INTELLIGENT DATA PROCESSING ECOSYSTEMS: INTEGRATING IOT, CLOUD, AND EDGE COMPUTING WITH ARTIFICIAL INTELLIGENCE FOR NEXT-GENERATION SMART SYSTEMS
- Authors
-
-
Dr. Tamara L. Shields
Department of Communication, Central Michigan University, Mount Pleasant, MI, USAAuthor -
Dr. Ethan C. Monroe
Department of Political Science, Wichita State University, Wichita, KS, USAAuthor
-
- Keywords:
- Internet of Things, Artificial Intelligence, Edge Computing, Cloud Computing
- Abstract
-
The convergence of Internet of Things (IoT), cloud computing, edge computing, and artificial intelligence (AI) technologies has created unprecedented opportunities for intelligent data processing and automated decision-making across various domains. This comprehensive review examines the synergistic integration of these technologies, analyzing their architectural frameworks, implementation challenges, and real-world applications. The proliferation of IoT devices, expected to reach billions of connections globally [3], necessitates sophisticated data processing paradigms that can handle massive volumes of heterogeneous data while ensuring real-time responsiveness and energy efficiency. This study investigates how cloud and edge computing infrastructures serve as foundational platforms for deploying AI algorithms, enabling intelligent data analytics from sensor networks to actionable insights. Through systematic analysis of current literature and emerging trends, we identify key challenges including security vulnerabilities, resource constraints, scalability issues, and ethical considerations in AI deployment. The findings reveal that federated learning, model compression techniques, and distributed computing architectures are critical enablers for successful IoT-AI integration. Furthermore, the research highlights the importance of standardized protocols, robust security frameworks, and energy-efficient algorithms in creating sustainable intelligent ecosystems. This work contributes to understanding the technological landscape of integrated IoT-AI systems and provides insights for future research directions in autonomous computing environments.
- Downloads
-
Download data is not yet available.
- References
-
[1] Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A survey. Comput. Netw. 2010, 54, 2787–2805. [CrossRef]
[2] Ersöz, B.; Oyucu, S.; Aksöz, A.; Sağıroğlu, Ş.; Biçer, E. Interpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights. Appl. Sci. 2024, 14, 10816. [CrossRef]
[3] Vailshery, L.S. Number of IoT Connections Worldwide 2022–2033. 2024. Available online: https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/ (accessed on 2 December 2024).
[4] Lombardi, M.; Pascale, F.; Santaniello, D. Internet of Things: A General Overview between Architectures, Protocols and Applications. Information 2021, 12, 87. [CrossRef]
[5] Ali, O.; Ishak, M.K.; Bhatti, M.K.L.; Khan, I.; Kim, K.I. A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface. Sensors 2022, 22, 995. [CrossRef] [PubMed]
[6] Buyya, R.; Yeo, C.S.; Venugopal, S.; Broberg, J.; Brandic, I. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 2009, 25, 599–616. [CrossRef]
[7] Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [CrossRef]
[8] Armbrust, M.; Fox, A.; Griffith, R.; Joseph, A.D.; Katz, R.; Konwinski, A.; Lee, G.; Patterson, D.; Rabkin, A.; Stoica, I.; et al. A view of cloud computing. Commun. ACM 2010, 53, 50–58. [CrossRef]
[9] Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016, 3, 637–646. [CrossRef]
[10] Satyanarayanan, M. The Emergence of Edge Computing. Computer 2017, 50, 30–39. [CrossRef]
[11] Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv 2015, arXiv:1603.04467.
[12] Khan, W.Z.; Ahmed, E.; Hakak, S.; Yaqoob, I.; Ahmed, A. Edge computing: A survey. Future Gener. Comput. Syst. 2019, 97, 219–235. [CrossRef]
[13] Mandalapu, V.; Elluri, L.; Vyas, P.; Roy, N. Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions. IEEE Access 2023, 11, 60153–60170. [CrossRef]
[14] Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA. 2016. Available online: http://www.deeplearningbook.org (accessed on 2 December 2024).
