- Saturday, Jan 3rd, 2026
| TITLE | AI-Driven Cloud Computing Services for IoT and Autonomous Vehicle Applications |
|---|---|
| ABSTRACT | The rapid proliferation of Internet of Things (IoT) devices and the advance in autonomous vehicle (AV) technologies have driven the need for powerful, scalable, and low‐latency cloud computing services integrated with artificial intelligence (AI). This paper explores how AI‐driven cloud computing services can support and enhance IoT and autonomous vehicle applications. We review the state of the art in cloud infrastructure, edge and fog computing, AI/ML inference engines, and communication technologies (e.g. V2X, 5G) in AV and IoT contexts. We propose an integrated architecture combining cloud, edge/fog, and AI inference services for IoT + AV systems. Our methodology includes surveying existing works, selecting use cases (environment perception, path prediction, sensor fusion, remote diagnostics), building prototype modules, and evaluating them using metrics such as latency, throughput, accuracy, energy consumption, and reliability. In experiments (simulation or small‐scale real deployment), the prototype demonstrates that using cloud + edge AI services can reduce latency in perception tasks by ~30 50% compared to cloud only architectures; also improvements in path prediction accuracy and sensitivity to obstacles, stronger ability to handle large volumes of sensor data, and more efficient energy usage at the vehicle/edge level. Challenges observed include connectivity variability, privacy/security concerns especially for vehicle data, high bandwidth requirements, model update management, and cost trade‐offs. In the discussion, trade offs between on board vs cloud/edge inference are analysed, as are infrastructure design choices. In conclusion, AI driven cloud services are essential enablers for scalable, intelligent, and responsive IoT + AV systems; however, real‐world adoption requires addressing reliability, regulatory, privacy, and cost issues. Future work will include federated learning among vehicles, more robust edge AI, dynamic resource allocation under variable network conditions, and large scale field trials. |
| AUTHOR | John Agunwamba Paulinus School of Computing, University of Nigeria, Enugu State, Nigeria |
| VOLUME | 12 |
| DOI | DOI:10.15680/IJARETY.2025.1206008 |
| 8_AI-Driven Cloud Computing Services for IoT and Autonomous Vehicle Applications.pdf | |
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