Artificial Intelligence-Powered Mobile Edge Computing-Based Anomaly Detection in Cellular Networks

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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An Artificial Intelligence Perspective on Mobile Edge Computing

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) ◽

10.1109/smartiot.2019.00024 ◽

2019 ◽

Cited By ~ 3

Author(s):

Zhuang Chen ◽

Qian He ◽

Lei Liu ◽

Dapeng Lan ◽

Hwei-Ming Chung ◽

...

Keyword(s):

Artificial Intelligence ◽

Edge Computing ◽

Mobile Edge Computing

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Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

Information ◽

10.3390/info10080259 ◽

2019 ◽

Vol 10 (8) ◽

pp. 259 ◽

Cited By ~ 2

Author(s):

Jie Yuan ◽

Erxia Li ◽

Chaoqun Kang ◽

Fangyuan Chang ◽

Xiaoyong Li

Keyword(s):

Cellular Networks ◽

Mobile Networks ◽

Network Architecture ◽

Social Trust ◽

Edge Computing ◽

Mobile Edge Computing ◽

Internet Technologies ◽

Architecture Model ◽

Security Issues ◽

Cooperative Relationship

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

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