Edge AI for Transforming Autonomous Systems and Telecommunications for Enhanced Efficiency and Responsiveness

Hameed, Maan; Abdulkareem Hameed, Nada; David, Azhibaev; Kassim Ahmad, Hussain; S. Alani, Saad

doi:10.22034/jipm.2025.728384

Edge AI for Transforming Autonomous Systems and Telecommunications for Enhanced Efficiency and Responsiveness

Document Type : Original Article

Authors

Maan Hameed ¹

Nada Abdulkareem Hameed ²

Azhibaev David ³

Hussain Kassim Ahmad ⁴

Saad S. Alani ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728384

Abstract

ABSTRACT
Background: Enabling Edge Artificial Intelligence (Edge AI) to be implemented in autonomous systems and telecommunications can offer for improved real-time data, non-recurring latency, enhanced operational proficiency. Some empirical research suggests that Edge AI minimizes latency by 70%, enhances computing speed by 50%, and cuts bandwidth consumption by 30% in the most demanding cases.
Objective: The purpose of this article is to investigate how Edge AI can serve as an enabling technology for the future of self-sustaining environments such as autonomous mobility and telecommunications in terms of measured utility and differentiation.
Methods: Screening 120 refereed articles and 25 case studies connected to Edge AI application in telecoms and self-governing systems, this systematic looked-for patterns in the proximal research and promising agendas. The review encompassed research works concerned with latency minimization, bandwidth enhancement and enhancement in the processing capacity. Focus was made on application areas like self-driving cars, industrial IoT, and smart city platforms and performance analysis was made in these areas.
Results: The current study prove that when employed in autonomous systems, Edge AI enhances decision making reaction time by 40-60%, while enhancing data traffic throughput within telecommunications networks by 35%. Further, Edge AI makes the overall energy consumption lower in IoT-based applications by cutting down the average usage by a quarter thus creating a sustainable network.
Conclusion: Edge AI becomes a central tool in the development of self-driving cars and telecommunications, increased performance and ability to handle mass amount of data at a low latency. These developments place Edge AI at the base of the evolution of future intelligent systems as the basis for smarter and more responsive technological landscapes.

Keywords

KEYWORDS: Edge Artificial Intelligence (Edge AI)

Autonomous Systems

Telecommunications

Latency Reduction

Real-time Processing

Bandwidth Optimization

Smart Cities

Edge Computing

Network Scalability

References

Ageyev, D., Yarkin, D. Qasim, N. (2014). Traffic aggregation and EPS network planning problem. 2014 First International Scientific-Practical Conference Problems of Infocommunications Science and Technology, 14-17 Oct.. https://doi.org/10.1109/INFOCOMMST.2014.6992316.

Ali, H., Karim, S., Saab, M., Hassan, S., Bodnar, N., Ahmed, S., Mustafa, S., et al. (2024). Technological innovations and sustainability: Shaping the future of smart cities in urban planning. Edelweiss Applied Science and Technology, 8, 1992-2011. https://doi.org/10.55214/25768484.v8i4.1577

Bourechak, A., Zedadra, O., Kouahla, M. N., Guerrieri, A., Seridi, H., and Fortino, G. (2023). At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives. Sensors, 23 (3). https://doi.org/10.3390/s23031639.

Chavhan, S., Gupta, D., Gochhayat, S. P., N., C. B., Khanna, A., Shankar, K., and Rodrigues, J. J. P. C. (2022). Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities. ACM Trans. Internet Technol., 22 (4), Article 106. https://doi.org/10.1145/3507906

Chen, C., Wang, C., Liu, B., He, C., Cong, L., and Wan, S. (2023). Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems, 24 (11), 13023-13034. https://doi.org/10.1109/TITS.2022.3232153

Christou, A. G., Stergiou, C. L., Memos, V. A., Ishibashi, Y., and Psannis, K. E. (2023). Revolutionizing Connectivity: The Power of AI, IoT, and Edge Computing for Smart and Autonomous Systems. 2023 6th World Symposium on Communication Engineering (WSCE), 27-29 Sept. https://doi.org/10.1109/WSCE59557.2023.10365771.

Dai, Y., Xu, D., Maharjan, S., Qiao, G., and Zhang, Y. (2019). Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles. IEEE Wireless Communications, 26 (3), 12-18. https://doi.org/10.1109/MWC.2019.1800411

Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., and Zomaya, A. Y. (2020). Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Internet of Things Journal, 7 (8), 7457-7469. https://doi.org/10.1109/JIOT.2020.2984887

Garg, S., Kaur, K., Aujla, G. S., Kaddoum, G., Garigipati, P., and Guizani, M. (2023). Trusted Explainable AI for 6G-Enabled Edge Cloud Ecosystem. IEEE Wireless Communications, 30 (3), 163-170. https://doi.org/10.1109/MWC.016.220047

Gong, T., Zhu, L., Yu, F. R., and Tang, T. (2023). Edge Intelligence in Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 24 (9), 8919-8944. https://doi.org/10.1109/TITS.2023.3275741

Huang, S., Wang, S., Wang, R., Wen, M., and Huang, K. (2021). Reconfigurable Intelligent Surface Assisted Mobile Edge Computing With Heterogeneous Learning Tasks. IEEE Transactions on Cognitive Communications and Networking, 7 (2), 369-382. https://doi.org/10.1109/TCCN.2021.3056707

Iatsykovska, U., Khlaponin. Y., Qasim, N., Khlaponin, D., Trush, I., Karpiński, M. (2018). Operation analysis of statistical information telecommunication networks using neural network technology. IEEE. Conferences on Intelligent Data Acquisition and Advanced Computing Systems, 460 (1), 199-203. https://doi.org/10.1051/e3sconf/202346004003

Iyer, S. S., and Roychowdhury, V. (2023). AI computing reaches for the edge. Science, 382 (6668), 263-264. https://doi.org/10.1126/science.adk6874

Jassim, M. M., Abass, H. K., Al-Ani, A. R. M., Mahdi, A. F., Almaaly, A. M. J., Navrozova, Y., and Bodnar, N. (2024). Deep Learning Approaches for Predicting Climate Change Impacts: An Empirical Analysis. 2024 36th Conference of Open Innovations Association (FRUCT). https://doi.org/10.23919/FRUCT64283.2024.10749950.

