Noisy Distance Measurements Using 3-D Localization with Rb-Rf Methods

TL;DR

This paper introduces a hybrid 3-D localization algorithm for wireless sensor networks that combines range-based and range-free strategies to improve accuracy in noisy environments, crucial for rescue and monitoring operations.

Contribution

It proposes a novel hybrid localization method that effectively reduces errors in noisy distance measurements and analyzes its scalability and flexibility in 3-D space.

Findings

Localization accuracy exceeds 90% with increased beacon nodes

Algorithm performance improves with network scalability

System parameters allow adjustable spatial granularity

Abstract

Wireless sensor networks are dynamically formed over the varying topologies. Wireless sensor networks can assist in conducting the rescue operations and can provide search in timely manner. Long time monitoring applications are environment monitoring, security surveillance and habitat monitoring. Further, where it can be deployed in time critical situations when disaster happens. As we are dealing with the human lives here, we can not just rely on the localization schemes that depend upon the connectivity information Rf i.e. range-free algorithms only. Further, rescue operations are carried out in highly noisy environments, so distance based Rb(range-based) localization algorithms generate high error in distance measurements. An efficient algorithm is needed that can measure the location of the sensor nodes near to the living being or being attached to them in 3-D space with a high accuracy. To achieve such kind of accuracy a combination of both the strategies is required. The proposed method which incorporates both the Rb(range-based)&Rfrange-free strategies that successfully localizes nodes in a sensor network with noisy distance measurements. We also have depicted the effect of scalability on the performance of the algorithm. Results show that as the scalability of the network increases with the number of beacon nodes; the performance of the algorithm goes high above 90 percent . The granularity of the areas estimated may be easily adjusted by changing the system parameters which makes the proposed algorithm flexible.