Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

Sabella, Dario; Vaillant, Alessandro; Kuure, Pekka; Rauschenbach, Uwe; Giust, Fabio

doi:10.1109/mce.2016.2590118

Cited by 333 publications

(150 citation statements)

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“…These studies are quite shallow in addressing the MEC integration with IoT, they are mostly focusing on the requirements and usability of MEC in IoT applications. In this short magazine article [21], the authors discuss the To the best of our knowledge there is not a single survey which addresses broader range of areas about MEC and its influence on IoT realization. Since both MEC and IoT are very essential to the realization of 5G, it is vital to express their associativity in terms of application scenarios and key technical attributes.…”

Section: B Paper Motivationmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

594

328

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The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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Section: B Paper Motivationmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

594

328

View full text Add to dashboard Cite

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“…The concept and motivation behind edge computing has been described in a number of recent publications [1,[3][4][5][6][42][43][44][45]. However, there are very few works that present a distributed IoT system for video analytics and real-time tracking.…”

Section: B Iot Systems For Edge Video Analyticsmentioning

confidence: 99%

REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Neff

Mendieta

Mohan

et al. 2020

IEEE Internet Things J.

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This article presents REVAMP 2 T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacybuilt-in decentralized situational awareness. REVAMP 2 T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP 2 T proposes a unified integrated computer vision pipeline for detection, re-identification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and re-identifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP 2 T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy • Efficiency (AE), for holistic evaluation of IoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP 2 T outperforms current state-of-the-art by as much as thirteen-fold AE improvement.

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“…Industry 4.0 [75], [76], [77] and Industrial Internet Consortium (IIC) [78], [79] have considered standards for practical applications on M2M in an industrial domain; they are proactively trying to develop new application platforms for manufacturing floors, such as factories, manufacturing facilities, etc. In the future, direct communication mechanisms among different M2M devices are required to realize flexible and scalable service systems among a number of service domains [80]. Table II summarizes the relationship between M2M and IoT standards in a layered fashion.…”

Section: A Standards and Technologiesmentioning

confidence: 99%

Is Fragmentation a Threat to the Success of the Internet of Things?

Aly

Khomh

Guéhéneuc

et al. 2019

IEEE Internet Things J.

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Internet of Things (IoT) aims to bring connectivity to almost every objects, i.e., things, found in the physical space. It extends connectivity to everyday things, however, such increase in the connectivity creates many prominent challenges.Context: Generally, IoT opens the door for new applications for machine-to-machine (M2M) and human-to-human communications. The current trend of collaborating, distributed teams through the Internet, mobile communications, and autonomous entities, e.g., robots, is the first phase of the IoT to develop and deliver diverse services and applications. However, such collaborations is threatened by the fragmentation that we witness in the industry nowadays as it brings difficulty to integrate the diverse technologies of the various objects found in IoT systems. Diverse technologies induce interoperability issues while designing and developing various services and applications, hence, limiting the possibility of reusing the data, more specifically, the software (including frameworks, firmware, APIs, user interfaces) as well as of facing issues, like security threats and bugs, when developing new services or applications. Different aspects of handling data collection ranging from discovering smart sensors for data collection, integrating and applying reasoning on them must be available to provide interoperability and flexibility to the diverse objects interacting in the system. However, such approaches are bound to be challenged in future IoT scenarios as they bring substantial performance impairments in settings with the very large number of collaborating devices and technologies.Objective: We raise the awareness of the community about the lack of interoperability among technologies developed for IoT and challenges that their integration poses. We also provide guidelines for researchers and practitioners interested in connecting IoT networks and devices to develop services and applications.Method: We apply the methods advocated by the Evidencebased Software Engineering paradigm (EBSE). This paradigm and its core tool, the Systematic Literature Review (SLR), were introduced to the sofware-engineering research community early 2004 to help researchers and industry systematically and objectively gather and aggregate evidences about different topics. In this paper, we conduct a SLR of both IoT interoperability issues and the state-of-practice of IoT technologies in the industry, highlighting the integration challenges related to the IoT that have significantly shifted the landscape of Internet-based collaborative services and applications nowadays.Results: Our SLR identifies a number of studies from journals, conferences, and workshops with the highest quality in the Manuscript field. This SLR reports different trends, including frameworks and technologies, for the IoT for better comprehension of the paradigm and discusses the integration and interoperability challenges across the different layers of this technology while shedding light on the current IoT state-of-practice. It also discus...

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Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

Cited by 333 publications

References 0 publications

Survey on Multi-Access Edge Computing for Internet of Things Realization

Survey on Multi-Access Edge Computing for Internet of Things Realization

REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Is Fragmentation a Threat to the Success of the Internet of Things?

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Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

Cited by 333 publications

References 0 publications

Survey on Multi-Access Edge Computing for Internet of Things Realization

Survey on Multi-Access Edge Computing for Internet of Things Realization

REVAMP2T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking

Is Fragmentation a Threat to the Success of the Internet of Things?

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Product

Resources

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REVAMP²T: Real-Time Edge Video Analytics for Multicamera Privacy-Aware Pedestrian Tracking