Optimization of AI-Powered Edge-IoT Architecture for Real-Time Response in Distributed Smart City Systems
Keywords:
Smart City, Edge Computing, Internet of Things, Artificial Intelligence, Real-Time Systems
Abstract
Smart city development, increasingly powered by the widespread adoption of Internet of Things (IoT) devices, demands systems capable of processing data in real time and with high reliability. Traditional cloud-based models often fall short due to latency, bandwidth issues, and privacy risks when managing the constant stream of data from distributed IoT sensors. This paper reviews recent advancements and proposes optimization strategies for integrating Artificial Intelligence (AI) into Edge-IoT systems, specifically designed to enhance responsiveness in smart city environments. Key areas include lightweight AI model design, adaptive resource management, efficient data flow, and network enhancements. We highlight technologies such as federated learning, task offloading, and software-defined networking to minimize delays and increase performance. In addition, the paper discusses challenges—scalability, heterogeneity, energy efficiency, and security—while outlining promising directions for future research. This work offers valuable insights for researchers and professionals working to build smart urban systems that are responsive, efficient, and context-aware.
Downloads
Download data is not yet available.
References
[1] A. Kirimtat, O. Krejcar, A. Kertesz, and M. F. Tasgetiren, “Future Trends and Current State of Smart City Concepts: A Survey,” IEEE Access, vol. 8, pp. 86448–86467, 2020, doi: 10.1109/ACCESS.2020.2992441.
[2] S. N. Ali Kazmi, A. Ulasyar, and M. F. Nadeem Khan, “IoT based Energy Efficient Smart Street Lighting Technique with Air Quality Monitoring,” in 2020 14th International Conference on Open Source Systems and Technologies (ICOSST), 2020, pp. 1–6. doi: 10.1109/ICOSST51357.2020.9332982.
[3] C. A. R. Freire, F. A. F. Ferreira, E. G. Carayannis, and J. J. M. Ferreira, “Artificial Intelligence and Smart Cities: A DEMATEL Approach to Adaptation Challenges and Initiatives,” IEEE Trans. Eng. Manag., vol. 70, no. 5, pp. 1881–1899, 2023, doi: 10.1109/TEM.2021.3098665.
[4] W. Li, Q. Li, L. Chen, F. Wu, and J. Ren, “A Storage Resource Collaboration Model Among Edge Nodes in Edge Federation Service,” IEEE Trans. Veh. Technol., vol. 71, no. 9, pp. 9212–9224, 2022, doi: 10.1109/TVT.2022.3179363.
[5] H. Rexha and S. Lafond, “Data Collection and Utilization Framework for Edge AI Applications,” in 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), 2021, pp. 105–108. doi: 10.1109/WAIN52551.2021.00023.
[6] S. Nimkar and M. M. Khanapurkar, “Edge Computing for IoT: A Use Case in Smart City Governance,” in 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 2021, pp. 1–5. doi: 10.1109/ICCICA52458.2021.9697263.
[7] N. Ghafoorianfar and M. Roopaei, “Environmental Perception in Autonomous Vehicles Using Edge Level Situational Awareness,” in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 444–448. doi: 10.1109/CCWC47524.2020.9031155.
[8] M. Pan, W. Su, and Y. Wang, “Review of Research on the Curriculum for Artificial Intelligence and Industrial Automation based on Edge Computing,” in 2021 International Conference on Networking and Network Applications (NaNA), 2021, pp. 222–226. doi: 10.1109/NaNA53684.2021.00045.
[9] A. Tariq, A. Lakas, F. M. Sallabi, T. Qayyum, M. A. Serhani, and E. Baraka, “Empowering Trustworthy Client Selection in Edge Federated Learning Leveraging Reinforcement Learning,” in 2023 IEEE/ACM Symposium on Edge Computing (SEC), 2023, pp. 372–377. doi: 10.1145/3583740.3626815.
[10] J.-C. Gamazo-Real, R. T. Fernández, and A. M. Armas, “Estimation of Air Quality Parameters using Lightweight Machine Learning on Low-cost Edge-IoT Architectures,” in 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 2022, pp. 348–352. doi: 10.1109/ISMODE56940.2022.10180952.
[11] S. Seid, M. Zennaro, M. Libsie, E. Pietrosemoli, and P. Manzoni, “A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring,” in 2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020, pp. 762–767. doi: 10.1109/MSN50589.2020.00130.
[12] M. D. Hossain et al., “Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach,” Sensors, vol. 22, no. 10, 2022, doi: 10.3390/s22103678.
[13] P. A. Budiman, Marfani, and D. M. Sari, “Multi-Access Edge Computing Implemention On Tower Ecosystem Indonesia: Challenges And Visibility,” in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022, pp. 158–162. doi: 10.1109/ICTC55196.2022.9952477.
[14] T. Liang, J. Glossner, L. Wang, S. Shi, and X. Zhang, “Pruning and quantization for deep neural network acceleration: A survey,” Neurocomputing, vol. 461, pp. 370–403, 2021, doi: https://doi.org/10.1016/j.neucom.2021.07.045.
[15] M. Ali, F. Naeem, M. Tariq, and G. Kaddoum, “Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 2, pp. 778–789, 2023, doi: 10.1109/JBHI.2022.3181823.
