Device to Device Communication in 5G Network using Device-Centric Resource Allocation Algorithm

Abstract

Due to the overutilization of commercial buildings, climatic changes have occurred, which has caused a major impact on the environment Global warming is the main issue faced by society due to ozone weakening. Many researchers conclude that commercial buildings in society are a major part of consuming energy and generating carbon equally in the environment Most of the living areas are filled with commercial buildings where the peoples have to get their needs. This commercial building consists of an HVAC and Refrigeration system which is the major reason for the energy consumption in the commercial building. These systems are utilized in commercial buildings for customer satisfaction, comfortable shopping, and avoiding food wastage. But the main motive of this is to reduce the carbon emission produced from the commercial building and achieve optimal energy consumption without reducing the customer's comfort and avoiding food wastage. This article is categorized into two articles to construct an algorithm with an effective prediction to achieve a plan. This article concentrates on the requirements of a Zero Carbon (Z.C.) building. So that the data of energy consumption required for the commercial building are to be analyzed. For such analysis, the first part of this article is done with an Internet Of thigh (IoT) technology to collect the dataset of sensors in the building. This IoT Edge system is interconnected with all the devices of sensors used in HVAC and Refrigeration systems with the support of microcontrollers. The devices like sensors, consumers, actuators with user interfaces, and other technical gadgets are controlled and communicated by the Building Management System (BMS). This IoT Edge system collects and transfers the data into the IoT hub. IoT hub maintains the two-way communication among the cloud and Edge system utilized to secure the sensor data. This system supports the huge dataset storage used to predict and analyze the exact reason for energy consumption. This sensor data collection helps to evaluate the energy requirements according to a day/week or month in the commercial building's usages. In the second part of the article, the predictions are made with the new hybridization of the Firefly Optimization Algorithm (FOA) and the Deep Learning algorithm of Long Short-Term Memory Network (LSTM), respectively. The new proposed algorithm is Firefly optimized Long Short-Term Memory Network (FOLSTM), which is used to obtain an optimal solution. This proposed algorithm is compared with several traditional methods, which shows that FOLSTM is the better algorithm for accurate prediction.