Using multi-objective metaheuristics for the optimal selection of positioning systems

The interworking between cellular and wireless local area networks, as well as the spreading of mobile devices equipped with several positioning technologies pave the ground to new and more favorable indoor/outdoor location-based services (LBSs). Thus, wireless internet service providers are required to take several positioning methods into account at the same time, to leverage the different features of existing technologies. This would allow providing LBSs satisfying the user-required quality of position in terms of accuracy, privacy, power consumption, and often, conflicting features. Therefore, this paper presents GlobalPreLoc, a multi-objective strategy for the dynamic and optimal selection of positioning technologies. The strategy exploits a pattern-mining algorithm for future position prediction combined with conventional multi-objective evolutionary algorithms, for choosing continuously the best location providers, accounting for the user requirements, the terminal capabilities, and the surrounding positioning infrastructures. To practically implement the strategy, we also designed an architecture based on secure user plane location specification to provide indoor and outdoor LBSs in interworking wireless networks exploiting GlobalPreLoc features.