Artificial intelligence implication on energy sustainability in Internet of Things: A survey

• Published in: Information processing & management Vol. 60; no. 2; p. 103212
• Main Authors: Charef, Nadia, Ben Mnaouer, Adel, Aloqaily, Moayad, Bouachir, Ouns, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Data aggregation and fusion; Energy awareness; Energy harvesting; IoT; Machine learning; Sustainability; Swarm intelligence; Sustainability; Machine learning; AI; Data aggregation and fusion; Swarm intelligence; Energy harvesting; Energy awareness; IoT
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2022.103212

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. •A survey that explores enhanced AI-based solutions for IoT applications.•Discuss the integration of AI, ML, and SI techniques in the design of existing IoT energy protocols.•Comprehensive coverage of resources management of the IoT to achieve energy sustainability.•Explain the benefits of using different AI/ML techniques to improve energy sustainability in IoT.