Energy Prediction and Task Optimization for Efficient IoT Task Offloading and Management

Authors

  • G. Pradeep Research Scholar, Department of Computer Science & Engineering, Dr. M.G.R Educational & Research Institution, Chennai
  • S. Ramamoorthy Professor, Department of Computer Application, Dr. M.G.R Educational & Research Institution, Chennai
  • M. Krishnamurthy Professor & Head,Department of Computer Science and Engineering,KCG College of Technology, Chennai
  • V. Saritha Dept. of Computer Science and Engineering, Sri Padmavathi Mahila Visvavidyalayam, Tirupati.

Keywords:

Energy Prediction, IoT Task Offloading, Edge Computing, Adaptive Offloading, Energy Efficiency

Abstract

This research addresses energy-aware task offloading within the Internet of Things (IoT) networks. In today’s interconnected world, IoT devices play an increasingly pivotal role. However, they often face limitations regarding energy consumption, which hinders their prolonged operation and effectiveness. Existing IoT task offloading strategies focus on isolated aspects of energy optimization, overlooking the holistic nature of energy management. This leads to suboptimal utilization of device resources, reduced device lifespans, and potential performance bottlenecks. This proposes the Energy Prediction and Task Optimization (EPTO) algorithm; we leverage multi-dimensional profiling, real-time monitoring, and adaptive decision-making. EPTO consistently outperforms traditional strategies, enhancing energy efficiency, device lifespan and quality of service. EPTO combines innovative methods, including LSTM-based energy prediction, adaptive offloading policies, and dynamic resource allocation. It employs a comprehensive mathematical modeling approach that integrates data from diverse sources, offering unparalleled adaptability in dynamic IoT environments.in this paper we employed a diverse dataset comprising various IoT devices, each characterized by battery levels, computation intensity, data transmission energy, historical energy consumption patterns, and task characteristics. This dataset enabled realistic simulations and robust performance evaluations. Our proposed work evaluated with the following performance metrics, including Energy Efficiency Ratio (EER), Task Completion Time (TCT), Battery Lifetime Extension (BLE), Resource Utilization (RU), and Rate of Offloaded Tasks (ROB). Our quantitative results demonstrate substantial improvements in energy efficiency, with EER values exceeding 0.85. Task Completion Time is notably reduced, with TCT averaging 65 seconds, while BLE metrics show significant device lifespan extensions of up to 30%. EPTO's adaptability suits various IoT domains like smart cities, healthcare, and industrial automation. Its responsive resource management supports diverse IoT scenarios. EPTO addresses IoT sustainability and optimization, shaping greener and more efficient ecosystems. It revolutionizes energy management, paving the way for smarter IoT networks.

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Published

02.09.2023

How to Cite

Pradeep , G. ., Ramamoorthy , S. ., Krishnamurthy , M. ., & Saritha , V. . (2023). Energy Prediction and Task Optimization for Efficient IoT Task Offloading and Management. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 411–427. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3425

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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