Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2019

Abstract

Many IoT networks, including for battlefield deployments, involve the deployment of resource-constrained sensors with varying degrees of redundancy/overlap (i.e., their data streams possess significant spatiotemporal correlation). Collaborative intelligence, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). Using realworld data from a multicamera deployment, we first demonstrate the significant performance gains (up to 14% increase in accuracy) from such collaborative intelligence, achieved through two different approaches: (a) one involving statistical fusion of outputs from different nodes, and (b) another involving the development of new collaborative deep neural networks (DNNs). We then show that these collaboration-driven performance gains susceptible to adversarial behavior by one or more nodes, and thus need resilient mechanisms to provide robustness against such malicious behavior. We also introduce an underdevelopment testbed at SMU, specifically designed to enable realworld experimentation with such collaborative IoT intelligence techniques.

Keywords

Constrained sensors, Correlated observations, Energy overheads, IOT networks, Multi-cameras, Performance Gain, Performance metrics, Spatiotemporal correlation

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

22nd International Conference on Information Fusion, FUSION 2019, Ottawa, Canada, July 2-5

First Page

1

Last Page

9

ISBN

9780996452786

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://ieeexplore.ieee.org/document/9011397

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