Katare, D., Perino, D., Nurmi, J., Warnier, M., Janssen, M., and Ding, A. Y. (2023). A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services. IEEE Communications Surveys & Tutorials, 25 (4), 2714-2754. https://doi.org/10.1109/COMST.2023.3302474

Lee, Y. L., Tsung, P. K., and Wu, M. (2018). Techology trend of edge AI. 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), 16-19 April 2018. https://doi.org/10.1109/VLSI-DAT.2018.8373244.

Letaief, K. B., Shi, Y., Lu, J., and Lu, J. (2022). Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications. IEEE Journal on Selected Areas in Communications, 40 (1), 5-36. https://doi.org/10.1109/JSAC.2021.3126076

Lin, Z., Bi, S., and Zhang, Y. J. A. (2021). Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems. IEEE Transactions on Wireless Communications, 20 (11), 7257-7271. https://doi.org/10.1109/TWC.2021.3081991

Mahmood, O. F., Jasim, I. B., Qasim, N. H. (2021). Performance Enhancement of Underwater Channel Using Polar Code-OFDM Paradigm International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 3 (9), 55-62. https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_9_september_2021/15978/final/fin_irjmets1630649429.pdf

McEnroe, P., Wang, S., and Liyanage, M. (2022). A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges. IEEE Internet of Things Journal, 9 (17), 15435-15459. https://doi.org/10.1109/JIOT.2022.3176400

Muratore, G., Rincon, J. A., Julian, V., Carrascosa, C., Greco, G., and Fortino, G. (2020). Towards a Dynamic Edge AI Framework Applied to Autonomous Driving Cars. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection, 406-415. https://doi.org/10.1007/978-3-030-51999-5_34

Navardi, M., Humes, E., and Mohsenin, T. (2022). E2EdgeAI: Energy-Efficient Edge Computing for Deployment of Vision-Based DNNs on Autonomous Tiny Drones. 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), 5-8 Dec. https://doi.org/10.1109/SEC54971.2022.00077.

Qasim, N., and Pyliavskyi, V. (2020). Color temperature line: forward and inverse transformation. Semiconductor physics, quantum electronics and optoelectronics, 23, 75-80. https://doi.org/10.15407/spqeo23.01.075

Qasim, N. H., Vyshniakov, V., Khlaponin, Y., and Poltorak, V. (2021). Concept in information security technologies development in e-voting systems. International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 3 (9), 40-54. https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_9_september_2021/15985/final/fin_irjmets1630649545.pdf

Riggio, R., Coronado, E., Linder, N., Jovanka, A., Mastinu, G., Goratti, L., Rosa, M., et al. (2021). AI@EDGE: A Secure and Reusable Artificial Intelligence Platform for Edge Computing. 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 8-11 June 2021. https://doi.org/10.1109/EuCNC/6GSummit51104.2021.9482440.

Shen, Y., Shao, J., Zhang, X., Lin, Z., Pan, H., Li, D., Zhang, J., et al. (2024). Large language models empowered autonomous edge AI for connected intelligence. IEEE Communications Magazine, 62 (10), 140-146. https://doi.org/10.1109/MCOM.001.2300550

Shi, Y., Yang, K., Jiang, T., Zhang, J., and Letaief, K. B. (2020). Communication-Efficient Edge AI: Algorithms and Systems. IEEE Communications Surveys & Tutorials, 22 (4), 2167-2191. https://doi.org/10.1109/COMST.2020.3007787

Szántó, P., Kiss, T., and Sipos, K. J. (2022). Energy-efficient AI at the Edge. 2022 11th Mediterranean Conference on Embedded Computing (MECO), 7-10 June. https://doi.org/10.1109/MECO55406.2022.9797178.

Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., and Chen, M. (2019). In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning. IEEE Network, 33 (5), 156-165. https://doi.org/10.1109/MNET.2019.1800286

Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., and Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE, 107 (8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951

Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., and Huang, K. (2018). Towards an Intelligent Edge: Wireless Communication Meets Machine Learning. IEEE Communications Magazine, 58 (1), 19-25. https://doi.org/10.1109/MCOM.001.1900103

Zhu, S., Ota, K., and Dong, M. (2022). Green AI for IIoT: Energy Efficient Intelligent Edge Computing for Industrial Internet of Things. IEEE Transactions on Green Communications and Networking, 6 (1), 79-88. https://doi.org/10.1109/TGCN.2021.3100622

Zou, X., Li, K., Zhou, J. T., Wei, W., and Chen, C. (2023). Robust Edge AI for Real-Time Industry 4.0 Applications in 5G Environment. IEEE Communications Standards Magazine, 7 (2), 64-70. https://doi.org/10.1109/MCOMSTD.0008.2100019

Zou, Z., Jin, Y., Nevalainen, P., Huan, Y., Heikkonen, J., and Westerlund, T. (2019). Edge and Fog Computing Enabled AI for IoT-An Overview. 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 18-20 March. https://doi.org/10.1109/AICAS.2019.8771621.