A Bacterial Foraging Based Smart Offloading for IoT Sensors in Edge Computing

The main aim of edge computing is to facilitate the low power computational IoT sensors in processing heavy tasks. Sensors should be enough efficient to offload the required task and get back the computation results in the stipulated time. However, acquiring the desired quality of services (QoS) is a challenging task. Therefore, efficient resource management is inevitable to reduce the response time and communication cost. In this paper, a computation offloading algorithm based on nature-inspired multi-objective bacterial foraging optimization (MO-BFO) is proposed for load management over edge servers. The performance of the proposed MO-BFO algorithm is quantitatively evaluated against standard ant colony optimization (ACO), particle swarm optimization (PSO), and round-robin (RR) scheduler. The detailed results show that the proposed algorithm reduces the response time, communication cost, and ensures effective load management compared to its counterparts.