International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 234-240, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
P. Karthikeyan
Pachikayala Susmitha
Shaik Anwar Basha
Kasturi Teja
Jupudi Sai Sravan Kalyan
Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, India.
Abstract: Energy-Efficient Task Offloading focuses primarily on reducing energy consumption in terminal devices and improving processing capabilities by relocating peak energy-consuming processing tasks in an edge or cloud environment. Generally, all processing tasks were in terminal devices in traditional systems, resulting in increased energy consumption, processing capabilities, delay, and ineffectiveness in processing real-time processing tasks. Moreover, decisions in traditional task offloading schemes were non-adaptive, leading to complexities in dealing with dynamic network environments. Therefore, to address such complexities, latest schemes include cloud/edge collaborations, machine learning algorithms, and optimization algorithms with a major focus on cognitive decision-making with respect to task offloading with an objective of improving efficiency, processing capabilities, among other performance aspects.
Keywords: Task Offloading, Edge Computing, IoT, Energy Consumption, Processing Capability.
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