The Impact of Data Analytics Based on Internet of Things, Edge Computing, and Artificial Intelligence on Energy Efficiency in Smart Environment
Source abstract
Abstract
This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Impact of AI and Big Data on Business and Society · Internet of Things and AI · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
Faculty of Production Technology and Management, Jan Evangelista Purkyně University, 400 96 Ústí nad Labem, Czech Republic
Funding
Kazimierz Wielki University
References (100+)
Show source-provided references
1. Kang, M., and Park, M. (2025). Power Estimation and Energy Efficiency of AI Accelerators on Embedded Systems. Energies, 18.10.3390/en18143840 ↗
2. Wu, H., Judd, P., Zhang, X., Isaev, M., and Micikevicius, P. (2020). Integer quantization for deep learning inference: Principles and empirical evaluation. arXiv.
3. Exploring Edge TPU for Deep Feed-Forward Neural Networks10.1016/j.iot.2023.100749 ↗
4. Tu, X., Mallik, A., Chen, D., Han, K., Altintas, O., Wang, H., and Xie, J. (2023, January 6–9). Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices. Proceedings of the 8th ACM/IEEE Symposium on Edge Computing, Wilmington, DE, USA.10.1145/3583740.3628442 ↗
5. Modern methods in the field of machine modeling and simulation as a research and practical issue related to Industry 4.0
6. Nabavinejad, S.M., and Salami, B. (2021). On the Impact of Device-Level Techniques on Energy-Efficiency of Neural Network Accelerators. arXiv.
7. Rojek, I., Mroziński, A., Kotlarz, P., Macko, M., and Mikołajewski, D. (2023). AI-Based Computational Model in Sustainable Transformation of Energy Markets. Energies, 16.10.3390/en16248059 ↗
8. Rojek, I., Mikołajewski, D., Mroziński, A., and Macko, M. (2023). Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage. Energies, 16.10.3390/en16186613 ↗
9. A Survey of Techniques for Improving Energy Efficiency in Embedded Computing Systems10.1504/IJCAET.2014.065419 ↗
10. Power optimization of variable voltage core-based systems10.1109/43.811318 ↗
11. Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., and Zurada, J.M. (2010). HybridNeuralNetworksasPredictionModels. Artificial Intelligence and Soft Computing; Lecture Notes in Artificial Intelligence, Springer.10.1007/978-3-642-13208-7 ↗
12. Wang, Y., Feng, Y., Xi, C., Wang, B., Tang, B., and Geng, Y. (2024). Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model. Energies, 17.10.3390/en17205205 ↗
13. Discussion on industrial internet platform construction based on power internet of things10.1088/1742-6596/1939/1/012077 ↗
14. Study on the reference architecture and assessment framework of industrial internet platform10.1109/ACCESS.2020.3021719 ↗
15. Strengthen or weaken? How industrial internet platform affects the core competitiveness of manufacturing companies10.1007/s12063-023-00426-4 ↗
16. Improve the industrial digital transformation through Industrial Internet platforms10.1007/s42524-023-0286-9 ↗
17. IoT in energy: A comprehensive review of technologies, applications, and future directions10.1007/s12083-024-01725-8 ↗
18. Drissi Elbouzidi, A., Ait El Cadi, A., Pellerin, R., Lamouri, S., Tobon Valencia, E., and Bélanger, M.J. (2023). The role of AI in warehouse digital twins: Literature review. Appl. Sci., 13.10.3390/app13116746 ↗
19. Rojek, I., Kotlarz, P., Dorożyński, J., and Mikołajewski, D. (2024). Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0. Electronics, 13.10.3390/electronics13101832 ↗
20. Flexible Intelligent Manufacturing Technology Solution Based on 5G Edge-Cloud Collaboration
21. Models for Better Environmental Intelligent Management within Water Supply Systems10.1007/s11269-014-0654-7 ↗
22. Comparison of different types of neuronal nets for failures location within water-supply networks
23. Research on Knowledge Graph System for Maintenance of Coal Mine Mechanical and Electrical Equipment
24. Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., and Larson, J. (2024). From local to global: A graph rag approach to query-focused summarization. arXiv.