[16] L. Pioli, C. F. Dorneles, D. D. J. de Macedo, and M. A. R. Dantas, “An overview of data reduction solutions at the edge of IoT systems: a systematic mapping of the literature,” Computing, vol. 104, no. 8, pp. 1867–1889, 2022, doi: 10.1007/s00607-022-01073-6.
[17] L. EL-Garoui, S. Pierre, and S. Chamberland, “A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments,” Smart Cities, vol. 3, no. 3, pp. 1004–1021, 2020, doi: 10.3390/smartcities3030050.
[2] S. N. Ali Kazmi, A. Ulasyar, and M. F. Nadeem Khan, “IoT based Energy Efficient Smart Street Lighting Technique with Air Quality Monitoring,” in 2020 14th International Conference on Open Source Systems and Technologies (ICOSST), 2020, pp. 1–6. doi: 10.1109/ICOSST51357.2020.9332982.
[3] C. A. R. Freire, F. A. F. Ferreira, E. G. Carayannis, and J. J. M. Ferreira, “Artificial Intelligence and Smart Cities: A DEMATEL Approach to Adaptation Challenges and Initiatives,” IEEE Trans. Eng. Manag., vol. 70, no. 5, pp. 1881–1899, 2023, doi: 10.1109/TEM.2021.3098665.
[4] W. Li, Q. Li, L. Chen, F. Wu, and J. Ren, “A Storage Resource Collaboration Model Among Edge Nodes in Edge Federation Service,” IEEE Trans. Veh. Technol., vol. 71, no. 9, pp. 9212–9224, 2022, doi: 10.1109/TVT.2022.3179363.
[5] H. Rexha and S. Lafond, “Data Collection and Utilization Framework for Edge AI Applications,” in 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), 2021, pp. 105–108. doi: 10.1109/WAIN52551.2021.00023.
[6] S. Nimkar and M. M. Khanapurkar, “Edge Computing for IoT: A Use Case in Smart City Governance,” in 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 2021, pp. 1–5. doi: 10.1109/ICCICA52458.2021.9697263.
[7] N. Ghafoorianfar and M. Roopaei, “Environmental Perception in Autonomous Vehicles Using Edge Level Situational Awareness,” in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 444–448. doi: 10.1109/CCWC47524.2020.9031155.
[8] M. Pan, W. Su, and Y. Wang, “Review of Research on the Curriculum for Artificial Intelligence and Industrial Automation based on Edge Computing,” in 2021 International Conference on Networking and Network Applications (NaNA), 2021, pp. 222–226. doi: 10.1109/NaNA53684.2021.00045.
[9] A. Tariq, A. Lakas, F. M. Sallabi, T. Qayyum, M. A. Serhani, and E. Baraka, “Empowering Trustworthy Client Selection in Edge Federated Learning Leveraging Reinforcement Learning,” in 2023 IEEE/ACM Symposium on Edge Computing (SEC), 2023, pp. 372–377. doi: 10.1145/3583740.3626815.
[10] J.-C. Gamazo-Real, R. T. Fernández, and A. M. Armas, “Estimation of Air Quality Parameters using Lightweight Machine Learning on Low-cost Edge-IoT Architectures,” in 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 2022, pp. 348–352. doi: 10.1109/ISMODE56940.2022.10180952.
[11] S. Seid, M. Zennaro, M. Libsie, E. Pietrosemoli, and P. Manzoni, “A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring,” in 2020 16th International Conference on Mobility, Sensing and Networking (MSN), 2020, pp. 762–767. doi: 10.1109/MSN50589.2020.00130.
[12] M. D. Hossain et al., “Dynamic Task Offloading for Cloud-Assisted Vehicular Edge Computing Networks: A Non-Cooperative Game Theoretic Approach,” Sensors, vol. 22, no. 10, 2022, doi: 10.3390/s22103678.
[13] P. A. Budiman, Marfani, and D. M. Sari, “Multi-Access Edge Computing Implemention On Tower Ecosystem Indonesia: Challenges And Visibility,” in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022, pp. 158–162. doi: 10.1109/ICTC55196.2022.9952477.
[14] T. Liang, J. Glossner, L. Wang, S. Shi, and X. Zhang, “Pruning and quantization for deep neural network acceleration: A survey,” Neurocomputing, vol. 461, pp. 370–403, 2021, doi: https://doi.org/10.1016/j.neucom.2021.07.045.
[15] M. Ali, F. Naeem, M. Tariq, and G. Kaddoum, “Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 2, pp. 778–789, 2023, doi: 10.1109/JBHI.2022.3181823.
[16] L. Pioli, C. F. Dorneles, D. D. J. de Macedo, and M. A. R. Dantas, “An overview of data reduction solutions at the edge of IoT systems: a systematic mapping of the literature,” Computing, vol. 104, no. 8, pp. 1867–1889, 2022, doi: 10.1007/s00607-022-01073-6.
[17] L. EL-Garoui, S. Pierre, and S. Chamberland, “A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments,” Smart Cities, vol. 3, no. 3, pp. 1004–1021, 2020, doi: 10.3390/smartcities3030050.
Published
2024-02-29
How to Cite
[1]
A. Amrullah, “Optimization of AI-Powered Edge-IoT Architecture for Real-Time Response in Distributed Smart City Systems”, Intellithings Journal, vol. 1, no. 1, pp. 1-7, Feb. 2024.
Section
Articles
