AI-based fog and edge computing: A systematic review, taxonomy and future directions

Iftikhar, Sundas; Gill, Sukhpal Singh; Song, Chenghao; Xu, Minxian; Aslanpour, Mohammad Sadegh; Toosi, Adel Nadjaran; Du, Junying; Wu, Huaming; Ghosh, Shreya; Chowdhury, Deepraj; Golec, Muhammed; Kumar, Mohit; Abdelmoniem, Ahmed M.; Cuadrado, Félix; Varghese, Blesson; Rana, Omer F.; Dustdar, Schahram; Uhlig, Steve

doi:10.1016/j.iot.2022.100674

Cited by 63 publications

(20 citation statements)

References 182 publications

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“…With Edge Computing, content providers can host services at the edge of the network with accurate user location within a radio access network [16]. This can improve the QoE for mobile users [5].…”

Section: Context Aware Applicationsmentioning

confidence: 99%

“…The application for mobile device users was, however, an evolution of the cloudlet paradigm and the authors presented a semi-quantitative analysis of how to structure the cloudlet architecture for an urban environment, concluding that smaller edge cloudlets performed better for multiple users than a larger centralized metropolitan area network [9]. The implication of these findings was that an unspecified number of smaller cloudlet servers would be positioned to provide diffuse coverage to ensure a high QoE [5].…”

Section: Cloudlet Paradigmmentioning

confidence: 99%

“…This concept was at least partially considered in an earlier study where dynamic cloudlets were proposed from any mobile devices in the network that possessed the necessary computing resources [101]. This analysis proposed an infrastructure co-located with the Wi-Fi access point but also capable of discovery locally devices that could share computing resources [5]. The same fundamental idea has been elaborated into the FemtoCloud proposal, in which mobile devices with significant idle computing power can link via a client service installed on the devices [102].…”

Section: Cloudlet Paradigmmentioning

confidence: 99%

“…However, this technology faces key issues: security, speed of services and slow connections, which are often combined as low bandwidth/high latency and jitter as mobile devices offload computational and processing capacity to cloud computing services [3,4]. These challenges have been exacerbated by the continued proliferation of mobile and fixed Internet-connected devices [5]. Problems of high latency and narrow bandwidths with reduced Quality of Experience (QoE) for users led to proposals to re-imagine the cloud: rather than being thought of as a homogeneous entity; the cloud would have a distinct "edge" separate from the core in which large-scale processing and storage would occur [6].…”

Section: Introductionmentioning

confidence: 99%

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Edge AI: A survey

Singh

Gill

2023

Internet of Things and Cyber-Physical Systems

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Section: Context Aware Applicationsmentioning

confidence: 99%

Section: Cloudlet Paradigmmentioning

confidence: 99%

Section: Cloudlet Paradigmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Edge AI: A survey

Singh

Gill

2023

Internet of Things and Cyber-Physical Systems

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“…However, the authors did not consider the key enabling technologies for the deployment of AI models. In [ 18 ], the authors analyze the role of AI algorithms and the challenges of the application of these algorithms for resource management. However, the authors did not consider the key enabling technologies for the deployment of AI models.…”

Section: Introductionmentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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Sustainable edge computing: Challenges and future directions

Arroba,

Buyya,

Cárdenas

et al. 2024

Softw Pract Exp

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The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

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AI-based fog and edge computing: A systematic review, taxonomy and future directions

Cited by 63 publications

References 182 publications

Edge AI: A survey

Edge AI: A survey

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Sustainable edge computing: Challenges and future directions

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