Artificial Intelligence–Based Energy‐Efficient Clustering and Routing in IoT‐Assisted Wireless Sensor Network

Abstract

Two well-known optimization problems are energy-efficient routing and clustering which have been studied widely to extend lifetime of Internet of Things (IoT)– assisted wireless sensor networks (WSNs). An advancement made in wireless technologies has developed a greater impact over the IoT systems. For connected people and objects, IoT have become popular for exchanging and collecting data based on sensors. Communication between entities plays a vital role to develop a sustainable environment. In IoT-assisted WSNs, there are several ways in which the nodes are considered as the resource parameters, like energy resources, storage resources, and computing resources for achieving higher energy utilization and for maintaining long network lifetime. Clustering is one of the efficient approaches that connects and organizes the sensor nodes by balancing the loads and maximizing the lifespan of the network. At first, the nodes are simulated together in IoT-assisted WSN. Using optimization algorithm, this performs the cluster head selection, after that on the basis of optimization the routing process is done. By considering the fitness, the routing path is selected parameters, like QoS parameters, and trust factors. The QoS parameters include the energy, delay, distance, as well as link lifetime. Using fitness parameter, the optimal path with the minimum distance path is selected. The industrial operations have been transformed by Artificial Intelligence (AI). AI is using for reducing the computational costs of optimization this is one of the effective and important applications of AI. In this chapter, firstly, optimization concept is fully explained. Then, various optimization algorithms explained used in the clustering and routing. These algorithms optimize the problem of study. At the end, various models are fully discussed. The results of the algorithms show that by better clustering and routing, the conditions can be improved.