Mobile Receiver-Assisted Localization Based on Selective Coordinates in Approach to Estimating Proximity for Wireless Sensor Networks

Received signal strength (RSS)-based mobile localization has become popular due to its inexpensive localization solutions in large areas. Compared to various physical properties of radio signals, RSS is an attractive approach to localization because it can easily be obtained through existing wireless devices without any additional hardware. Although RSS is not considered to be a good choice for estimating physical distances, it provides some useful distance related information in adding and indicating connectivity information in neighboring nodes. RSS-based localization is generally divided into range-based and rangefree. Range-based localization can achieve excellent accuracy but is too costly to apply to large-scale networks. Methods of range-free localization are regarded as cost-effective solutions for localization in sensor networks. However, the localizations are subject to the effect of radio patterns that affect variations in the radial distance estimates between nodes. It is a challenging task to select an efficient RSS value that can provide small variations in the radial distance in wireless environments. We propose a method of Mobile Localization using the Proximities of Selective coordinates (MoLPS) to localize target nodes by using information on proximities between target nodes and mobile receivers as a metric to estimate the location of target nodes. We ran a simulation experiment to assess the performance of MoLPS with 100 target nodes that were randomly deployed along a sensory field boundary. We found from the results of the simulation experiment that localization error had been reduced to below 2m in more than 80% of the target nodes. Keywords—Localization, proximity estimation, genetic algorithm, wireless sensor networks, received signal strength.