AI and Edge Analytics for Real-Time IoT Decision-Making in SMACEnvironments: A Comprehensive Review
DOI:
https://doi.org/10.69996/jsihs.2025016Keywords:
AI, IoT, Social, Mobile, Analytics, Edge, CloudAbstract
There is a pressing need for intelligent, real-time decision-making at the network's edge due to the fast development of the Internet of Things (IoT) and the rising needs of SMAC environments. This article provides a thorough analysis of how AI and Edge Analytics are revolutionizing IoT systems through the ability to make autonomous decisions with low latency and localization. By deploying AI models, such as machine learning and deep learning closer to data sources, edge computing minimizes dependence on centralized cloud infrastructure, thereby improving response time, privacy, and bandwidth utilization. This article analyzes various AI-driven edge architectures, processing techniques, and deployment models tailored for SMAC-integrated IoT applications. It also explores the challenges associated with limited computational resources, data heterogeneity, and security constraints at the edge. Through an in-depth survey of current literature, this work highlights existing solutions, identifies research gaps, and proposes future directions for building efficient, scalable, and intelligent edge-based IoT systems. This review aims to guide researchers and practitioners in leveraging AI and edge analytics for next-generation real-time IoT decision-making.
References
[1] A. Alzahrani, P. Kostkova, H. Alshammari, S. Habibullah and A. Alzahrani, “Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity,” Alexandria Engineering Journal, vol.127, 522-540, 2025.
[2] L. Harris, “The Impact of Artificial Intelligence on Securing Internet of Things (IoT) Devices in Smart Environments,” Internet of Things (IoT), 2025.
[3] K.K. Vaigandla, “Communication technologies and challenges on 6G networks for the Internet: Internet of Things (IoT) based analysis,” In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), vol. 2, pp. 27-31, 2022.
[4] B.Kelly, “The impact of edge computing on real-time data processing,” International Journal of Computing and Engineering, vol.5, no.5, pp.44-58, 2024.
[5] K.K. Vaigandla, “Role of IoT and ML in Healthcare,” Babylonian Journal of Artificial Intelligence, vol.2025, pp.23-36, 2025.
[6] C. Ramakrishna, "A Smart System for Future Generation based on the Internet of Things Employing Machine Learning, Deep Learning, and Artificial Intelligence: Comprehensive Survey." Int. J. Recent Innov. Trends Comput. Commun, vol.11, no.9, pp.1798-1815, 2023.
[7] P. Sunhare, R.R. Chowdhary and M.K. Chattopadhyay, “Internet of things and data mining: An application oriented survey,” Journal of King Saud University-Computer and Information Sciences, vol.34, no.6, pp.3569-3590, 2022.
[8] M. Langen, “Social-mobile-analytics-cloud: a digital ecosystem for innovation,” In 2016 International Conference on Engineering, Technology and Innovation/IEEE lnternational Technology Management Conference (ICE/ITMC), pp. 1-5, 2016.
[9] P. Karthikeyan and M. Thangavel, “Applications of security, mobile, analytic, and cloud (SMAC) technologies for effective information processing and management,” IGI Global, 2018.
[10] S. Mendhurwar and R. Mishra, “Emerging synergies between Internet of Things and social technologies,” Journal of Global Information Technology Management, vol.21, no.2, pp.75-80, 2018.
[11] K. M. Law, A.W. Ip, B.B. Gupta and S. Geng, “Managing IoT and mobile technologies with innovation, trust, and sustainable computing,” CRC Press, 2021.
[12] E. Ahmed, I. Yaqoob, I.A.T. Hashem, I. Khan, A.I.A. Ahmed, M. Imran and A. Vasilakos, “The role of big data analytics in Internet of Things,” Computer Networks, vol.129, pp.459-471, 2017.
[13] H. Tyagi and R. Kumar, “Cloud computing for IoT,” In Internet of Things (IoT) Concepts and Applications, pp. 25-41, 2020.
[14] S. Chen, L. Zhang, Y.Tang, C. Shen, R. Kumar, K. Yu et al., “Indoor temperature monitoring using wireless sensor networks: A SMAC application in smart cities,” Sustainable Cities and Society, vol.61, 102333, 2020.
[15] A. Karras, A. Giannaros, C. Karras, K.C. Giotopoulos, D. Tsolis and S. Sioutas, “Edge artificial intelligence in large-scale IoT systems, applications, and big data infrastructures,” In 2023 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1-8, 2023.
