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The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs\u2019 lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs\u2019 lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO).<\/jats:p>","DOI":"10.1155\/2021\/9915133","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T17:36:30Z","timestamp":1624901790000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Elite Adaptive Simulated Annealing Algorithm for Maximizing the Lifespan in LSWSNs"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2406-3981","authenticated-orcid":false,"given":"Jie","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Wenxian","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Menghan","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1862-6709","authenticated-orcid":false,"given":"Mengying","family":"Xu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"crossref","unstructured":"UmashankarM. 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