Willingness Allocation-Assisted Cooperative Localization Algorithm Based on Competitive Game for Resource-Constrained Environment

• Published in: IEEE transactions on green communications and networking Vol. 9; no. 2; pp. 498 - 512
• Main Authors: Chen, Geng, Cheng, Lili, Zeng, Qingtian, Shen, Fei, Zhang, Yu-Dong
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2025
• Subjects: Accuracy; Clustering algorithms; competitive game; Cooperative positioning; CWAR; Games; Green products; Location awareness; Optimization; position accuracy; power allocation; Resource management
• ISSN: 2473-2400; 2473-2400
• DOI: 10.1109/TGCN.2024.3436535

With the rapid development of Internet of Things (IoT) technology, cooperative positioning is gradually becoming a key technology improving the localization performance without any infrastructure change. However, the game balance problem of positioning accuracy and resource consumption in resource-constrained scenarios remains challenging. To address these issues, we propose a cooperative positioning algorithm based on Competitive Game (CG) for optimization power management subject to the power budgets, and then adopt the Cooperative Willingness Allocation Rule (CWAR) to further improve positioning accuracy. Firstly, a clustering algorithm is applied to form a node cluster with the target node k as the cluster head for node selection and power allocation conveniently. Secondly, a new energy management strategy based on competitive game is proposed to minimize square position error bound of each agent individually with penalization by its power cost. The optimal response equilibrium point of Nash equilibrium is obtained by using the proposed CG algorithm to develop a solution for energy management games combining global information. Moreover, a fairness aware CWAR is proposed, which uses an improved Shapley value to proportionally distribute the cooperative willingness among the reference agents based on each node's contribution for further expanding the location information of the agent nodes. The experimental results have shown that the proposed algorithm has an excellent performance in position accuracy and resource consumption. Compared with the Average, Random, Link Bargaining Equilibrium(LBE) and Price Allocation Rule (PAR) algorithms, the proposed algorithm improves the positioning accuracy by 38.50%, 49.00%, 31.55% and 17.08%, respectively. Meanwhile, compared with the Exhaustive, Random, LBE and PAR algorithm, the proposed algorithm reduced resource consumption by 87.50%, 69.80%, 57.58% and 63.89% respectively.