Energy-Efficiency Clustering and Data Collection for Wireless Sensor Networks in Industry 4.0

Abstract

Wireless Sensor Networks (WSNs)-based networking systems introduces to transfer from traditional industry to digital industry, commonly known as the fourth stage of industrialization (Industry 4.0). The WSN is an encouraging technology for many industrial applications because of their several potential benefits. However, they impose several challenges when using them for various monitoring and control applications in the Industry 4.0. Improving lifetime and minimizing power consumption are the main challenges of wireless sensor networks. Solving this problem consist of optimizing node deployment, offering an energy-efficient routing protocol, and providing a clustering approach for sensor nodes in order to optimize battery utilization. Despite the fact that Cluster Head (CH) is overwhelmed with nodes traffic and dies quickly, in most studies, the choice of CH and the creation of clusters take into consideration only the value of residual energy in the sensor nodes, which causes an unequal load balance cluster. The purpose of this study is to propose an advanced clustering in wireless sensors networks, that takes into account not only the value of the residual energy but also the degree of connection, the distance between the CH and other network nodes and the antenna orientation. Besides, a drone routing approach based on artificial intelligence is adapted for data collection to overcome the problem of hot spot. The proposed approach is compared with existing clustering methods such as LEACH, LEACH-C, and LEACH-B that are designed for fixed WSNs. The results of simulations obtained depict that clustering algorithm with optimized routing significantly improves the network lifetime.