Contextual-bandit anomaly detection for IoT data in distributed hierarchical edge computing

Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q.S. Quek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff. 1

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 40th International Conference on Distributed Computing Systems, ICDCS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1227-1230
Number of pages4
ISBN (Electronic)9781728170022
DOIs
Publication statusPublished - 2020 Nov
Event40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020 - Singapore, Singapore
Duration: 2020 Nov 292020 Dec 1

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2020-November

Conference

Conference40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020
CountrySingapore
CitySingapore
Period20-11-2920-12-01

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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