A Model for Self-deployment of Autonomous Mobile Sensor Network in an Unknown Indoor Environment

Abstract

Wireless sensor networks (WSN) emerge at the center of the fast expanding Internet of Things (IoT) revolution, and hence increased research efforts are being directed towards its efficient deployment, optimization and adaptive operation. Rapid deployment of WSN in an unknown open environment is a critical challenge that involves finding optimal locations for the network nodes to deliver optimally balanced sensing and communication services at the maximum possible coverage subject to complex mutual constraints. We address this challenge with a variant of the Voronoi-based algorithm that leverages the converged movement towards Voronoi cells’ centers with the intelligent nodes’ provisioning algorithm to deliver fully automated and autonomous WSN that rapidly self-deploys itself to any finite indoor environment without using any prior knowledge of the size and structure of the target space. Sequential provisioning supports realistic implementation that accounts for collision avoidance and mitigates the risk of wasteful over-deployment. The preliminary comparative simulation results carried out in a simplified environment indicate very fast convergence to the well balanced WSN at the fairly small deployment cost and thereby validate our model as a very promising compared to the previous approaches to WSN deployment.