Neural network based mobile phone localization using Bluetooth connectivity

Location information is useful for mobile phones. There exists a dilemma between the relatively high price of GPS devices and the dependence of location information acquisition on GPS for most phones in current stage. To tackle this problem, in this paper, we investigate the position inference of phones without GPS according to Bluetooth connectivity and positions of beacon phones. With the position of GPS-equipped phones as beacons and with the Bluetooth connections between neighbor phones as constraints, we formulate the problem as an optimization problem defined on the Bluetooth network. The solution to this optimization problem is not unique. Heuristic information is employed to improve the performance of the result in the feasible set. Recurrent neural networks are developed to solve the problem distributively in real time. The convergence of the neural network and the solution feasibility to the defined problem are both theoretically proven. The hardware implementation of the proposed neural network is also explored in this paper. Simulations and comparisons with different application backgrounds are considered. The results demonstrate the effectiveness of the proposed method.