ML-based Network Pruning for Routing Data Overhead Reduction in Wireless Sensor Networks

Routing in Wireless Sensor Networks (WSNs) is one of the tasks that heavily impact network lifetime: current routing protocols, such as Ad-hoc On-demand Distance Vector (AODV), generate excessive and rather unnecessary overhead for route discovery, which in turn contributes to deplete the limited power resources of sensors. In this work, we propose a novel machine learning-based approach to perform network pruning during route discovery aiming at reducing data overhead. Our approach assumes that sensor nodes are aware of their locations and have processing capabilities to run lightweight machine learning algorithms. We perform extensive simulations considering WSNs consisting of different amounts of nodes. Results show that our proposed approach can reduce data overhead by 50% to 65%, depending on the amount of nodes and pruning aggressiveness.