Improved salp swarm algorithm based optimization of mobile task offloading
Aishwarya R., Mathivanan G.

TL;DR
This paper introduces an improved optimization algorithm to efficiently offload mobile tasks to edge servers, reducing energy use and delays.
Contribution
The novel contribution is an improved salp swarm algorithm-based approach for optimal task offloading in mobile edge computing.
Findings
The ISSA-MAOA technique minimizes energy consumption, memory usage, and task completion delays.
The proposed method enhances resource management and user experience in mobile cloud computing environments.
Optimized task offloading leads to improved efficiency and sustainability in mobile application execution.
Abstract
The realization of computation-intensive applications such as real-time video processing, virtual/augmented reality, and face recognition becomes possible for mobile devices with the latest advances in communication technologies. This application requires complex computation for better user experience and real-time decision-making. However, the Internet of Things (IoT) and mobile devices have computational power and limited energy. Executing these computational-intensive tasks on edge devices may result in high energy consumption or high computation latency. In recent times, mobile edge computing (MEC) has been used and modernized for offloading this complex task. In MEC, IoT devices transmit their tasks to edge servers, which consecutively carry out faster computation. However, several IoT devices and edge servers put an upper limit on executing concurrent tasks. Furthermore,…
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Taxonomy
TopicsIoT and Edge/Fog Computing · IoT-based Smart Home Systems · IoT Networks and Protocols
