Adaptive Parallel Seeker Optimization-based Route Planning for clustered WSN in smart cities. (September 2022) - Mansour, Romany F.; Abdel-Khalek, Sayed; Basha, Shaik Mahaboob; Khayyat, Manal M; Elnaim, Bushra M.E.; Shankar, Vishnu
Highlights: Develop an Adaptive Parallel Seeker Optimization based Routing Technique Employ weight based clustering scheme for proper cluster head selection Derives a fitness function for APSO algorithm to optimally choose routes Validate the performance of APSO-EARPT technique under different measures Abstract: Smart city is a phenonmenon that integrates physical and social infrastructures with Information Technology to keep a city's cooperative intelligence under control. Smart cities primarily rely on Wireless Sensor Networks (WSN) to manage and maintain its service offerings. In literature, clustering and multihop routing techniques have been proposed, validated and implemented to reduce the consumption of energy in the network. With this motivation, the current study develops Adaptive Parallel Seeker Optimization-based Energy Aware Route Planning Technique (APSO-EARPT) for clustered WSN in smart cities. The presented APSO-EARPT technique concentrates on appropriate selection of Cluster Heads (CHs) and optimal routes in WSN. To accomplish this, APSO-EARPT model encompasses Weight-Based Clustering Scheme (WBCS) for effective selection of CHs. Then, routing process is performed with the help of APSO algorithm. The proposed APSO-EARPT technique computes a Fitness Function (FF) that comprises of three variables such as Residual Energy (RE), distance to Base Station (BS), and node degree. This fitness function helps in optimal selection of routes in WSN. In order to validate the supremacy of the proposed APSO-EARPT model in terms of network lifespan and energy efficiency, simulations were conducted and the results confirmed the excellent performance of the proposed model. Graphical Abstract: Image, graphical abstract