In the rapidly expanding world of IoT, where sensors and smart devices are becoming as ubiquitous as the data they generate, a novel approach to load balancing and resource allocation is making waves. A. Jenice Prabhu, from the Department of Computer Science and Engineering at Loyola Institute of Technology and Science, has introduced RACE-IoT, a technique that promises to revolutionize how we manage IoT edge computing.
RACE-IoT, or Resource Allocation using Clustering Enabled Optimization in IoT, is a response to the challenges of low latency and efficient resource use in IoT applications. Prabhu’s work introduces a novel Shannon–dragonfly (Sha-Dragon) optimization algorithm, which combines the dragonfly algorithm with Shannon entropy for resource scheduling. “The idea is to balance the load by reallocating resources to maintain an even load ratio across all servers,” Prabhu explains. This approach ensures that time-sensitive IoT applications, such as those in agriculture, transportation, and healthcare, can deliver services promptly and efficiently.
The implications for the agriculture sector are significant. With the increasing use of IoT sensors in precision farming, real-time data processing is crucial for monitoring soil conditions, crop health, and weather patterns. RACE-IoT’s ability to balance loads and optimize resource use can lead to more efficient data processing, enabling farmers to make data-driven decisions swiftly. “This technology can help farmers optimize their resources, reduce waste, and increase yields,” says Prabhu.
In tests, RACE-IoT outperformed existing strategies like PBSM, HRL-Edge-Cloud, and ERAM-EE in terms of energy consumption, throughput, response time, end-to-end delay, resource utilization, makespan, and success rate. The proposed technique achieved higher success rates of 35%, 17%, and 18% respectively compared to these existing techniques. These results demonstrate the efficiency and potential of RACE-IoT in real-world applications.
The research, published in the International Journal of Computational Intelligence Systems, is a significant step forward in IoT edge computing. As the number of IoT devices continues to grow, the need for efficient load balancing and resource allocation will only increase. RACE-IoT’s innovative approach could shape future developments in the field, paving the way for more responsive and efficient IoT applications.
Prabhu’s work highlights the importance of interdisciplinary research in addressing real-world challenges. By combining principles from information theory and swarm intelligence, RACE-IoT offers a novel solution to the complexities of IoT edge computing. As we move towards a more connected world, such innovations will be crucial in harnessing the full potential of IoT technologies.