SEMAEco-IoT: A Secured IoT-based Smart Energy Monitor and Alert for Enhanced Energy Conservation and Optimization
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Description
Abstract Today, there is a global demand for efficient energy consumption utilizing energy-fit management solutions that are poised to deliver sustainable living practices with reduced consumption of energy therein. These in their own right- have also continued to raise germane concern tailored at environmental, health and consumption regulations – to yield global concern and priorities targeted at replacing traditional solutions with (alternate) intelligent, optimized models and strategies. Our study presents SEMAEco-IoT – designed with the microcontroller ESP and sensor to observe environmental conditions and energy consumption by home appliances. It utilizes machine learning algorithm to analyze total energy consumed by each appliance and delivers optimal consumption that reduces energy waste. The system tested across multiple parameters – yields effectiveness, reliability, and efficiency. Our use of ESP8266 and ThingSpeak is able to handle expansive inputs without significant delays or data losses. Results affirm its capability to maintain stable performance even with more device connected. Keywords: Intrusion. DoS, Transfer Learning, Korhonen Neural Net, Genetic Algorithm