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Mobile Edge Computing

Here are the basic concept of mobile edge computing (MEC) and some useful resources for MEC.

Background of MEC:

Many IoT devices may have limited or no computing capability and some IoT applications (e.g., autonomous driving and AR) require low latency in the control, communication, and computing services.

Owing to a large amount of data and tremendous devices, MEC has been proposed to improve the Quality of Service (QoS) of users at the edge, e.g., the macro base station (MBS) is equipped with computing resources to provide services to UEs or wireless devices.

UAV-aided MEC provides better wireless connections (line-of-sight) to UEs and more flexibility in the implementation as compared to the traditional MEC where the computing facilities are only available at the MBS.

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For more details, please refer to the reference below:
L. Zhang and N. Ansari, “Latency-Aware IoT Service Provisioning in UAV-Aided Mobile-Edge Computing Networks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10573-10580, Oct. 2020.

Userful Presentation Slides:
ICC_2022
May 18, 2022 ICC 2022, Seoul, South Korea

Machine Learning Driven Latency Optimization for Application-aware Edge Computing-based IoTs

WCNC_2022
April 13, 2022 WCNC 2022, Austin, USA

Machine Learning Driven UAV-assisted Edge Computing

ICC_2022
Mar. 2021 IEEE IoT, Oct. 2020

Latency-aware Service Provisioning in UAV Mobile Edge Computing

Presentation PPT Download:
  1. 2022-05-18 Machine Learning Driven Latency Optimization for Application-aware Edge Computing-based IoTs ― IEEE ICC 2022
  2. 2022-04-13 Machine Learning Driven UAV-assisted Edge Computing ― IEEE WCNC 2022
  3. 2020-10 Latency-aware Service Provisioning in UAV Mobile Edge Computing ― IEEE IoT 2020

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Mobile Edge Computing Publications:

Journals Articles:
  1. L. Zhang, B. Jabbari and N. Ansari, “Deep Reinforcement Learning Driven UAV-assisted Edge Computing,” IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25449-25459, Dec. 2022.
  2. L. Zhang and J. Chakareski, “UAV-Assisted Edge Computing and Streaming for Wireless Virtual Reality,” IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3267-3275, Mar. 2022.
  3. L. Zhang and N. Ansari, “Optimizing the Operation Cost for UAV-aided Mobile Edge Computing,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6085-6093, Jun. 2021.
  4. L. Zhang and N. Ansari, “Latency-Aware IoT Service Provisioning in UAV-Aided Mobile-Edge Computing Networks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10573-10580, Oct. 2020.
Conference Papers:
  1. L. Zhang and B. Jabbari, “Machine Learning Driven Latency Optimization for Application-aware Edge Computing-based IoTs,” Proc. IEEE International Conference on Communications (ICC), pp. 183-188, May 2022.
  2. L. Zhang, B. Jabbari and N. Ansari, “Machine Learning Driven UAV-assisted Edge Computing,” Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 2220-2225, Apr. 2022.