25. Liu, S., Wen, T., Pattamatta, A.S.L., and Srolovitz, D.J. (2024). A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification. arXiv.10.1016/j.mattod.2024.08.028 ↗
26. Zakharov, D., Magaril, E., and Rada, E.C. (2018). Sustainability of the Urban Transport System under Changes in Weather and Road Conditions Affecting Vehicle Operation. Sustainability, 10.10.3390/su10062052 ↗
27. Cho, W., Lee, H., and Gu, J.-h. (2024). Optimization Techniques and Evaluation for Building an Integrated Lightweight Platform for AI and Data Collection Systems on Low-Power Edge Devices. Energies, 17.10.3390/en17071757 ↗
28. Firoozi, A.A., Hejazi, F., and Firoozi, A.A. (2024). Advancing Wind Energy Efficiency: A Systematic Review of Aerodynamic Optimization in Wind Turbine Blade Design. Energies, 17.10.3390/en17122919 ↗
29. Energy sharing transaction network establishment of industrial complex and integrated design method10.5855/ENERGY.2023.32.2.011 ↗
30. Merenda, M., Porcaro, C., and Iero, D. (2020). Edge machine learning for AI-enabled IoT devices: A review. Sensors, 20.10.3390/s20092533 ↗
31. Alajlan, N.N., and Ibrahim, D.M. (2022). TinyML: Enabling of inference deep learning models on ultra-low-power IoT edge devices for AI applications. Micromachines, 13.10.3390/mi13060851 ↗
32. Joint optimization risk factor and energy consumption in IoT networks with Tinyml-enabled internet of UAVs10.1109/JIOT.2023.3348837 ↗
33. Hu, J.-L., Li, Y., and Chew, J.-C. (2025). Industry 5.0 and Human-Centered Energy System: A Comprehensive Review with Socio-Economic Viewpoints. Energies, 18.10.3390/en18092345 ↗
34. Rojek, I., Mikołajewski, D., Kopowski, J., Bednarek, T., and Tyburek, K. (2025). Energy Efficiency and Sustainability of Additive Manufacturing as a Mass-Personalized Production Mode in Industry 5.0/6.0. Energies, 18.10.3390/en18133413 ↗
35. Lapegna, M., Balzano, W., Meyer, N., and Romano, D. (2021). Clustering algorithms on low-power and high-performance devices for edge computing environments. Sensors, 21.10.3390/s21165395 ↗
36. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1136/bmj.n71 ↗
37. AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives10.1109/COMST.2023.3338015 ↗
38. Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution10.1109/TITS.2020.2997832 ↗
39. Artificial-Intelligence-Enabled Intelligent 6G Networks10.1109/MNET.011.2000195 ↗
40. The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)10.1016/j.is.2021.101840 ↗
41. Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey10.1109/JIOT.2020.3028368 ↗
42. Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk10.1109/ACCESS.2025.3591883 ↗
43. Toor, A.A., Usman, M., Younas, F., Fong, A.C.M., Khan, S.A., and Fong, S. (2020). Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems. Sensors, 20.10.3390/s20072131 ↗
44. Toward Disaggregated and Heterogenous AI Systems
45. Chakaravarthy, R.V., Jiang, H., Chakravarthy, R., and Das, S. (2022, January 23–26). Auto-tuning of AI/ML Graphs for Optimal Performance in a Heterogenous Processor System. Proceedings of the 2022 IEEE 40th International Conference on Computer Design (ICCD), Lake Tahoe, CA, USA.10.1109/ICCD56317.2022.00040 ↗
