The proliferation of wireless sensor network (WSNs) applications span different domains of life, including medicine, engineering, industry, agriculture, and military. A notable part of research pertaining to WSNs relates to metaheuristic algorithms, implemented to address difficulties in the deployment of these networks. Due to robust and cost effective optimization ability, these algorithms efficiently optimize sensor locations for maximum coverage and extended energy consumption. This chapter presents the definitions of metaheuristic intelligence, wireless sensor network, and their respective types. Also, a wide range of scientific research works that include improving the performance of wireless sensor networks in terms of deployment, localization, and energy using optimization algorithms. Finally, the evaluation criteria for deployment and localization in wireless sensor networks are introduced.
|Title of host publication||Metaheuristics in Machine Learning: Theory and Applications|
|Editors||Diego Oliva, Essam H. Houssein, Salvador Hinojosa|
|Place of Publication||Cham|
|Publisher||Springer International Publishing AG|
|Number of pages||25|
|Publication status||Published - 14 Jul 2021|
|Name||Studies in Computational Intelligence|