What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Gedeon, Julien; Brandherm, Florian; Egert, Rolf; Grube, Tim; Mühlhäuser, Max

doi:10.1109/access.2019.2948399

Cited by 55 publications

(27 citation statements)

References 274 publications

(331 reference statements)

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“…The potentialities of adopting edge computing paradigms in a broad sense were pointed out [ 33 ], while the benefits in the ambit of smart cities frameworks were widely discussed in the survey [ 34 ]. Applying edge computing in LPWAN (e.g., LoRa based ones) whose nodes have limited computational capabilities is challenging but there exist studies which proved the feasibility and indicated the benefits [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Baldo

Mecocci

Parrino

et al. 2021

Sensors

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Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.

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Section: Related Workmentioning

confidence: 99%

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Baldo

Mecocci

Parrino

et al. 2021

Sensors

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“…The main motivation of this work is to identify the pitfalls in cloud video synthesis to process large-volume of data to avoid latency in critical applications [11] [12]. Cloud computing aims enormous resources that can be flexibly used to fit dynamic demands which leads to avoid substantial latencies when using services placed at distant locations [9], [10] 3.WORKING PRINCIPLE There are Three Different Sections to be discussed…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Based Smart Survilance Robot

Ganesan¹,

Das²,

Kundu³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Surveillance Robot aims to blend IoT capabilities with the support of cloud and machine learning is an advancement to deliver a sophisticated solution for real time security. Industrial and commercial surveillance data security is required for small camera as well as large scale deployment with a drones or robot cars. This paper deals with face recognition using AWS Rekognition and video streaming using AWS kinesis and AWS SNS(Simple notification Service). AWS Rekognition uses deep learning algorithms to introspect the video stream and find objects / faces on them and compare it with the collection of information that it has trained previously. It detects face with video feed and scans the database to identify the person with AWS Rekognition ,there is also an option of adding new faces by uploading photo of the person to an S3 bucket and face can be indexed .

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“…Fog Computing [10], also known as Edge computing [43], extends the Cloud Computing paradigm to the "Edges" of the network, bringing in new opportunities to explore applications and services. By assisting the placement of storage and data processing closer to the data sources, Edge Computing brings in benefits in terms of latency and energy consumption [44]- [48], for instance.…”

Section: B the Role Of Edge/fog Computingmentioning

confidence: 99%

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

Silva

Sofia

2020

IEEE Access

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This paper debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the the networking operation between Cloud and Edge. Specifically, the paper debates on notions of contextawareness and goes over different types of context-awareness indicators that are being applied to Edge selection algorithms, covering the approaches currently used, the role of the algorithms applied, their scope, and contemplated performance metrics. Lastly, the paper provides guidelines for future research in the context of Cloud-Edge and the application of context-awareness to assist in a higher degree of automation of the network and, as consequence, a better support of the Cloud to Edge continuum. INDEX TERMS Context-awareness, Internet of Things (IoT), Edge/Fog Computing.

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What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Cited by 55 publications

References 274 publications

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Deep Learning Based Smart Survilance Robot

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

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