Distance Estimation Using the Relative Densities and Connectivity of Sensor Nodes for Wireless Sensor Network Localization

Wireless sensor networks (WSNs) play a pivotal role within the Internet of Things (IoT) framework by seamlessly integrating sensor node functionalities with wireless connectivity. A critical research area regarding WSNs revolves around mitigating the constraints imposed by sensor node power consumption. Consequently, the estimation of a sensor node’s location via optimal power has emerged as a significant focus in WSN research. The range-free estimation method is particularly suitable because of its simplicity and lack of reliance on additional hardware or excessive power consumption. It hinges solely on the hop count, which is defined as the number of hops traversed. An early pioneering work in this domain is the Distance Vector-Hop (DV-Hop) localization algorithm, which accurately determines the locations of unknown nodes without requiring specific coverage areas covered by anchor nodes. However, the accuracy of this method depends upon several factors, including node density and the connectivity status of individual sensor nodes, resulting in low accuracy. Thus, this research introduces an algorithm that estimates the distance between a pair of sensor nodes by leveraging the relationship between the distance and the number of sensor nodes present in the intersection area between the pair. Furthermore, the connectivity status of each sensor node within this intersection area is accounted for to increase the precision of distance estimation. To assess the effectiveness of the proposed scheme, the simulation results are compared against those obtained via other recent localization algorithms, which are based on hop counts, namely, DV-Hop, DV-maxHop, GAPSODV-Hop, DECHDV-Hop, and PM.