{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:57:20Z","timestamp":1732042640242},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Foundation of China","award":["61170060"]},{"name":"The National Key Research and Development Project","award":["2020YFB1314103"]},{"name":"The Key teaching research project of Anhui province","award":["2020jyxm0458"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"To address the problems of low monitoring area coverage rate and the long moving distance of nodes in the process of coverage optimization in wireless sensor networks (WSNs), a multi-strategy improved sparrow search algorithm for coverage optimization in a WSN (IM-DTSSA) is proposed. Firstly, Delaunay triangulation is used to locate the uncovered areas in the network and optimize the initial population of the IM-DTSSA algorithm, which can improve the convergence speed and search accuracy of the algorithm. Secondly, the quality and quantity of the explorer population in the sparrow search algorithm are optimized by the non-dominated sorting algorithm, which can improve the global search capability of the algorithm. Finally, a two-sample learning strategy is used to improve the follower position update formula and to improve the ability of the algorithm to jump out of the local optimum. Simulation results show that the coverage rate of the IM-DTSSA algorithm is increased by 6.74%, 5.04% and 3.42% compared to the three other algorithms. The average moving distance of nodes is reduced by 7.93 m, 3.97 m, and 3.09 m, respectively. The results mean that the IM-DTSSA algorithm can effectively balance the coverage rate of the target area and the moving distance of nodes.<\/jats:p>","DOI":"10.3390\/s23084124","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T07:25:11Z","timestamp":1681975511000},"page":"4124","source":"Crossref","is-referenced-by-count":12,"title":["A Multi-Strategy Improved Sparrow Search Algorithm for Coverage Optimization in a WSN"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"http:\/\/orcid.org\/0009-0002-6596-8496","authenticated-orcid":false,"given":"Hui","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"given":"Bin","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"given":"Tian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"given":"Zihang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"ref_1","first-page":"433","article-title":"A PSO based energy efficient coverage control algorithm for wireless sensor networks","volume":"56","author":"Wang","year":"2018","journal-title":"Comput. Mater. Contin"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1109\/TII.2019.2961340","article-title":"Security-aware industrial wireless sensor network deployment optimization","volume":"16","author":"Cao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/COMST.2016.2610578","article-title":"A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems","volume":"19","author":"Fei","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","first-page":"2908","article-title":"Virtual force-based node dynamic coverage algorithm in wireless sensing networks","volume":"30","author":"Zhou","year":"2018","journal-title":"J. Syst. Simul."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.inffus.2017.08.001","article-title":"Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks","volume":"41","author":"Fang","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_6","first-page":"1077","article-title":"Optimal deployment of WSN nodes based on secure connectivity","volume":"31","author":"Sun","year":"2018","journal-title":"J. Sens. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/TCNS.2016.2547579","article-title":"Distributed deployment algorithms for coverage improvement in a network of wireless mobile sensors: Relocation by virtual force","volume":"4","author":"Mahboubi","year":"2016","journal-title":"IEEE Trans. Control Netw. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.comcom.2019.11.001","article-title":"Design of coverage algorithm for mobile sensor networks based on virtual molecular force","volume":"150","author":"Liu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, X., Wang, X., and Qian, Z. (2019). A virtual force algorithm-l\u00e9vy-embedded grey wolf optimization algorithm for wireless sensor network coverage optimization. Sensors, 19.","DOI":"10.3390\/s19122735"},{"key":"ref_10","first-page":"1231","article-title":"Improved dynamic WSN node deployment algorithm for PSO","volume":"40","author":"Cao","year":"2019","journal-title":"Comput. Eng. Des."},{"key":"ref_11","first-page":"1944","article-title":"Research on optimization of WSN area coverage based on Delaunay triangulation strategy","volume":"43","author":"Zhang","year":"2021","journal-title":"Comput. Eng. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s11235-021-00866-y","article-title":"An improved whale optimization algorithm solving the point coverage problem in wireless sensor networks","volume":"79","author":"Toloueiashtian","year":"2022","journal-title":"Telecommun. