Communication and Energy-Constrained Neighbor Selection for Distributed Cooperative Localization

Cooperative localization is a promising technique in wireless networks, and neighbor selection (NS) is essential to limit the degree of cooperation and reduce the amount of data to be exchanged. However, the existing NS algorithms may suffer from major performance loss when applied to networks with limited resources (e.g., bandwidth, time and energy). In this paper, we establish a general optimization framework for the NS problem to minimize the localization error under strict resource constraints. Based on the squared position error bound (SPEB) criterion, we formulate two distributed NS problems under implicit and explicit energy constraints, respectively, to balance the energy consumption of the network, where implicit energy constraints mean that specific energy profiles of the nodes' neighbors are unavailable while explicit energy constraints mean the opposite. Moreover, we propose to jointly optimize the NS and power allocation in the explicit case to further improve the localization performance. The resulting problems are challenging to solve due to the nonlinear objective functions and discrete optimization variables. We first transform them into more tractable forms and then develop novel algorithms based on the penalty dual decomposition method to solve the transformed problems efficiently. Simulation results show that the proposed algorithms can significantly outperform benchmark algorithms. In particular, the proposed algorithm almost achieves the performance lower bound in the implicit case.

Automated summary

Cooperative localization is a promising technique in wireless networks, and neighbor selection is crucial in reducing data exchange and improving localization accuracy. This paper proposes a general optimization framework for neighbor selection under resource constraints. Two distributed neighbor selection problems are formulated to balance energy consumption, one with implicit energy constraints and one with explicit energy constraints. In addition, a joint optimization of neighbor selection and power allocation is proposed to further improve localization performance. The resulting challenging problems are solved efficiently using novel algorithms based on the penalty dual decomposition method. Simulation results show that the proposed algorithms outperform benchmark algorithms, with the implicit case almost achieving the performance lower bound.