Green internet of things using UAVs in B5G networks: A review of applications and strategies

Alsamhi, Saeed Hamood; Afghah, Fatemeh; Sahal, Radhya; Hawbani, Ammar; Al-qaness, Mohammed A. A.; Lee, Brian; Guizani, Mohsen

doi:10.1016/j.adhoc.2021.102505

Cited by 141 publications

(80 citation statements)

References 153 publications

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“…There is a need for further integration of digital enabling technologies such as functional electronics (e.g., nanoelectronics, flexible, organic and printed electronics or electronic smart systems), blockchain [17], edge computing [18], UAVs [19], 5G/6G [20], big data, and AI [21] into existing circular business approaches to provide information and additional services.…”

Section: G-iot and Edge-ai For Digital Circular Economy (Dce)mentioning

confidence: 99%

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Fraga‐Lamas

Lopes

Fernández‐Caramés

2021

Sensors

176

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Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.

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Section: G-iot and Edge-ai For Digital Circular Economy (Dce)mentioning

confidence: 99%

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Fraga‐Lamas

Lopes

Fernández‐Caramés

2021

Sensors

176

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“…IoT devices help societies (i.e., businesses, medicine and healthcare, agriculture, building management, green environment management, smart cities, and smart things) collect a significant amount of data [ 87 , 88 ]. ML for IoT assists with its surpassing feature in maintenance, monitoring, prediction, detection, and vehicle telemetry.…”

Section: Machine Learning For Iotmentioning

confidence: 99%

“…Recently, drones have become one of the IoT smart devices that play a vital role in enhancing connectivity and delivering data in real time [ 115 ]. Furthermore, the developments of 5G were based on the integration of drones as aerial base stations.…”

Section: ML For Enhancing Connectivity In Iot Environmentsmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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“…Recently, 5G has been deployed for supporting communication of highly‐mobile nodes, including UAVs 17 . Optimal radio resource allocation is one of the fundamental challenges for design and operation of 5G networks 18 .…”

Section: Current and Future Trendsmentioning

confidence: 99%