Exploring the Role of Edge and Cloud Computing in Enhancing Data Analytics for IoT Ecosystems

Sudhakar Babu S

Authors

Sudhakar Babu S Cloud Architect, Bangalore, India Author

Keywords:

Edge Computing, Cloud Computing, IoT, Data Analytics, Hybrid Computing, Latency Reduction, 5G

Abstract

The integration of edge and cloud computing is revolutionizing the Internet of Things (IoT) by addressing the challenges of real-time data processing, storage, and analysis. This paper investigates the synergistic role of edge and cloud computing in optimizing data analytics for IoT ecosystems, emphasizing their ability to manage vast data streams efficiently. By reviewing state-of-the-art research, the study highlights key advantages, limitations, and practical use cases of the hybrid computing paradigm. The findings demonstrate improved data reliability, reduced latency, and cost efficiency in IoT applications. Finally, the paper explores future opportunities for leveraging emerging technologies such as machine learning and 5G to further enhance IoT ecosystems.

References

Satyanarayanan, Mahadev. "The Emergence of Edge Computing." Computer, vol. 50, no. 1, 2017, pp. 30-39.

Zhang, Q., Yang, L. T., Chen, Z., and Li, P. "A Survey on Deep Learning for Big Data." Information Fusion, vol. 42, 2020, pp. 146-157.

Lekkala, C. (2022). Automating Infrastructure Management with Terraform: Strategies and Impact on Business Efficiency. European Journal of Advances in Engineering and Technology, 9(11), 82–88.

Marjani, Marzieh, Nasaruddin, Fatimah, Gani, Abdullah, et al. "Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges." IEEE Access, vol. 5, 2017, pp. 5247-5261.

Shi, Weisong, Cao, Jie, Zhang, Quan, et al. "Edge Computing: Vision and Challenges." IEEE Internet of Things Journal, vol. 3, no. 5, 2016, pp. 637-646.

Lekkala, C. (2023). Containerization vs. Serverless Architectures for Data Pipelines. Journal of Engineering and Applied Sciences Technology, 5(1), 1–5.

Zhou, Z., Zhang, P., Liu, J., et al. "Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing." Proceedings of the IEEE, vol. 107, no. 8, 2019, pp. 1738-1762.

Xu, Weizhong, Wang, Y., and Zhou, Ming. "A Survey on Edge Computing: Architectures, Applications, and Approaches." International Journal of High-Performance Computing, vol. 35, no. 3, 2021, pp. 298-314.

Lekkala, C. (2023). Adopting Low-Code Platforms for Data Pipeline Development in Cloud Environments. International Journal of Science and Research (IJSR), 12(2), 1730–1736.

Huang, C., Lin, Y., and Wu, J. "Enhancing IoT Data Processing Through Collaborative Edge-Cloud Computing." Journal of Network and Computer Applications, vol. 124, 2019, pp. 45-56.

Yousefpour, Amin, Patil, Sachin, and Zhang, Jian. "FOG Computing for IoT: Security and Privacy Issues." IEEE Communications Surveys & Tutorials, vol. 21, no. 2, 2019, pp. 2126-2158.

Buyya, Rajkumar, Srirama, Satish Narayana, and Sarker, Iqbal H. "Edge and Cloud Computing Integration for IoT Data Analytics." Future Generation Computer Systems, vol. 101, 2020, pp. 1-12.

Lekkala, C. (2023). Leveraging Reinforcement Learning for Autonomous Data Pipeline Optimization and Management. International Journal of Science and Research (IJSR), 12(5), 2667–2674.

Chen, Jian, Ran, Xian, and Lee, Kang. "Deep Learning with Edge Computing: A Review." Proceedings of the IEEE, vol. 106, no. 5, 2018, pp. 874-893.

Gubbi, Jayavardhana, Buyya, Rajkumar, Marusic, Slaven, and Palaniswami, Marimuthu. "Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions." Future Generation Computer Systems, vol. 29, no. 7, 2017, pp. 1645-1660.

Lekkala, C. (2023). Deploying and Managing Containerized Data Workloads on Amazon EKS. Journal of Artificial Intelligence & Cloud Computing, 2(2), 1–5.

Santos, Roberto, Gomes, Paulo, and da Silva, Antônio. "Improving IoT Systems with Edge Computing: A Systematic Literature Review." Journal of Systems and Software, vol. 175, 2021, p. 110903.