[15] Dorton, S.L.; Ministero, L.M.; Alaybek, B.; Bryant, D.J. Foresight for ethical AI. Front. Artif. Intell. 2023, 6, 1143907. [CrossRef]
[16] Andriulo, F.C.; Fiore, M.; Mongiello, M.; Traversa, E.; Zizzo, V. Edge Computing and Cloud Computing for Internet of Things: A Review. Informatics 2024, 11, 71. [CrossRef]
[17] Hamdan, S.; Ayyash, M.; Almajali, S. Edge-Computing Architectures for Internet of Things Applications: A Survey. Sensors 2020, 20, 6441. [CrossRef]
[18] Rupanetti, D.; Kaabouch, N. Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities. Appl. Sci. 2024, 14, 7104. [CrossRef]
[19] Javadpour, A.; Ja'Fari, F.; Taleb, T.; Benzaïd, C.; Rosa, L.; Tomás, P.; Cordeiro, L. Deploying Testbed Docker-based application for Encryption as a Service in Kubernetes. In Proceedings of the 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 26–28 September 2024; pp. 1–7. [CrossRef]
[20] Tian, Y.; Wang, S.; Xiong, J.; Bi, R.; Zhou, Z.; Bhuiyan, M.Z.A. Robust and Privacy-Preserving Decentralized Deep Federated Learning Training: Focusing on Digital Healthcare Applications. IEEE/ACM Trans. Comput. Biol. Bioinform. 2024, 21, 890–901. [CrossRef]
[21] Rauniyar, A.; Hagos, D.H.; Jha, D.; Håkegård, J.E.; Bagci, U.; Rawat, D.B.; Vlassov, V. Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. J. Med. Syst. 2024, 48, 1. [CrossRef]
[22] Han, S.; Mao, H.; Dally, W.J. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv 2015, arXiv:1510.00149.
[23] Jacob, B.; Kligys, S.; Chen, B.; Zhu, M.; Tang, M.; Howard, A.; Adam, H.; Kalenichenko, D. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 18–23 June 2018; pp. 2704–2713.
[24] Gill, S.S.; Tuli, S.; Xu, M.; Singh, I.; Singh, K.V.; Lindsay, D.; Tuli, S.; Smiraglia, D.; Foglino, F.; Sfirakis, F.; et al. Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and future directions. Internet Things 2019, 8, 100118. [CrossRef]
[25] Tuli, S.; Mahmud, R.; Tuli, S.; Buyya, R. FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing. J. Syst. Softw. 2019, 154, 22–36. [CrossRef]
- Downloads
- Published
- 2024-12-28
- Section
- Articles
How to Cite
Similar Articles
- Dr. Caroline S. Whitaker, Dr. Daniel K. Monroe, A COMPREHENSIVE FRAMEWORK FOR SECURE AND PRIVATE SMART HOME INFRASTRUCTURE USING DECENTRALIZED EDGE AI , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Nicole A. Jennings, Dr. Samuel B. Kirkland, ACCELERATING URBAN INTELLIGENCE: A FRAMEWORK FOR REAL-TIME EDGE ANALYTICS IN IOT-DRIVEN SMART CITIES , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Hannah J. Cole, Dr. Derek S. Vaughn, FOG COMPUTING: A CATALYST FOR REAL-TIME INTERNET OF THINGS APPLICATIONS , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Jessica L. Monroe, Dr. Eric D. Langford, THE INTEGRATION OF INTERNET OF THINGS, BIG DATA ANALYTICS, AND CLOUD COMPUTING TECHNOLOGIES FOR REAL-TIME APPLICATION DEVELOPMENT , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Caroline M. Rivers, Dr. Jared E. Nolan, MULTI-LAYERED FEATURE MODELS FOR ENHANCED IOT APPLICATION DEPLOYMENT IN EDGE ENVIRONMENTS , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Dr. Elena J. Foster, Dr. Marcus T. Delgado, THE 6G CONTINUUM: A PLATFORM ARCHITECTURE FOR REAL-TIME INDUSTRIAL DIGITAL TWINS , European Journal of Emerging Real-Time IoT and Edge Infrastructures: Vol. 1 No. 01 (2024): Volume 01 Issue 01
You may also start an advanced similarity search for this article.