Energy-Efficient IoT Networks Using AI Driven Approaches
DOI:
https://doi.org/10.22105/siot.v1i3.272Keywords:
Energy efficiency, IoT networks, AI-driven approaches, Machine learning, OptimizationAbstract
Energy efficiency in IoT networks is increasingly becoming a critical issue as the number of interconnected devices continues to grow. This paper investigates AI-based strategies to improve energy efficiency within IoT networks. By employing machine learning techniques such as neural networks, decision trees, and Reinforcement learning (RL), we aim to forecast and optimize energy consumption trends. Our research involves gathering data from diverse IoT environments and assessing the effectiveness of these models under both simulated and real-life conditions. The findings indicate notable enhancements in energy consumption, leading to longer battery life, decreased operational expenses, and reduced environmental impact. These results emphasize the necessity of incorporating AI into IoT systems for the development of sustainable and efficient networks. The AI-driven approaches enable IoT devices to function more effectively, resulting in considerable energy savings and cost reductions. This paper adds to the expanding research on sustainable IoT solutions and illustrates AI's potential to tackle significant energy efficiency issues in this domain.
References
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: vision and challenges. IEEE internet of things journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
Goodfellow, I. (2016). Deep learning. MIT press. https://www.deeplearningbook.org
Mohapatra, H., & Rath, A. K. (2020). IoT-based smart water. In IOT technologies in smart-cities: from sensors to big data, security and trust (Vol. 63, pp. 63–82). IET. https://B2n.ir/t17919
Mohapatra, H., & Dalai, A. K. (2022). IoT based v2i framework for accident prevention. 2022 2nd international conference on artificial intelligence and signal processing (AISP) (pp. 1–4). IEEE. https://ieeexplore.ieee.org/abstract/document/9760623/
Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., & Buyya, R. (2011). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: practice and experience, 41(1), 23–50. https://doi.org/10.1002/spe.995
Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice hall PTR. https://www.google.com/books/edition/Neural_Networks/bX4pAQAAMAAJ?hl=en
Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The international journal of robotics research, 32(11), 1238–1274. https://doi.org/10.1177/0278364913495721
Floridi, L. (2016). The Routledge handbook of philosophy of information. Routledge London. https://api.taylorfrancis.com/content/books/mono/download?identifierName=doi&identifierValue=10.4324/9781315757544&type=googlepdf
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