An optimization-based algorithm for indoor localization of JADE agents

This paper describes and evaluates an optimizationbased localization algorithm which has been recently implemented to enrich the possibilities of the localization add-on module for JADE. The described algorithm targets indoor scenarios and it enables localization of JADE agents running on smart devices in known environments, provided that a conventional WiFi network is present. The algorithm assumes that WiFi access points in fixed and known positions are available, and it estimates the position of the smart device where the agent is running using estimates of the distance between the smart device and each access point. Distance estimates are used to build an optimization problem, whose solution is an estimate of the position of the smart device. The described algorithm uses particle swarm optimization to solve the built optimization problem, but it is open to the adoption of other optimization techniques. The validity of the proposed approach is supported by experimental results shown in the last part of the paper

An optimization-based algorithm for indoor localization of JADE agents

This paper describes and evaluates an optimizationbased localization algorithm which has been recently implemented to enrich the possibilities of the localization add-on module for JADE. The described algorithm targets indoor scenarios and it enables localization of JADE agents running on smart devices in known environments, provided that a conventional WiFi network is present. The algorithm assumes that WiFi access points in fixed and known positions are available, and it estimates the position of the smart device where the agent is running using estimates of the distance between the smart device and each access point. Distance estimates are used to build an optimization problem, whose solution is an estimate of the position of the smart device. The described algorithm uses particle swarm optimization to solve the built optimization problem, but it is open to the adoption of other optimization techniques. The validity of the proposed approach is supported by experimental results shown in the last part of the paper