Advancing Industrial IoT: A Swarm-Intelligent-Based Job Offloading in Edge Computing - Krishan, Krishan; Rajni, Rajni; Priyanka, Priyanka; Chaudhary, Prachi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
By integrating detectors, supervision and communication tools into manufacturing processes, the Industrial Internet of Things (IoT) raises productivity, lowers costs and improves the value of products. It is difficult to process enormous amounts of information, which makes it difficult to swiftly transition conventional sectors to edge computing. This paper provides a unique swarm-intelligent technique named boosted beetle swarm optimization (BBSO) for offloading jobs from edge gadgets to edge servers with the lowest latency and energy consumption, taking into account the rapidly growing number of industrialized edge items and heterogeneous edge servers. The presented multi-objective optimization issue considers job performance cost, consumption of energy and latency. The entire cost of assigning each work to a separate mobile edge computing (MEC) server is represented by the fitness coefficient. Using experimentation, the effectiveness of the suggested BBSO-driven offloading technique is contrasted with alternative techniques.