Optimisation of energy management in IoT devices using LSTM models: Energy consumption prediction with sleep-wake scheduling control

• Proposes an LSTM-based model for accurate energy-consumption prediction. • Integrates adaptive algorithms that dynamically adjust to temperature, humidity, and environment. • Predicts optimal sleep/wake times and applies smart scheduling to extend battery life and reduce energy use. The Internet of Things is an enormous network of interrelated devices that makes intelligent interaction and high-level control possible in various environments, such as smart homes, smart cities, and industry, by collecting, processing, and transferring data. The majority of the low-power devices within the network utilize limited sources of energy, such as batteries, and hence energy management is a critical factor in the design and operation of the systems. Current methods, such as reinforcement and evolutionary approaches, have at times been found to provide some enhancements but lacked extensive implementation over broad systems due to computational complexity as well as their inability to adapt to changing environmental settings. The growing number of IoT devices presents challenges in energy management, making it crucial to develop accurate prediction models. This research aims to address this challenge by proposing a novel solution using Long Short-Term Memory (LSTM) networks for energy consumption forecasting. This work suggests an optimal energy usage management model based on Long Short-Term Memory networks. The model collects historical energy usage, activity scheduling, and environmental factors such as temperature and humidity. Following the preprocessing, which includes noise removal and normalisation, it predicts future energy consumption. Scheduling data and the analysis and processing of environmental conditions are done using the short-term memory, while the long-term memory helps the model identify more complex patterns in the energy consumption over time to make more accurate predictions. Based on this prediction, smart policies are made for going to sleep and waking up the devices, so that unnecessary devices are put into sleep mode and only woken up when needed. Adaptive learning algorithms also assist in adjusting to environmental conditions. Results of experiments show that the proposed method can save energy up to 58% and increase device lifetime by 30%, while the prediction of energy consumption has an accuracy of 95%.