Secure Mobile Edge Computing in IoT via Collaborative Online Learning

Bingcong Li, Tianyi Chen, Georgios B. Giannakis

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

To accommodate heterogeneous tasks for the Internet of Things (IoT), the emerging mobile edge paradigm extends computing services from the cloud to the edge, but at the same time exposes new challenges on security. In this context, the present paper deals with online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel approaches are developed to cope with adversarial worst-case attacks, and stochastic attacks with random attack strategies. Rather than relying on extra bandwidth and power resources to evade jamming attacks, the resultant algorithms select the most reliable server to offload computing tasks with minimal security concerns. It is analytically established that without any prior information on future jamming and server security risks over a time horizon T, the proposed schemes can achieve O(√ T) regret. Information sharing among devices can accelerate the security-aware computing tasks, quantified by what is termed 'value of cooperation.' Effectiveness of the proposed schemes is tested on synthetic and real datasets.

Original languageEnglish (US)
Article number8882321
Pages (from-to)5922-5935
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume67
Issue number23
DOIs
StatePublished - Dec 1 2019

Keywords

  • Cyber security
  • jamming
  • mobile edge computing
  • multi-armed bandit
  • online learning

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