EMS: An Energy Management Scheme for Green IoT Environments

Said, Omar; Al-Makhadmeh, Zafer; Tolba, Amr

doi:10.1109/access.2020.2976641

Cited by 80 publications

(13 citation statements)

References 49 publications

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“…The IoT sensor nodes were classified into different levels based on their importance and fault tolerance strategy is adopted based on node levels. Average energy consumption rate, number of nodes failing, data throughput and network lifetime were used as a performance metrics to benchmark proposed EMS [201].…”

Section: ) Artificial Intelligence (Ai) Enabled Computingmentioning

confidence: 99%

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

et al. 2021

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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.INDEX TERMS Fifth generation, Internet of Things, green Internet of Things, fog, machine learning.

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Section: ) Artificial Intelligence (Ai) Enabled Computingmentioning

confidence: 99%

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

et al. 2021

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“…Said, Omar Al-Makhadmeh, et al, [78] Introduced an IoT Energy Management Scheme (EMS). Heterogeneous forms of energy-controlled nodes are considered in this framework.…”

Section: Energy Management Surveymentioning

confidence: 99%

Energy Management for Internet of Things via Distributed Systems

Sadeeq

Zeebaree

2021

JASTT

108

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The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

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“…However, this strategy does not work for all scenarios. For this reason, a relevant research challenge is reducing energy consumption in IoT devices [140], [141].…”

Section: ) Energy Consumptionmentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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EMS: An Energy Management Scheme for Green IoT Environments

Cited by 80 publications

References 49 publications

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

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

Energy Management for Internet of Things via Distributed Systems

A Survey on Trustworthiness for the Internet of Things

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