., & Zhang, Y. (2017). A coverage hole recovery algorithm for wireless sensor networks based on cuckoo search. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) (1552-1557). Honolulu, HI, USA. (St 36)
There are 50 citations in total.

Details

Primary Language English
Subjects Satisfiability and Optimisation, Mathematical Optimisation
Journal Section Review Articles
Authors

Gözde Bayat 0000-0003-1116-1881

Eyüp Emre Ülkü 0000-0002-1985-6461

Buket Doğan 0000-0003-1062-2439

Project Number FYL-2023-10950
Early Pub Date June 14, 2025
Publication Date June 30, 2025
Submission Date July 15, 2024
Acceptance Date January 24, 2025
Published in Issue Year 2025 Volume: 24 Issue: 47

Cite

APA Bayat, G., Ülkü, E. E., & Doğan, B. (2025). A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 24(47), 318-348. https://doi.org/10.55071/ticaretfbd.1514389
AMA Bayat G, Ülkü EE, Doğan B. A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. June 2025;24(47):318-348. doi:10.55071/ticaretfbd.1514389
Chicago Bayat, Gözde, Eyüp Emre Ülkü, and Buket Doğan. “A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24, no. 47 (June 2025): 318-48. https://doi.org/10.55071/ticaretfbd.1514389.
EndNote Bayat G, Ülkü EE, Doğan B (June 1, 2025) A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24 47 318–348.
IEEE G. Bayat, E. E. Ülkü, and B. Doğan, “A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 24, no. 47, pp. 318–348, 2025, doi: 10.55071/ticaretfbd.1514389.
ISNAD Bayat, Gözde et al. “A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24/47 (June 2025), 318-348. https://doi.org/10.55071/ticaretfbd.1514389.
JAMA Bayat G, Ülkü EE, Doğan B. A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2025;24:318–348.
MLA Bayat, Gözde et al. “A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 24, no. 47, 2025, pp. 318-4, doi:10.55071/ticaretfbd.1514389.
Vancouver Bayat G, Ülkü EE, Doğan B. A COMPREHENSIVE SURVEY OF NEXT-GENERATION OPTIMIZATION ALGORITHMS FOR TARGET COVERAGE IN MOBILE WIRELESS SENSOR NETWORKS. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2025;24(47):318-4.
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