An Approach for Generating Effective AP Set Based on Information Discrimination

The widespread adoption of wireless networks has led to a significant increase in the deployment of Access Points (APs) for Received Signal Strength (RSS) fingerprint localization. This surge has introduced redundancy, negatively impacting localization and increasing computational costs. While traditional AP filtering mitigates redundancy to some extent, an Effective Access Point Set Construction method (EID) based on information distinctiveness was proposed in this paper. Firstly, EID was utilized to evaluate APs by assessing their spatial resolution through information distinctiveness. Moreover, an incremental clustering algorithm was designed, which was able to form sets of different categories according to localization abilities of APs. Finally, an AP effective set selection strategy based on the maximum point set distance principle was proposed in the paper, resulting in suitable AP sets. Extensive experiments validated the performance of the proposed EID in real-world scenarios. EID was also compared with existing AP selection methods including the Group Discrimination-Based (GDB) algorithm, Software Defined Network (SDN) algorithm, and Nonuniform Quantization RSSI Entropy (NQRE) algorithm. Experimental results showed that EID demonstrates a significant improvement in localization accuracy by 18.7%, 11.2%, and 14.6% with enhanced stability, achieving a localization error below 1.2 m in 95% of cases, even with a 40% reduction in AP quantity. © 2024 Editorial Department of Journal of Sichuan University. All rights reserved.