AI at the Edge: Cloud-Edge Synergy


Authors : Kishan Raj Bellala

Volume/Issue : Volume 10 - 2025, Issue 5 - May


Google Scholar : https://tinyurl.com/3y7y8ruf

DOI : https://doi.org/10.38124/ijisrt/25may967

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The new paradigm of AI at the Edge and its synergy with Cloud Computing to combine the advantages of local data processing and analysis with the scalability and resources offered by cloud systems is explained in this paper. AI capabilities deployed at the Edge enable real-time decision-making, reduced latency, and improved efficiency across various applications including healthcare, smart cities, industrial automation, and autonomous vehicles. Organizations can achieve maximum computational power and network bandwidth optimization and system performance enhancement through the combined strengths of Edge Computing and Cloud Computing. The advancement comes with security challenges and data privacy risks and requirements for effortless Edge-Cloud system integration. This paper conducts a thorough analysis of AI at the Edge with Cloud-Edge synergy use cases and advantages and limitations and future trends to explain the transformative potential of this relationship in artificial intelligence and computing.

Keywords : Edge Computing, Cloud Computing, AI Applications, Real-Time Processing, Data Privacy, and System Performance, Shedding Light on the Transformative Potential of this Symbiotic Relationship.

References :

  1. Manduva, V. C. (2021). Optimizing AI Workflows: The Synergy of Cloud Computing and Edge Devices. International Journal of Modern Computing, 4(1), 50-68.
  2. Sun, P. (2023). Cloud-Edge-Network-Device Synergy, and Convergence of Communication, Sensing, and Computing. In A Guidebook for 5GtoB and 6G Vision for Deep Convergence (pp. 331-336). Singapore: Springer Nature Singapore.
  3. REDDY, P. (2023). AI and Edge Computing: Synergistic Approaches for Real-time Data Processing in Cloud Environments.
  4. Chennupati, N. S. (2025). Edge-Cloud Synergy in Real-Time AI Applications: Opportunities, Implementations, and challenges. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 11(2), 2524–2539. https://doi.org/10.32628/cseit25112740
  5. Sathupadi, K., Achar, S., Bhaskaran, S. V., Faruqui, N., Abdullah-Al-Wadud, M., & Uddin, J. (2024). Edge-cloud synergy for AI-enhanced sensor network data: A real-time predictive maintenance framework. Sensors, 24(24), 7918.
  6. Zou, Z., Jin, Y., Huan, Y., Nevalainen, P., Heikkonen, J., & Westerlund, T. (2019). Edge and Fog Computing Enabled AI for IoT-An Overview. 51–56. https://doi.org/10.1109/aicas.2019.8771621
  7. Rong, G., Fan, H., Xu, Y., & Tong, X. (2021). An edge-cloud collaborative computing platform for building AIoT applications efficiently. Journal of Cloud Computing, 10(1). https://doi.org/10.1186/s13677-021-00250-w
  8. Rane, J., Mallick, S. K., Kaya, Ö., & Rane, N. L. (2024). Artificial intelligence, machine learning, and deep learning in cloud, edge, and quantum computing: A review of trends, challenges, and future directions. deep science. https://doi.org/10.70593/978-81-981271-0-5_1
  9. Gong, C., Gong, X., Lu, Y., & Lin, F. (2020). Intelligent Cooperative Edge Computing in Internet of Things. IEEE Internet of Things Journal, 7(10), 9372–9382. https://doi.org/10.1109/jiot.2020.2986015
  10. Rane, J., Mallick, S. K., Kaya, Ö., & Rane, N. L. (2024). Artificial intelligence, machine learning, and deep learning in cloud, edge, and quantum computing: A review of trends, challenges, and future directions. deep science. https://doi.org/10.70593/978-81-981271-0-5_1
  11. Wang, X., Chen, M., Wang, C., Zhao, Q., Han, Y., & Chen, X. (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
