A metaheuristic optimization approach for energy efficiency in the IoT networks

© 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches

Load Balancing of Energy Cloud using Wind Driven and Firefly Algorithms in Internet of Everything

The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The “thing” in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such “thing”s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating “Internet of Everything (IoE)” paradigm with a completely diversified technology - Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased -life-time and reduction in traffic