Optimal motion sensor placement in smart homes and intelligent environments using a hybrid WOA-PSO algorithm

With ever-increasing development of the Internet of Things (IoT) technology, intelligent environments and homes are developing rapidly. Motion sensors are amongst the most important elements of environmental automation and their optimized placement guarantees covering the environment as much as possible using a minimized number of sensors. Finding the optimal number and location of motion sensors is two of the main challenges in this area. Meta-heuristic algorithms are among the techniques used for placing motion sensors. Nonetheless, each technique has its limitations, and the use of hybrid methods can be effective in overcoming these constraints. The present study combines the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) aiming to find the optimized motion sensor placement in smart homes and intelligent environments. Some demerits of PSO include covering small search space, inability in solving high-dimensional problems, or having complex objective functions, as PSO uses a constant inertia weight.The WOA, on the other hand, can cover a wider and more uncertain space due to the use of helical logarithmic functions. Therefore, these two algorithms can be combined to provide a hybrid algorithm with fewer weaknesses and more advantages. The proposed method compared to previous works provides improvements in terms of coverage percentage, detection accuracy, and operating cost.