46. Rahimi, I., and Patel, I. (2025). AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study. arXiv.
47. Fraga-Lamas, P., Lopes, S.I., and Fernández-Caramés, T.M. (2021). Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors, 21.10.3390/s21175745 ↗
48. Can Blockchain Strengthen the Internet of Things?10.1109/MITP.2017.3051335 ↗
49. Suárez-Albela, M., Fernández-Caramés, T.M., Fraga-Lamas, P., and Castedo, L. (2017). A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications. Sensors, 17.10.3390/s17091978 ↗
50. A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard10.1109/ACCESS.2018.2808326 ↗
51. The Benefits of Self-Awareness and Attention in Fog and Mist Computing10.1109/MC.2015.207 ↗
52. Fernández-Caramés, T.M., Fraga-Lamas, P., Suárez-Albela, M., and Vilar-Montesinos, M. (2018). A Fog Computing and Cloudlet Based Augmented Reality System for the Industry 4.0 Shipyard. Sensors, 18.10.3390/s18061798 ↗
53. Decentralized Consensus for Edge-Centric Internet of Things: A Review, Taxonomy, and Research Issues10.1109/ACCESS.2017.2779263 ↗
54. Fang, X., Wang, J., Song, G., Han, Y., Zhao, Q., and Cao, Z. (2020). Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling. Energies, 13.10.3390/en13010123 ↗
55. A survey on multi-agent reinforcement learning and its application
56. Canese, L., Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Giardino, D., Re, M., and Spanò, S. (2021). Multi-agent reinforcement learning: A review of challenges and applications. Appl. Sci., 11.10.3390/app11114948 ↗
57. Federated multiagent deep reinforcement learning approach via physics-informed reward for multimicrogrid energy management10.1109/TNNLS.2022.3232630 ↗
58. Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems10.1016/j.apenergy.2022.118575 ↗
59. Jia, D., Yang, X., Sheng, W., Liu, K., Jin, T., Li, X., and Dong, W. (2025). Hybrid Drive Simulation Architecture for Power Distribution Based on the Federated Evolutionary Monte Carlo Algorithm. Energies, 18.10.3390/en18215595 ↗
60. Alaraj, M., Radi, M., Alsisi, E., Majdalawieh, M., and Darwish, M. (2025). Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review. Energies, 18.10.3390/en18174779 ↗
61. Wang, Y., Huo, C., Xu, F., Zheng, L., and Hao, L. (2025). Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling. Energies, 18.10.3390/en18010197 ↗
62. Ji, P., Tao, L., Xue, Y., and Feng, L. (2025). Diffusion Neural Learning for Market Power Risk Assessment in the Electricity Spot Market. Energies, 18.10.3390/en18246542 ↗
63. Shi, X., and Guo, C. (2025). Novel Real-Time Power System Scheduling Based on Behavioral Cloning of a Grid Expert Strategy with Integrated Graph Neural Networks. Energies, 18.10.3390/en18081934 ↗
64. Graph neural network–based ai for solar farm and grid interaction modeling10.12732/ijam.v38i8s.559 ↗
65. Rodriguez-Casavilca, H.M., Mauricio, D., and Villanueva, J.M.M. (2025). Evolution of Artificial Intelligence-Based OT Cybersecurity Models in Energy Infrastructures: Services, Technical Means, Facilities and Algorithms. Energies, 18.10.3390/en18195163 ↗
66. Poursaeed, A.H., and Namdari, F. (2025). Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids. Energies, 18.10.3390/en18040908 ↗
67. Matsushima, F., Aoki, M., Nakamura, Y., Verma, S.C., Ueda, K., and Imanishi, Y. (2025). Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning. Energies, 18.10.3390/en18030653 ↗
68. Yousef, L.A., Yousef, H., and Rocha-Meneses, L. (2023). Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions. Energies, 16.10.3390/en16248057 ↗
69. The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction
70. Hybrid Artificial Intelligence System in Constraint Based Scheduling of Integrated Manufacturing ERP Systems
71. Evans, R., and Gao, J. (2025, December 14). DeepMind AI Reduces Google Data Centre Cooling Bill by 40%. Available online: https://deepmind.google/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40.
72. (2025, December 14). Interact City. Available online: https://www.assets.signify.com/is/content/PhilipsLighting/Assets/interact/global/20190904-led-road-lighting-and-group-monitoring-for-pune.pdf.
73. (2025, December 14). Unifying Sustainability Across a Retail Giant’s Diverse Network of Stores. Available online: https://www.se.com/ww/en/work/campaign/life-is-on/case-study/massmart/.