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s11235-021-00831-9","article-title":"A QoS aware optimal node deployment in wireless sensor network using Grey wolf optimization approach for IoT applications","volume":"78","author":"Jaiswal","year":"2021","journal-title":"Telecommun. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1007\/s00607-021-00906-0","article-title":"Sensor network sensing coverage optimization with improved artificial bee colony algorithm using teaching strategy","volume":"103","author":"Lu","year":"2021","journal-title":"Computing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102660","DOI":"10.1016\/j.adhoc.2021.102660","article-title":"Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm","volume":"122","author":"Chowdhury","year":"2021","journal-title":"Ad. Hoc. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, H., Ban, X., Qian, X., and Ni, J. (2019). An adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network. Sensors, 19.","DOI":"10.3390\/s19194320"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, F., and Wang, W. (2021). A coverage optimization method for WSNs based on the improved weed algorithm. Sensors, 21.","DOI":"10.3390\/s21175869"},{"key":"ref_18","first-page":"1","article-title":"Hybrid coverage strategy for fireworks virtual force based on \u03bc-law explosion operator in WSN","volume":"38","author":"Teng","year":"2022","journal-title":"Control Theory Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100546","DOI":"10.1016\/j.iot.2022.100546","article-title":"Wireless Sensor Network coverage optimization based on Yin\u2013Yang pigeon-inspired optimization algorithm for Internet of Things","volume":"19","author":"Yin","year":"2022","journal-title":"Internet Things"},{"key":"ref_20","first-page":"7483148","article-title":"Multiobjective optimization strategy of WSN coverage based on IPSO-IRCD","volume":"2022","author":"Wu","year":"2022","journal-title":"J. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, Q., Lan, Z., Zhang, D., Yang, L., and Luo, S. (2022). Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem. Sustainability, 14.","DOI":"10.3390\/su14169944"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhang, J., Wei, W., Qin, T., Fan, Y., Luo, X., and Yang, J. (2022). Research on coverage optimization in a WSN based on an improved COOT bird algorithm. Sensors, 22.","DOI":"10.3390\/s22093383"},{"key":"ref_23","first-page":"818","article-title":"An enhanced sparrow search algorithm for wireless sensor network coverage optimization","volume":"34","author":"Wang","year":"2021","journal-title":"J. Sens. Technol."},{"key":"ref_24","unstructured":"Duan, J., Yao, A.-N., Wang, Z., and Yu, L.-T. (2022). Improved sparrow search algorithm to optimize wireless sensor network coverage. J. Jilin Univ., 1\u201311. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/22.1341.T.20220726.1034.003.html."},{"key":"ref_25","unstructured":"Wu, J., Li, H.-B., Luo, L., Cui, H., and Zhao, S.-F. (2022). Multi-objective coverage optimization based on improved sparrow search algorithm in WSN. Electron. Meas. Technol., 1\u201310."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18424","DOI":"10.1109\/ACCESS.2021.3053594","article-title":"A novel coverage optimization strategy for heterogeneous wireless sensor networks based on connectivity and reliability","volume":"9","author":"Cao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yang, J., Qin, T., Fan, Y., Li, Z., and Wei, W. (2022). A Multi-Strategy Improved Sparrow Search Algorithm for Solving the Node Localization Problem in Heterogeneous Wireless Sensor Networks. Appl. Sci., 12.","DOI":"10.3390\/app12105080"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Z.-K., Huang, X.-Y., Zhu, D.-L., Yan, S.-Q., Li, Q., and Guo, W. (2022). A learning sparrow search algorithm incorporating boundary processing mechanism. J. Beijing Univ. Aeronaut. Astronaut., 1\u201316.","DOI":"10.1155\/2022\/2475460"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"118414","DOI":"10.1016\/j.eswa.2022.118414","article-title":"Multi-objective sparrow search algorithm: A novel algorithm for solving complex multi-objective optimisation problems","volume":"210","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_31","unstructured":"Chai, Y., Sun, X.-X., and Ren, S. (2022). A chaotic sparrow search algorithm incorporating multi-way learning. Comput. Eng. Appl., 1\u201312."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, R., and Mo, Y. (2022). Performance of a Novel Enhanced Sparrow Search Algorithm for Engineering Design Process: Coverage Optimization in Wireless Sensor Network. Processes, 10.","DOI":"10.3390\/pr10091691"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/4124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T08:28:05Z","timestamp":1681979285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/4124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,20]]},"references-count":32,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23084124"],"URL":"https:\/\/doi.org\/10.3390\/s23084124","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,20]]}}}