A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Abstract

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.