Enhancing IOT Energy Efficiency Using Distributed Edge Computing Mechanisms

The rapid expansion of the Internet of Things (IOT) has significantly increased the number of interconnected smart devices across healthcare, agriculture, smart cities, industrial automation, and transportation systems. However, the continuous operation and data transmission of IoT devices result in high energy consumption, limited battery lifespan, increased network congestion, and reduced system efficiency. Traditional cloud-centric architectures often introduce higher latency and excessive energy utilization due to long-distance communication and centralized data processing. To address these challenges, this paper presents a distributed edge computing mechanism for enhancing energy efficiency in IoT environments. The proposed framework utilizes decentralized edge nodes to process, filter, and manage data closer to IoT devices, thereby reducing communication overhead, minimizing latency, and optimizing computational resource allocation. The framework integrates intelligent task scheduling, adaptive load balancing, and energy-aware data processing techniques to improve overall system performance while conserving device energy. Experimental analysis demonstrates that the proposed approach significantly reduces energy consumption, improves response time, enhances network reliability, and extends the operational lifetime of IoT devices compared with conventional cloud-based models. The findings indicate that distributed edge computing provides an effective and scalable solution for achieving sustainable and energy-efficient IoT ecosystems in next-generation smart applications.

Internet of Things (IoT), Edge Computing, Energy Efficiency, Distributed Computing, Smart Devices, Energy Optimization, Task Scheduling, Load Balancing, Sustainable IoT, Network Performance.

1. D. K. Sah, M. Vahabi and H. Fotouhi, "Real-Time Inference for IIoT Using Distributed Low-Power Edge Clusters," 2025 IEEE 11th World Forum on Internet of Things (WF-IoT), Chengdu, China, 2025, pp. 1-3, doi: 10.1109/WF-IoT64238.2025.11270629. [Google Scholar] [Crossref]

2. J. R, D. U, P. Mohandas, S. M, Y. N and A. A, "Edge-to-Cloud Deep Learning Framework for IoT Environments," 2026 3rd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS), Nagpur, India, 2026, pp. 1-8, doi: 10.1109/ICETEMS66917.2026.11469696. [Google Scholar] [Crossref]

3. P. Singh, G. R. V, S. Ponmaniraj, A. Aggarwal and S. Kumari, "Energy-Efficient IoT Architectures for Smart Cities: Implementing Adaptive Edge Computing for Sustainable Urban Development," 2025 International Conference on Electronics, AI and Computing (EAIC), Jalandhar, India, 2025, pp. 1-6, doi: 10.1109/EAIC66483.2025.11214219. [Google Scholar] [Crossref]

4. S. Tiwari, Z. Naaz and V. Sharma, "Efficient Task Offloading in Edge Computing Using Priority Based Queuing and Energy Optimisation," 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES), Greater Noida Gautam Budh Nagar, India, 2025, pp. 743-747, doi: 10.1109/CISES66934.2025.11264850. [Google Scholar] [Crossref]

5. H. Dong, Z. Dou, Y. Ji and J. Dong, "Attention-Driven Deep Reinforcement Learning for Efficient Task Offloading in Mobile Edge Computing," 2025 10th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE), Xi'an, China, 2025, pp. 223-228, doi: 10.1109/ISAEECE66033.2025.11160206. [Google Scholar] [Crossref]

6. M. Bhatia and S. Sood, "Quantum-Computing-Inspired Optimal Power Allocation Mechanism in Edge Computing Environment," in IEEE Internet of Things Journal, vol. 11, no. 10, pp. 17878-17885, 15 May15, 2024, doi: 10.1109/JIOT.2024.3358900. [Google Scholar] [Crossref]

7. V. Sharma and S. Kumar, "Role of Artificial Intelligence (AI) to Enhance the Security and Privacy of Data in Smart Cities," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 596-599, doi: 10.1109/ICACITE57410.2023.10182455. [Google Scholar] [Crossref]

8. P. S N, D. K. J. Bahadur Saini, S. M. Jirankali, G. Varma, H. Khanum and S. Kolekar, "Energy-Aware and Trustworthy Edge Intelligence for Sustainable AI-Driven Smart Cities," 2026 7th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Goathgaun, Nepal, 2026, pp. 415-422, doi: 10.1109/ICMCSI67283.2026.11412727. [Google Scholar] [Crossref]

9. H. Byeon et al., "Consumer Technology in Task Offloading and Edge Resource Allocation: AIoT and Edge Computing for Next-Generation Communication," in IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 5356-5365, May 2025, doi: 10.1109/TCE.2025.3552205. [Google Scholar] [Crossref]

10. D. V. Patil, S. Joshi, N. V, N. Yamsani, T. P. Priyanka and V. Kumar Shukla, "Real-Time Energy Optimization in Smart Buildings Using Predictive Edge Intelligence," 2025 International Conference on Intelligent and Secure Engineering Solutions (CISES), Greater Noida Gautam Budh Nagar, India, 2025, pp. 887-892, doi: 10.1109/CISES66934.2025.11265329. [Google Scholar] [Crossref]

11. M.Anitha and K.Sivaraman, "Enhancing Data Privacy in Edge Computing Through Hybrid Machine Learning and Blockchain Technologies," 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON), Lonavala, India, 2026, pp. 1-6, doi: 10.1109/I3CTCON68242.2026.11507430. [Google Scholar] [Crossref]

12. M. Adudhodla, M. Archana, K. D. Reddy, V. Swathi and B. Rambabu, "Secure Federated Learning Framework for Anomaly Detection in IoT Networks using Lightweight Cryptographic Hashing," 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 2025, pp. 472-477, doi: 10.1109/ICIMIA67127.2025.11200771. [Google Scholar] [Crossref]

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