Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

The Internet of Things (IoT) is a network numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in IoT, which helps to produce seamless data highly influence network’s lifetime. Despite significant applications several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency considered be vital design IoT-assisted WSN; this accomplished by clustering multi-hop routing techniques. In view this, we introduce improved metaheuristic-driven energy-aware cluster-based (IMD-EACBR) scheme for WSN. proposed IMD-EACBR model intends achieve maximum energy utilization lifetime network. order attain primarily designs Archimedes optimization algorithm-based (IAOAC) technique cluster head (CH) election organization. addition, IAOAC algorithm computes suitability purpose connects multiple structures specifically efficiency, detachment, node degree, inter-cluster distance. Moreover, teaching–learning-based (TLBO) (TLBO-MHR) applied optimum selection routes destinations. Furthermore, TLBO-MHR method originates using distance metrics. performance has been examined aspects. Simulation outcomes demonstrated enhancements over recent state-of-the-art approaches. transmission emergency data, based on requirements hop count end, subjected rigorous testing NS-3.26’s full simulation capabilities. results reveal improvements terms proportion dead nodes, network, amount consumed, packet delivery ratio (PDR), latency.