Appliance Scheduling Towards Energy Management in IoT Networks Using Bacteria Foraging Optimization (BFO) Algorithm

Abstract

Modern life is almost impossible without electricity, and there is an explosive growth in the daily demand for electric energy. Furthermore, the explosive increase in the number of Internet of Things (IoT) devices today has led to corresponding growth in the demand for electricity by these devices. Serious energy crisis arises as a consequence of these high energy demand. One good solution to this problem could be Demand Side Management (DSM) which involves scheduling of consumers’ appliances in a fashion that will ensure peak load reduction. This ultimately will ensure stability of the Smart Grid (SG) networks, minimization of electricity cost, as well as maximization of user comfort. In this work, we adopted Bacteria Foraging (BFA) Optimization technique for the scheduling of IoT appliances. Here, the load is shifted from peak hours toward the off peak hours. The results show that BFA optimisation based scheduling technique caused a reduction in the total electricity cost and peak average ratio.