|Lam, Ka Ho
|RSSI-based localization algorithms in LoRa networks
|Lee, Wah-ching (EIE)
Cheung, Chi-chung Lawrence (EIE)
|Hong Kong Polytechnic University -- Dissertations
Wireless communication systems
|Department of Electronic and Information Engineering
|vi, 61 pages : color illustrations
|Localization (positioning) is a very important research topic that has been used in many different applications. Many different wireless technologies such as Bluetooth and ZeeBee have been studied for use in localization in indoor environments. However, the most popular option for outdoor environments is satellite-based localization technology, and Global Positioning System (GPS) is the most popular satellite-based system. LoRa wireless technology has recently been proposed to support M2M (Machine-to-Machine) and IoT (Internet of Things) applications. The key features of LoRa are long range (up to 15 km), low power (five to six-year battery lifetime) and low cost (low cost chipsets and networks). These key features support LoRa technology in becoming an appropriate alternative (other than satellite-based localization technology) for localization in outdoor environments. Based on this new technology, we used Receiver Signal Strength Indicator (RSSI) to develop different localization algorithms using LoRa technology. To the best of our knowledge, - We are among the first working on localization using LoRa technology; - We are the first to develop RSSI-based localization algorithms in LoRa networks, and - We are the first to handle blocking and multi-path (non-Gaussian noise) for localization in LoRa networks. Different RSSI-based localization algorithms have been proposed to handle blocking and multi-path (non-Gaussian noise) in LoRa networks: - RSSI-based LoRa Localization with K-mean Clustering (RLL-KC) - RSSI-based LoRa Localization with Iterative Elimination (RLL-IE) - RSSI-based LoRa Localization with Minimum MBRE (RLL-MM) - RSSI-based LoRa Localization with Density-based Clustering (RLL-DC) The first two algorithms are proposed to eliminate anchor node(s) that are highly affected by noise and then process the localization by using the remaining anchor nodes while the last two algorithms are proposed to select a set of anchor nodes that are not highly affected by noise and then use them to process the localization. The performance of all proposed localization algorithms was investigated through simulations. We also developed real LoRa localization systems to investigate the performance of the proposed algorithms. Based on these performance investigations, we conclude that the performance of the proposed localization algorithms is much better than a very popular traditional localization algorithm in terms of localization error. Moreover, the performance of the proposed localization algorithms is similar to GPS, the most popular outdoor localization algorithm on system. Finally, the real LoRa localization systems show that the proposed localization algorithms work properly in both outdoor and large-scale indoor environments.
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