Deep learning-based energy management framework for IoT devices in edge computing environment

Abstract

Recently, various cutting-edge Internet of Things (IoT) services and solutions markets such as smart home, energy, and manufacturing rapidly increase in size. In most cases, continuous monitoring application is expect to be the main component which these IoT solutions and services are being provided to users. In order to provide a continuous monitoring service stably in such an environment, multiple IoT devices should be deployed, and a huge amounts of data are generated and collected from these IoT devices. Once these IoT devices are deployed in the environment, it is difficult to change the configuration of them. Also, it is almost impossible to supply power via connecting cables to all the IoT devices simultaneously, in most cases. Therefore, typical IoT devices are generally supplied energy from a capacity-constrained battery. Furthermore, IoT devices transmit measurement data in uniform period fashion in order to continuously provide a monitoring service. Therefore, energy saving of IoT devices has become a critical issue in diverse IoT applications due to their limited battery capacity. Particularly, unnecessary energy cost for redundant data transmission such as transmitting duplicate or similar data generally occurs. Therefore, based on these mentioned problems, an deep learning-based energy management framework method for IoT devices in an edge computing system environment is newly proposed. To reduce energy consumption of multiple IoT devices, the transmission period of data transmitted from IoT devices is dynamically adjusted by considering both prediction for imputation error of un-transmitted data and energy consumption of IoT devices, simultaneously. The proposed imputation error prediction module is designed using various deep learning model structures and the optimal transmission period value is obtained through an optimization problem considering both the prediction value of imputation error and the numerically modeled energy consumption of the IoT device. Furthermore, the proposed transmission period control framework is implemented in the configured testbed environment and checks whether the proposed framework can be developed in the practical environment. In addition, radio frequency-based energy harvesting technology is considered for the energy supply of IoT devices. When a large number of IoT devices are distributed in the OFDMA environment, the minimum harvesting energy of IoT devices should be optimized through efficient wireless channel and transmission power resource allocation to prolong the lifetime of IoT devices. Therefore, two resource allocation methodologies that optimizes the harvesting power of IoT devices has been studied. First one is a method to maximize the minimum harvested power of an IoT device, and the other one maximizes the harvested power of each IoT device while considering the minimum harvested power constraint. In the both resource allocation methodologies, the amount of DC power converted from RF power is considered as a non-linear model for practical energy harvesting result. Finally, by combining the proposed transmission period control algorithm and energy harvesting mechanism, an optimal period allocation method of data transmission and energy harvesting is newly proposed.