aspg-18-1615-1792025630 20261015005350 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2020 1 1 Mahmoud M. Ismail Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt The energy challenge in IoT refers to the significant energy consumption of IoT devices, which can lead to sustainability issues, shorter battery life, and increased operating costs. IoT devices are known for their high energy consumption, and optimizing their energy usage can have a significant impact on sustainability and cost. Machine learning (ML) can learn from data and patterns to predict and control energy consumption in IoT systems, making them more energy efficient. The main contribution of this paper is the establishment of a novel deep learning framework for enhanced predictive modeling of energy consumption in IoT networks to help realize Energy-efficient IoT systems. our framework applies recurrent processing to capture long-term relations in the energy consumption of IoT appliances. Then, the self-attention mechanism is devised to help the model to focus on important predictive features. Simulation experiments against the competing ML baselines demonstrate the predictive capability of our framework. 2020 61 69 1615 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.010105 https://americaspg.com/journal/18/article/1615 https://americaspg.com/storage/31678449360.pdf