IoT Framework for Quality-of-Service Enhancement in Building Management System

Abstract

The Internet of Things (IoT) is used for automating human life during the provision of its services. This incredible technology has the potential to improve and ease human life significantly. Building Management System (BMS) is one such system, which is being extensively used in today's world as a remedy against buildings using high electricity supply. Multi-agent System (MAS) is being used in smart buildings to communicate and negotiate with multiple agents for energy supply. Machine Learning algorithms are included as a part of the Economical Building Management System (EBMS) to enhance the computational calculation for the electricity distribution and real-time electricity price forecasting even when facility managers are absent. Quality of Service (QoS) plays a very important role while considering the Internet of Things (IoT) due to the large number of interconnected nodes, so Quality-of-Service Enhancement metrics must be clearly defined to enhance the popularity of any service. As the number of nodes expands, QoS is compromised which is the nature of IoT technology. QoS is inversely dependent on node count, i.e., with the increment of nodes QoS will start hampering as increasing the number of nodes will increase the number of requests over the IoT server. In today’s environment, an optimized framework is strongly needed which can control the QoS for IoT. In order to limit the number of queries, an improved framework has been proposed and implemented in this study using mathematical tools, specifically MATLAB. The best power control strategy depends on the goal of the optimization problem: a fair strategy maximizes the product of each user's QoS, the best policies are derived using various Machine Learning algorithms, and applying the standard of QoS optimization for goals and QoS limitations are shown.