Quality-driven Energy Optimization in Internet of Things

As a consequence of limited internal battery capacity and needless expenditure of electrical energy in commercial and residential deployments of Internet of Things (IoT) in recent years, there has been a rise in the impetus given to energy saving and optimization. In this paper, we propose a technique to solve the energy optimization problem for an IoT deployment that detects critical events like elderly fall detection, fire detection, intruder detection, etc. This technique aims to achieve an optimal set of configurations for a given IoT deployment such that the total energy consumption is minimum, and the deployment satisfies the required threshold accuracy expected from the deployment. Researchers have addressed this issue using algorithms mostly based on the Greedy Heuristic approach to reduce energy consumption by considering the dynamic nature of configurations of IoT devices. We propose Dynamic multiple-choice knapsack (DMCKP) algorithm that produces a better solution. The results of the proposed approach are compared with the existing algorithms to demonstrate the superiority of the proposed algorithm.