Artificial intelligence implication on energy sustainability in Internet of Things: A survey |
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| Institution: | 1. College of Management, Shenzhen University, Shenzhen, China, 518055;2. Greater Bay Area International Institute for Innovations, Shenzhen University, Shenzhen, China, 518055;3. Shenzhen University, Shenzhen, China, 518055;4. School of Logistics, Yunnan University of Finance and Economics, Kunming, 650221, China;1. School of Management, Hefei University of Technology, Hefei 230009, P.R. China;2. Philosophical and Social Laboratory for Data Science and Intelligent Society Governance at Ministry of Education of China, Hefei 230009, P.R. China;3. College of Business, Lamar University, Beaumont, USA;4. Center for Mental Health Education, University of Shanghai for Science and Technology, Shanghai 201210, P.R. China;1. School of Information Management, Central China Normal University, Wuhan, 430079, China;3. Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China;4. Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China;5. School of Information Management, Wuhan University, Wuhan, 430072, China |
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| Abstract: | 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. |
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| Keywords: | IoT Sustainability Energy harvesting Energy awareness Machine learning AI Swarm intelligence Data aggregation and fusion |
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