{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:01:30Z","timestamp":1777705290891,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND).<\/jats:p>","DOI":"10.3233\/jifs-224252","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:07:14Z","timestamp":1691939234000},"page":"5299-5311","source":"Crossref","is-referenced-by-count":0,"title":["A novel cluster heads election method based on group sparse TSK fuzzy system for energy-efficient wireless sensor network"],"prefix":"10.1177","volume":"46","author":[{"given":"Xuehui","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Information Technology and Intelligent Manufacturing, Shanghai Xingjian College, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Transit Engineering Informatization (FSDI), 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