Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan, Latif U.; Yaqoob, Ibrar; Tran, Nguyen H.; Kazmi, S. M. Ahsan; Dang, Tri Nguyen; Hong, Choong Seon

doi:10.1109/jiot.2020.2987070

Cited by 288 publications

(157 citation statements)

References 229 publications

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“…One of the most important use cases of IoT is smart city and its important subset smart home/building [186]. Recently, the MEC contexts and novel 5G technologies have been enabled to emerge the judicious edge big data analysis and wireless access for IoT systems to further improve the urban quality of life for citizen with many aspects including security, privacy, energy management, safety, convenient life, ect.. For energy management, an fog-based IoT automation mechanism was validated in [151] to optimize the resource management for smart building systems.…”

Section: ) Smart Home and Smart Citymentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: ) Smart Home and Smart Citymentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…Edge and fog computing are two architectures that aim to bring processing closer to users at the network edges. While some in the literature do not differentiate between fog and edge [ 54 ], we and many others differentiate between them [ 55 , 56 ], depending on where the computation is performed. In edge computing, processes are localized in the edge devices to produce instant results.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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“…RGB color features' based similarity/dissimilarity is calculated to segment BG/FG pixel. To measure similarity/ dissimilarity, OR and AND fusion of {R, G, B} channels were calculated as in (4) and (5). We experimented with these OR and AND fusions to observe the accuracy differences between them.…”

Section: E the Fusion Of Rgb Featuresmentioning

confidence: 99%

“…(4) Adaptive post-processing operation is employed in order to compensate for raw segmentation noises. (5) We develop a moving object detection method that can be applied in limited computing devices.…”

Section: Introductionmentioning

confidence: 99%

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

2020

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The moving object detection refers to the detection of physical moving objects from a video, which is applied in video surveillance, object recognition, object counting, human-computer interaction, and so on. Moreover, nowadays, real-time moving object detection is used as services in the cloud, edge, and fog computing. However, the existing methods do not meet the trade-off between accuracy and complexity. To address these issues, we present a background subtraction-based moving object detection method, called Fast-D. In this paper, we look at the 'non-smoothing color feature' to make the moving object detection more robust in real-time. Each color feature is given equal significance during the classification of a pixel. Background model and threshold are initialized for each pixel. And then, the background model and threshold are updated dynamically when there are changes in the background of the video. Adaptive post-processing is considered to discard salt and pepper noise and fill holes in the detected moving object silhouettes. The evaluation of our proposed method on four complex datasets exhibits the significance. INDEX TERMS Real-time moving object detection, background subtraction, object segmentation, change detection.

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Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Cited by 288 publications

References 229 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

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