An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Area of Science:

• Computer Science
• Electrical Engineering
• Telecommunications

Background:

• Internet of Things (IoT) devices require significant processing power for complex applications.
• Energy constraints on IoT devices necessitate efficient task offloading strategies.
• Traditional cloud computing (CC) presents latency and energy challenges for geographically dispersed IoT devices.

Purpose of the Study:

• To propose a dynamic cost model for minimizing energy consumption and task processing time in IoT environments.
• To develop an algorithm (TEMS) that optimizes resource allocation for IoT tasks.
• To evaluate the effectiveness of the proposed model in mobile edge computing (MEC) scenarios.

Main Methods:

• Development of a dynamic cost model incorporating energy, processing time, data transmission costs, and idle energy.
• Implementation of the TEMS algorithm for intelligent task scheduling.
• Simulation of IoT scenarios to evaluate the proposed approach.

Main Results:

• The TEMS algorithm demonstrated significant energy savings, up to 51.6%.
• Task completion time was improved by up to 86.6% through optimized scheduling.
• The dynamic cost model effectively balanced energy and time constraints.

Conclusions:

• The proposed dynamic cost model and TEMS algorithm offer a viable solution for energy-efficient task processing in IoT.
• Mobile edge computing (MEC) combined with intelligent scheduling significantly enhances IoT performance.
• This approach addresses the critical energy limitations of battery-powered IoT devices.