  12. Zhu, S., Ota, K., & Dong, M. (2022). Energy-Efficient Artificial Intelligence of Things with Intelligent Edge. IEEE Internet of Things Journal, 9(10), 7525–7532. https://doi.org/10.1109/jiot.2022.3143722
  13. Singh, R., & Gill, S. S. (2023). Edge AI: A survey. Internet of Things and Cyber-Physical Systems, 3, 71–92. https://doi.org/10.1016/j.iotcps.2023.02.004
  14. Badidi, E. (2023). Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions. Future Internet, 15(11), 370. https://doi.org/10.3390/fi15110370
  15. Chen, Z., Lan, D., Mao, Z., He, Q., Chung, H.-M., & Liu, L. (2019). An Artificial Intelligence Perspective on Mobile Edge Computing. 100–106. https://doi.org/10.1109/smartiot.2019.00024
  16. Zangana, H. M., & Zeebaree, S. R. M. (2024). Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 5(1), 11–30. https://doi.org/10.34010/injiiscom.v5i1.11883
  17. Shi, Y., Yang, K., Zhang, J., Letaief, K. B., & Jiang, T. (2020). Communication-Efficient Edge AI: Algorithms and Systems. IEEE Communications Surveys & Tutorials, 22(4), 2167–2191. https://doi.org/10.1109/comst.2020.3007787
  18. Yao, J., Wang, F., Jia, K., Zhang, F., Yao, Y., Zhang, S., Wu, A., Shen, T., Chu, Y., Ma, J., Zhang, J., Tan, Z., Yang, H., Ji, L., Wu, F., Kuang, K., Zhou, J., & Wu, C. (2022). Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI. IEEE Transactions on Knowledge and Data Engineering, 1. https://doi.org/10.1109/tkde.2022.3178211
  19. Banjanović-Mehmedović, L., & Husaković, A. (2023, October 1). Edge AI: Reshaping the Future of Edge Computing with Artificial Intelligence. https://doi.org/10.5644/pi2023.209.07
  20. Torres, D. R., Martín, C., Rubio, B., & Díaz, M. (2021). An open-source framework based on Kafka-ML for Distributed DNN inference over the Cloud-to-Things continuum. Journal of Systems Architecture, 118, 102214. https://doi.org/10.1016/j.sysarc.2021.102214
  21. Rupanetti, D., & Kaabouch, N. (2024). Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities. Applied Sciences, 14(16), 7104. https://doi.org/10.3390/app14167104
  22. Zou, Z., Jin, Y., Huan, Y., Nevalainen, P., Heikkonen, J., & Westerlund, T. (2019). Edge and Fog Computing Enabled AI for IoT-An Overview. 51–56. https://doi.org/10.1109/aicas.2019.8771621
  23. Xu, Z., Liu, W., Tan, H., Lu, J., Huang, J., & Yang, C. (2020). Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey. Security and Communication Networks, 2020, 1–13. https://doi.org/10.1155/2020/8872586

The new paradigm of AI at the Edge and its synergy with Cloud Computing to combine the advantages of local data processing and analysis with the scalability and resources offered by cloud systems is explained in this paper. AI capabilities deployed at the Edge enable real-time decision-making, reduced latency, and improved efficiency across various applications including healthcare, smart cities, industrial automation, and autonomous vehicles. Organizations can achieve maximum computational power and network bandwidth optimization and system performance enhancement through the combined strengths of Edge Computing and Cloud Computing. The advancement comes with security challenges and data privacy risks and requirements for effortless Edge-Cloud system integration. This paper conducts a thorough analysis of AI at the Edge with Cloud-Edge synergy use cases and advantages and limitations and future trends to explain the transformative potential of this relationship in artificial intelligence and computing.

Keywords : Edge Computing, Cloud Computing, AI Applications, Real-Time Processing, Data Privacy, and System Performance, Shedding Light on the Transformative Potential of this Symbiotic Relationship.

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