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 / Monica, Stefania; Bergenti, Federico. - ELETTRONICO. - 1867:(2017), pp. 65-70. (Intervento presentato al convegno 18th Workshop "From Objects to Agents", WOA 2017 nel 2017).
An optimization-based algorithm for indoor localization of JADE agents
MONICA, Stefania;BERGENTI, Federico
2017-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.