2022/10/01 by Ali Abdullah S. AlQahtani, AlQahtani, Ali Abdullah S., Nazim Choudhury +1
Computer Science · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Radio Wave Propagation Studies #Speech and Audio Processing
paper · pdf · doi:10.48550/arxiv.2210.00270
openalex publication_date 2022/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For decades, the determination of an objects location has been implemented utilizing different technologies. Despite GPS (Global Positioning System) provides a scalable efficient and cost effective location services however the satellite emitted signals cannot be exploited indoor to effectively determine the location. In contrast to GPS which is a cost effective localization technology for outdoor locations several technologies have been studied for indoor localization. These include Wireless Fidelity (Wi-Fi) Bluetooth Low Energy (BLE) and Received Signal Strength Indicator (RSSI) etc. This paper presents an enhanced method of using RSSI as a mean to determine an objects location by applying some Machine Learning (ML) concepts. The binary classification is defined by considering the adjacency of the coordinates denoting objects locations. The proposed features were tested empirically via multiple classifiers that achieved a maximum of 96 percent accuracy.