4.6 Article

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

期刊

IEEE ACCESS
卷 9, 期 -, 页码 38833-38858

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3061697

关键词

Green products; Wireless sensor networks; Internet of Things; 5G mobile communication; Sensors; Cloud computing; Wireless communication; Fifth generation; Internet of Things; green Internet of Things; fog; machine learning

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This article provides insights on green Internet of Things (GIoT) applications, practices, awareness, and challenges, discussing energy models, energy-efficient hardware design principles, and data traffic management technologies. Services provided through fog/edge computing can improve energy consumption and data security. The article emphasizes the role of artificial intelligence and machine learning in addressing data growth issues in IoT networks.
Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.

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