In recent years, the demand for real-time data processing and resource management in distributed computing environments has made edge computing increasingly vital. As the Internet of Things (IoT) continues to expand, the need for efficient communication technologies and robust ICT infrastructure has never been more pressing. One of the primary challenges in resource management within edge computing is scheduling and optimization, particularly concerning energy consumption and latency.
This research proposal aims to address these challenges by developing a model for energy and latency optimization using metaheuristic optimization algorithms, specifically Bacterial Foraging Optimization (BFO). The BFO algorithm will be enhanced by integrating a fuzzy system to improve its performance, aiding in the convergence of BFO in relation to scheduling efficiency while reducing latency and energy consumption.
The primary objective of this research proposal is to meet Quality of Service (QoS) requirements, such as energy consumption and end-to-end delay, by leveraging Fuzzy BFO. The expected outcomes of this research include improved energy efficiency and shorter processing times compared to mainstream methods like Round-Robin, Threshold Based, and Random Offloading.
The proposed approach is designed to be more scalable, capable of handling a large number of tasks and edge computing nodes while maintaining high QoS, even in resource-constrained environments. This scalability is crucial as the number of devices connected to the internet continues to grow, necessitating innovative solutions to manage the increasing data load effectively.
In the context of Sustainable Development Goals (SDGs), this research aligns with the objectives of promoting sustainable industrialization and fostering innovation. By enhancing energy efficiency in edge computing, the project contributes to responsible consumption and production patterns, which are essential for sustainable development.
The research will be conducted at the Laboratory of Network Technology and Applications at UGM from May to October 2025. During this period, the team will focus on developing the Fuzzy BFO model, conducting simulations, and analyzing the results to validate the effectiveness of the proposed approach.
Collaboration with industry partners will also be sought to ensure that the findings can be translated into practical applications. This partnership will facilitate the implementation of the optimized model in real-world scenarios, further enhancing the impact of the research on energy efficiency and latency reduction in edge computing.
In conclusion, the proposed research on energy and latency optimization in edge computing using Fuzzy Bacterial Foraging Optimization represents a significant step towards addressing the challenges of resource management in distributed computing environments. By focusing on energy efficiency and latency reduction, this project aims to contribute to the broader goals of sustainable development and technological advancement.