Improving the Efficiency of IoMT Using Fuzzy Logic Methods
K Kiran Kumar, S. Sivakumar, Pramoda Patro, RenuVij · 2024
Among all industries, 20% are homes where home energy management systems have become more feasible with the introduction of smart appliances and clever sensors. When gauging the smart home's efficacy, it is important to strike a balance between energy efficiency and resident convenience. Up to 60% of a typical home's annual energy bill goes toward the operation of heating, ventilation, and air conditioning (HVAC) systems. Multiple studies have shown that reducing energy usage is the primary motivation for using fuzzy logic systems in conjunction with other methods. However, user convenience is typically compromised while using such methods. In this research, the fuzzy inference system (FIS) takes humidity into account both the current temperature and the user's preferred setting to keep the thermostat at an optimal level. Furthermore, utilize the variation in interior room temperature as feedback for the suggested fuzzy inference system to optimize energy usage. Determining each rule's parameters in FIS takes more time and introduces more room for human mistakes as the number of rules grows. Suggested the use of combinatorial methods for the automatic generation of rule basis. Also, Sugeno FIS and Mamdani FIS are used to analyze the effectiveness of the offered methods. The suggested solution utilizes adaptive algorithms and smart sensors to keep the user's preferred temperature constant. The suggested FIS system may be implemented in an Internet of Things (IoT) operating system like RIOT, since it makes use of sensors and needs little in the way of memory and computing resources. The suggested method has been shown via simulation to result in a 2.5% decrease in energy use.