74. (2025, December 14). Microgrid. Available online: https://www.energystorage.ucsd.edu/microgrid.
75. (2025, December 14). Pathways to Purposeful Transformation Through Shared Value and Innovation. Available online: https://www.sbs.ox.ac.uk/sites/default/files/2021-05/enel-case-study.pdf.
76. (2025, December 14). AutoEdge.ai. Available online: https://autoedge.ai/resources/blog/hvac-case-study/.
77. Predicting Low-Cost Gas Sensor Readings From Transients Using Long Short-Term Memory Neural Networks10.1109/JIOT.2020.2990526 ↗
78. Dynamic monitoring frequency for energy-efficient data collection in Internet of Things10.1016/j.jocs.2022.101842 ↗
79. Physical-Layer Authentication for Ambient Backscatter Aided NOMA Symbiotic Systems10.1109/TCOMM.2023.3245659 ↗
80. Integrated Sensing and Communications for UAV Assisted Internet of Things Based on Deep Reinforcement Learning10.1109/TVT.2025.3539693 ↗
81. RIS-Assisted Beamfocusing in Near-Field IoT Communication Systems: A Transformer-Based Approach10.1109/JIOT.2025.3573717 ↗
82. Covert Communication of STAR-RIS Aided NOMA Networks10.1109/TVT.2024.3349543 ↗
83. Reddi, V.J., Plancher, B., Kennedy, S., Moroney, L., Warden, P., Agarwal, A., Banbury, C., Banzi, M., Bennett, M., and Brown, B. (2021). Widening Access to Applied Machine Learning with TinyML. arXiv.
84. Schwartz, R., Dodge, J., Smith, N.A., and Etzioni, O. (2019). Green AI. arXiv.
85. Efficient Energy Management for the Internet of Things in Smart Cities10.1109/MCOM.2017.1600218CM ↗
86. RF Energy Harvesting and Transfer for Spectrum Sharing Cellular IoT Communications in 5G Systems
87. Towards fourth industrial revolution impact: Smart product based on RFID technology10.1109/MIM.2017.7919135 ↗
88. An Intelligent Power Outlet System for the Smart Home of the Internet-of-Things10.1155/2015/214805 ↗
89. Khutsoane, O., Isong, B., and Abu-Mahfouz, A.M. (November, January 29). IoT devices and applications based on LoRa/LoRaWAN. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, Beijing, China.
90. A Novel Relay Node Placement and Energy Efficient Routing Method for Heterogeneous Wireless Sensor Networks10.1109/ACCESS.2020.2984495 ↗
91. Bagula, A., Abidoye, A.P., and Zodi, G.-A.L. (2016). Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things. Sensors, 16.10.3390/s16010009 ↗
92. An Energy-Efficient Architecture for the Internet of Things (IoT)10.1109/JSYST.2015.2469676 ↗
93. Online Distributed Offloading and Computing Resource Management with Energy Harvesting for Heterogeneous MEC-enabled IoT10.1109/TWC.2021.3076201 ↗
94. IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?10.1109/MSP.2018.2825478 ↗
95. Wireless Communication and Security Issues for Cyber-Physical Systems and the Internet-of-Things10.1109/JPROC.2017.2780172 ↗
96. Elliptic Curve Cryptography with Efficiently Computable Endomorphisms and Its Hardware Implementations for the Internet of Things10.1109/TC.2016.2623609 ↗
97. Energy-Efficient Cross-layer Spectrum Sharing in CR Green IoT Networks10.1109/TGCN.2021.3076695 ↗
98. Blockchain Technology Toward Green IoT: Opportunities and Challenges10.1109/MNET.001.1900526 ↗
99. Fernández-Caramés, T.M., Fraga-Lamas, P., Suárez-Albela, M., and Díaz-Bouza, M.A. (2018). A Fog Computing Based Cyber-Physical System for the Automation of Pipe-Related Tasks in the Industry 4.0 Shipyard. Sensors, 18.10.3390/s18061961 ↗
100. Federated Cooperation and Augmentation for Power Allocation in Decentralized Wireless Networks10.1109/ACCESS.2020.2979323 ↗