An RSSI-based Wireless Sensor Node Localisation using Trilateration and Multilateration Methods for Outdoor Environment
Not on the map. Why?
Abstract (arXiv)
Localisation can be defined as estimating or finding a position of the node. There are two techniques in localisation, which are range-based and range-free techniques. This paper focusses on the Received Signal Strength Indicator (RSSI) localisation method, which is categorised in a range-based technique along with the time of arrival, time difference of arrival and angle of arrival. Therefore, this study aims to compare the trilateration and multilateration method for RSSI-based technique for localising the transmitted (Tx) node. The wireless sensor module in the work used LOng-RAnge radio (LoRa) with 868MHz frequency. Nowadays, wireless networks have been a key technology for smart environments, monitoring, and object tracking due to low power consumption with long-range connectivity. The number of received (Rx) nodes are three and four for trilateration and multilateration methods, respectively. The transmitted node is placed at 32 different coordinates within the 10x10 meter outdoor area. The results show that error localisation obtained for General Error Localisation (GER) for multilateration and trilateration is 1.83m and 2.30m, respectively. An additional, the maximum and minimum error for multilateration and trilateration from 1.00 to 5.28m and 0.5 to 3.61m. The study concludes that the multilateration method more accurate than trilateration. Therefore, with the increasing number of Rx node, the accuracy of localisation of the Tx node increases.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.
3 papers.
- 2022ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondencescited by 1
- 2025Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning modelscited by 1
- 2025Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfacescited by 0
- 2025Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces2 Nov 2025
- 2025Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models7 Sep 2025
- 2022ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences22 Nov 2022