Collaborative Localization Strategy Based on Node Selection and Power Allocation in Resource-Constrained Environments

Abstract

Accurate positioning in the constrained environment of Global Navigation satellite Systems (GNSS) is a challenging problem, especially in resource-constrained urban canyon environments. In order to incentivize collaborative agency, this paper, grounded in an economic framework, proposes the utilization of auction mechanisms to address issues pertaining to collaboration and power allocation among agents. For different types of agents, different auction methods are designed according to their own resources for collaborative positioning. Firstly, an Iterative Bidirectional Auction (IBA) cooperative localization algorithm is proposed to solve the problem of cooperation and power allocation among agents in resource-constrained environments. Secondly, in order to ensure the fairness of power distribution, the auction reserve price is introduced, and the relationship between the auction reserve price and power distribution is deduced. Then, considering that there are different types of agents in the actual scenario, One-Shot Auction (OSA) algorithm is proposed to realize the cooperation between user agents and vehicle agents. Finally, analysis and numerical results demonstrate that under the proposed collaborative strategy, agents with better network conditions are more likely to participate in cooperation. Compared to non-cooperative positioning (NC), each agent experiences an improvement in position accuracy of over 60%. The performance of the proposed algorithm is approximately 43% better than uniform power allocation (UPA), and the position accuracy approaches that of the full power allocation (FPA) algorithm. Our algorithm outperforms OSA, PAR and BACL in positioning accuracy with the same agent nodes, and is the most power-efficient. This is pivotal for collaborative positioning under resource constraints.