Dependable machine intelligence at the tactical edge

Archan Misra, Kasthuri Jayarajah, Dulanga Weerakoon, Randy Tandriansyah, Shuochao Yao, Tarek Abdelzaher

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


The paper describes a vision for dependable application of machine learning-based inferencing on resource-constrained edge devices. The high computational overhead of sophisticated deep learning learning techniques imposes a prohibitive overhead, both in terms of energy consumption and sustainable processing throughput, on such resource-constrained edge devices (e.g., audio or video sensors). To overcome these limitations, we propose a "cognitive edge" paradigm, whereby (a) an edge device first autonomously uses statistical analysis to identify potential collaborative IoT nodes, and (b) the IoT nodes then perform real-Time sharing of various intermediate state to improve their individual execution of machine intelligence tasks. We provide an example of such collaborative inferencing for an exemplar network of video sensors, showing how such collaboration can significantly improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. We also identify various challenges in realizing such a cognitive edge, including the need to ensure that the inferencing tasks do not suffer catastrophically in the presence of malfunctioning peer devices. We then introduce the soon-To-be deployed Cognitive IoT testbed at SMU, explaining the various features that enable empirical testing of various novel edge-based ML algorithms.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications
EditorsTien Pham
ISBN (Electronic)9781510626775
StatePublished - 2019
EventArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceArtificial Intelligence and Machine Learning for Multi-Domain Operations Applications 2019
Country/TerritoryUnited States


  • Cognitive Edge
  • Collaborative Sensing
  • Machine Learning
  • Tactical Edge
  • Video Analytics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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