{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:42:21Z","timestamp":1784763741497,"version":"3.55.0"},"reference-count":21,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:p> Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence\u2013based approaches. This article presents a novel swarm intelligence\u2013based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm\u2013based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization\u2013grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization\u2013grey wolf optimization technique under all the test cases. <\/jats:p>","DOI":"10.1177\/1550147720949133","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T09:52:35Z","timestamp":1600941155000},"page":"155014772094913","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":58,"title":["Swarm intelligence\u2013based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks"],"prefix":"10.1177","volume":"16","author":[{"given":"Mohamed","family":"Elhoseny","sequence":"first","affiliation":[{"name":"Faculty of Computers & Information, Mansoura University, Mansoura, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R Sundar","family":"Rajan","sequence":"additional","affiliation":[{"name":"Department of 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