Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

Because of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT-based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy-efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP-MS) technique for IoT supported WSN. The EACMRP-MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP-MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type-II fuzzy logic (T2FL) technique is used for the optimal selection of multi-hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP-MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.