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Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

IEEE Internet of Things Journal · 2020 · Vol. 7(8) · pp. 6722–6747
Yaqiong LiuMugen PengGuochu ShouYudong ChenSiyu Chen

Abstract

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this article investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.

IoT and Edge/Fog ComputingIoT Networks and ProtocolsMolecular Communication and NanonetworksComputer scienceEdge computingMobile edge computingCloud computingComputer networkEnhanced Data Rates for GSM EvolutionEdge deviceKey (lock)Software-defined networkingContext (archaeology)

Funding

  • National Natural Science Foundation of China
  • Beijing Key Laboratory of Network System Architecture and Convergence
  • Higher Education Discipline Innovation Project
Citations
495
FWCI
54.03
field-weighted impact
References
187
Percentile
100%
vs. same field & year
Citations per year
References
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