ArticleSensors (Basel, Switzerland)2024
Integrating Connected Vehicles into IoT Ecosystems: A Comparative Study of Low-Power, Long-Range Communication Technologies.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
Abstract
Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities while remaining cost-effective, specifically Bluetooth Classic BR-EDR, Bluetooth LE, ZigBee, nRF24, and LoRa-for Vehicle-to-Infrastructure (V2I) and Vehicle-to-IoT (V2IoT) ecosystem interactions. During this research, several field tests were conducted employing different types of communication modules, across three distinct environments: an open-field inter-urban road, a forest inter-urban road, and an urban road. The modules were evaluated based on the communication range, messaging rate, error rate, and geographical data from GNSS (Global Navigation Satellite System) coordinates, using point-to-point communication between a roadside unit (RSU) and a moving vehicle equipped with an onboard unit (OBU). The results demonstrate the usability of these technologies for integrating vehicles into both public infrastructure (for V2I services) and private IoT systems, highlighting their potential for scalable, cost-effective deployment in smart transportation systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.