Secure mobile edge server placement using multi-agent reinforcement learning

Mumraiz Khan Kasi*, Sarah Abu Ghazalah, Raja Naeem Akram, Damien Sauveron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

Original languageEnglish
Article number2098
Number of pages19
JournalElectronics (Switzerland)
Volume10
Issue number17
Early online date30 Aug 2021
DOIs
Publication statusPublished - 30 Aug 2021

Keywords

  • Edge security
  • Mobile edge computing
  • Mobile edge server placement
  • Multiagent RL

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