[16] J. Zhang, Z. Qu, C. Chen, H. Wang, Y. Zhan, B. Ye and S. Guo, “Edge learning: The enabling technology for distributed big data analytics in the edge,” ACM Computing Surveys (CSUR), vol.54, no.7, pp.1-36, 2021.
[17] A. Hemmati, H.M. Arzanagh and A.M. Rahmani, “Fundamentals of Edge AI and Federated Learning,” Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications, pp.1-23, 2025.
[18] J.M. Tien, “Internet of things, real-time decision making, and artificial intelligence,” Annals of Data Science, vol.4, no.2, pp.149-178, 2017.
[19] H. Afreen and I.S. Bajwa, “An IoT-based real-time intelligent monitoring and notification system of cold storage,” IEEE Access, vol.9, pp.38236-38253, 2021.
[20] K.K.Vaigandla, R. Karne, M.K. Vanteru, M. Devsingh, D. Prasad, R.K. Siddoju and N. Dharavath, “Artificial Intelligence in Industrial IoT: Trends, Challenges, and Future Directions,” Journal of Computer Allied Intelligence, vol.3, no.2, pp.37-55, 2025.
[21] K.K. Vaigandla, “An Extensive Examination on IOT and Industrial IOT: Attacks, Security, Detection Methods, Blockchain Solutions and Challenges,” Babylonian Journal of Internet of Things, vol.2025, pp.89-100, 2025.
[22] Vaigandla, Karthik Kumar, Madhu Kumar Vanteru and Mounika Siluveru. "An extensive examination of the IoT and blockchain technologies in relation to their applications in the healthcare industry," Mesopotamian Journal of Computer Science, vol.2024, pp.1-14, 2024.
[23] A.A. Al Atawi, “Enhancing data management and real‐time decision making with IoT, cloud, and fog computing,” IET Wireless Sensor Systems, vol.14, no.6, pp.539-562, 2024.
[24] V. Vijayagopal, N. Chidambararaj, M. Kavitha, D. Sundrani, K.K. Vaigandla and B. Varadharajan, “Machine Learning for Natural Disaster Prediction and Prevention,” In Exploring Psychology, Social Innovation and Advanced Applications of Machine Learning, pp. 209-230, 2025.
[25] C. Banapuram, A. C. Naik, M.K. Vanteru, V.S. Kumar and K.K. Vaigandla, “A comprehensive survey of machine learning in healthcare: Predicting heart and liver disease, tuberculosis detection in chest X-ray images,” SSRG International Journal of Electronics and Communication Engineering, vol.11, no.5, pp.155-169, 2024.
[26] N. Sivapriya, R. Mohandas and K.K. Vaigandla, “A qos perception routing protocol for manets based on machine learning,” International Journal of Intelligent Systems and Applications in Engineering, vol.12, no.1, pp.733-745, 2023.
[27] K.K. Vaigandla, T. Mounika, N. Azmi, U. Urooj, K. Chenigaram and R.K. Karne, “Investigation on machine learning towards future generation communications,” In AIP Conference Proceedings, vol. 2965, no. 1, p. 030001, 2024.
[28] A. Karne, R. Karne, M.V.K. Kumar and A. Arunkumar, “Convolutional neural networks for object detection and recognition,” Journal of Artificial Intelligence, Machine Learning and Neural Network, vol.3, no.3, pp.1-13, 2023.
[29] S. Hamdan, M. Ayyash and S. Almajali, “Edge-computing architectures for internet of things applications: A survey,” Sensors, vol.20, no.22, pp.6441, 2020.
[30] K.K. Vaigandla and M. Siluveru, “Fog Computing with Internet of Things: An Overview of Architecture, Algorithms, Challenges and Applications,” Journal of Engineering and Technology (JET), vol.14, no.1, pp.187-220, 2023.
[31] C. Yang, S. Lan, L. Wang, W. Shen and G.G. Huang, “Big data driven edge-cloud collaboration architecture for cloud manufacturing: a software defined perspective,” IEEE access, vol.8, pp.45938-45950, 2020.
[32] M.M. John, H.H. Olsson and J. Bosch, “AI on the edge: Architectural alternatives,” In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 21-28, 2020.
[33] A. Singh, S.C. Satapathy, A. Roy and A. Gutub, “Ai-based mobile edge computing for iot: Applications, challenges, and future scope,” Arabian Journal for Science and Engineering, vol.47, no.8, pp.9801-9831, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Sensors, IoT & Health Sciences (JSIHS,ISSN: 2584-2560)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
