A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things

In the world of the Internet of Things (IoT), Wireless Sensor Networks (WSN) are an impressive technology. These networks are extremely resource-constrained and require the design of energy-efficient routing techniques. The Clustering and Location Prediction Routing Method based on Multiple Mobile Sinks (CLRP-MMS) for Mobile Internet of Thing (MIoT) is presented in this article. Recently, mobile sinks are used in routing more durability and energy saving in WSN. In this work, first, the entire nodes are divided into clusters, and then each cluster selects a Cluster Head (CH) by calculating the CH Choosing Function (CHCF). When clustering runs on networks with moving nodes, the possibility of disconnecting the nodes from CH nodes will cause a lot of data loss. It will change the amount of energy and rate of data received, but the amount of wasted energy is reduced by predicting the location and reducing the sink and CH nodes’ distance. The simulation results using NS-2 clearly showed that the proposed method improves energy consumption at least 28.12% and increases throughput at least 26.74% compared to EERAMSS and HALPDGSMS methods.