Abstract

The Wireless Sensor Network (WSN) based on the Internet of Things (IoT) provides more flexibility and reduces network deployment and enjoys a problem of power balance between sensor nodes because IoT operates primarily on fixed devices or sensors, while WSNs operate on mobile sensor nodes. Thus it becomes increasingly difficult to choose efficient and short paths through the WSN Protocol or it may lose focus on selecting the shortest possible route. Consequently, correct use of battery power in a multi-hop transmission is necessary to maintain network connectivity. This article uses IoT-WSN routing from the Artificial Neural Network (ANN). IoT nodes help in data collection and acquisition and WSNs route data and effectively transmit packets between the source and the sink nodes. In terms of mean energy efficiency, delay and network efficiency the simulation results are estimated. The ANN results are more network-wide than the existing machine learning algorithm.