{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:36:53Z","timestamp":1778603813176,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"African Center of Excellence in Internet of Things (ACEIoT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The application of the Internet of Things (IoT), vehicles to infrastructure (V2I) communication and intelligent roadside units (RSU) are promising paradigms to improve road traffic safety. However, for the RSUs to communicate with the vehicles and transmit the data to the remote location, RSUs require enough power and good network quality. Recent advances in technology have improved lithium-ion battery capabilities. However, other complementary methodologies including battery management systems (BMS) have to be developed to provide an early warning sign of the battery\u2019s state of health. In this paper, we have evaluated the impact of the received signal strength indication (RSSI) and the current consumption at different transmission frequencies on a static battery-based RSU that depends on the global system for mobile communications (GSM)\/general packet radio services (GPRS). Machine learning (ML) models, for instance, Random Forest (RF) and Support Vector Machine (SVM), were employed and tested on the collected data and later compared using the coefficient of determination (R2). The models were used to predict the battery current consumption based on the RSSI of the location where the RSUs were imposed and the frequency at which the RSU transmits the data to the remote database. The RF was preferable to SVM for predicting current consumption with an R2 of 98% and 94%, respectively. It is essential to accurately forecast the battery health of RSUs to assess their dependability and running time. The primary duty of the BMS is to estimate the status of the battery and its dynamic operating limits. However, achieving an accurate and robust battery state of charge remains a significant challenge. Referring to that can help road managers make alternative decisions, such as replacing the battery before the RSU power source gets drained. The proposed method can be deployed in other remote WSN and IoT-based applications.<\/jats:p>","DOI":"10.3390\/s23073536","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T06:15:15Z","timestamp":1679984115000},"page":"3536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Battery-Powered RSU Running Time Monitoring and Prediction Using ML Model Based on Received Signal Strength and Data Transmission Frequency in V2I Applications"],"prefix":"10.3390","volume":"23","author":[{"given":"Vienna N.","family":"Katambire","sequence":"first","affiliation":[{"name":"African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Musabe","sequence":"additional","affiliation":[{"name":"Rwanda Polytechnic, Kigali P.O. Box 164, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfred","family":"Uwitonze","sequence":"additional","affiliation":[{"name":"African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Didacienne","family":"Mukanyiligira","sequence":"additional","affiliation":[{"name":"National Council for Science and Technology, Kigali P.O. Box 2285, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.3390\/smartcities5040066","article-title":"An Intelligent IoT Based Traffic Light Management System: Deep Reinforcement Learning","volume":"5","author":"Damadam","year":"2022","journal-title":"Smart Cities"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105702","DOI":"10.1016\/j.asoc.2019.105702","article-title":"An improved pheromone-based vehicle rerouting system to reduce traffic congestion","volume":"84","author":"Ho","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_3","first-page":"1550147716683612","article-title":"Traffic management systems: A classification, review, challenges, and future perspectives","volume":"13","author":"Brennand","year":"2017","journal-title":"Int. J. Distrib. Sens. 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