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Edge Computing Enabled Real-Time Internet of Things Data Processing and Analytics Architecture

Mathews John

Pittsburgh, PA, USA

Abstract

The rapid growth of Internet of Things (IoT) devices has generated an unprecedented volume of data requiring efficient processing, storage, and analysis. Traditional cloud-centric architectures often face limitations such as high latency, bandwidth consumption, network congestion, and security vulnerabilities when handling real-time IoT applications. Edge computing has emerged as a transformative paradigm that brings computational resources closer to IoT devices, enabling low-latency and context-aware data processing. This study explores an edge computing enabled real-time IoT data processing and analytics architecture designed to improve operational efficiency, scalability, responsiveness, and security in distributed environments. The proposed architecture integrates IoT sensors, edge gateways, local analytics modules, and cloud infrastructure to facilitate seamless data acquisition, filtering, processing, and decision-making. The study evaluates the role of edge intelligence in applications such as smart healthcare, industrial automation, smart transportation, and smart cities. Furthermore, it examines challenges related to interoperability, energy efficiency, privacy protection, and resource management within edge-based IoT ecosystems. The research methodology includes architectural modeling, comparative analysis, simulation-based evaluation, and performance measurement using latency, throughput, and reliability metrics. The findings demonstrate that edge computing significantly enhances real-time IoT analytics performance while reducing dependency on centralized cloud systems.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 3 No. 3 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2018.0303001

Pages

3539-3548

Published

May 8, 2018
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mathews John (%2018). Edge Computing Enabled Real-Time Internet of Things Data Processing and Analytics Architecture. International Journal of Emerging Trends in Engineering and Management Research , Vol. 3 No. 3 (2018): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 3539-3548. https://doi.org/10.15662/ijetemr.2018.0303001

References

1. Ansari, N., & Sun, X. (2017). Mobile edge computing empowers Internet of Things. IEEE Network, 32(1), 1–7.
2. Assuncao, M. D., Veith, A., & Buyya, R. (2017). Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. Journal of Network and Computer Applications, 103, 1–17.
3. Calo, S., Touma, M., Verma, D., & Cullen, A. (2017). Edge computing architecture for applying AI to IoT. In Proceedings of IEEE International Conference on Big Data 2017.
4. Cao, H., Wachowicz, M., & Cha, S. (2017). Developing an edge computing platform for real-time descriptive analytics. IEEE International Conference Proceedings.
5. Choochotkaew, S., Yamaguchi, H., Shibuya, M., Hasegawa, T., & Higashino, T. (2017). EdgeCEP: Fully-distributed complex event processing on IoT edges. IEEE DCOSS Conference Proceedings.
6. Harth, N., Anagnostopoulos, C., & Pezaros, D. (2017). Predictive intelligence to the edge: Impact on edge analytics. Evolving Systems, 9(2), 95–118.
7. Munir, A., Kansakar, P., & Khan, S. U. (2017). IFCIoT: Integrated fog cloud IoT architectural paradigm for future Internet of Things. Future Generation Computer Systems, 78, 1–14.
8. Nastic, S., Rausch, T., Scekic, O., Dustdar, S., Gusev, M., Koteska, B., Kostoska, M., Jakimovski, B., Ristov, S., & Prodan, R. (2017). A serverless real-time data analytics platform for edge computing. IEEE Internet Computing, 21(4), 64–71.
9. Oyekanlu, E. (2017). Predictive edge computing for time series of industrial IoT and large-scale critical infrastructure based on open-source software analytic of big data. In IEEE International Conference on Big Data.
10. Rehman, M. H., Jayaraman, P. P., Malik, S. U. R., Khan, A. U. R., & Gaber, M. M. (2017). RedEdge: A novel architecture for big data processing in mobile edge computing environments. Journal of Sensor and Actuator Networks, 6(3), 17.
11. Shukla, A., Chaturvedi, S., & Simmhan, Y. (2017). RIoTBench: A real-time IoT benchmark for distributed stream processing platforms. Future Generation Computer Systems, 78, 1–14.
12. Ta-Shma, P., Akbar, A., Gerson-Golan, G., Hadash, G., Carrez, F., & Moessner, K. (2017). An ingestion and analytics architecture for IoT applied to smart city use cases. IEEE Internet of Things Journal, 5(2), 765–774.
13. Yu, W., Liang, F., He, X., Hatcher, W., Lu, C., Lin, J., & Yang, X. (2017). A survey on edge computing for the Internet of Things. IEEE Access, 6, 6900–6919.
14. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
15. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
16. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
17. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
18. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
19. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
20. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
21. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
22. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
23. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
24. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
25. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
26. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
27. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
28. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
29. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
30. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
31. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
32. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
33. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
34. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
35. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
36. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
37. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
38. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
39. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926