Improvement of Outdoor Fingerprint-based Localization using Image Processing

سال انتشار: 1403
محل انتشار: اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده
کد COI اختصاصی: DSAI01_016
زبان مقاله: انگلیسی
تعداد مشاهده: 201

نویسندگان

Faculty of Intelligent Systems Engineering and Data Sience, Persian Gulf University, Bushehr, Iran

Faculty of Intelligent Systems Engineering and Data Sciences, Persian Gulf University, Busher, Iran

ADiT-Lab, Instituto Politecnico de Viana do Castelo, ۴۹۰۰-۳۴۸ Viana do Castelo, Portugal, CiTin - Centro de Interface Tecnologico Industrial, Inovarcos, ۴۹۷۰-۷۸۶ Arcos de Valdevez, Portugal

چکیده

The emergence of industry ۴.۰ and ۵.۰ has brought about profound transformationsin both daily existence and industry. In many industries, the Internet of Things (IoT) is crucialfor supplying information and carrying out the necessary tasks. One significant developmentfor IoT activation is Low-Power Wide Area Networks (LPWANs), particularly LoRaWANs. Accuratetracking of machines, equipment, and objects to improve production and efficiency inindustries is a requirement for the success of these revolutions. Received Signal Strength Indication(RSSI) fingerprint map is one of the advanced localization techniques. The measured RSSIis greatly affected by environmental changes, such as object displacement and weather variations.In outdoor settings, these alterations and displacements are more noticeable. To obtain improvedlocalization accuracy, the fingerprint map needs to be updated frequently due to variations inRSSI caused by changes in the environment. For LoRa fingerprint-based localization, this posesa serious challenge. Environment related images, such as images from surveillance cameras,show environmental changes such as the movement of objects. Therefore, environmental imagescan be a useful tool for detecting environmental changes and updating fingerprint maps. Thisresearch helps to improve the accuracy and reliability of LoRaWAN fingerprint localization systemsusing image processing techniques to learn and predict the effect of environment changeson the RSSI and fingerprint map. To implement the proposed method, a real environment isused in a car parking environment, a place where the movement of vehicles is evident based onthe measured RSSI. The results show that this method can greatly improve localization, whichresults in localization output that is significantly more accurate.

کلیدواژه ها

Industry ۵.۰; Industry ۴.۰; IoT; IIoT; LPWAN; LoRaWAN; LoRa; RSSI; Fingerprint map..

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