Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2019
Abstract
In many practical environments, resource-constrained IoT nodes are deployed with varying degrees of redundancy/overlap--i.e., their data streams possess significant spatiotemporal correlation. We posit that collaborative inferencing, 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). However, such collaborative models are vulnerable to adversarial behavior by one or more nodes, and thus require mechanisms that identify and inoculate against such malicious behavior. We use a dataset of 8 outdoor cameras to (a) demonstrate that such collaborative inferencing can improve people counting accuracy by over 8%, and (b) show how a dynamic reputation mechanism preserves such gains even if some cameras behave maliciously.
Keywords
Internet of Things, Vision Sensing, Edge Computing, Deep Learning
Discipline
Artificial Intelligence and Robotics
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 1st International Workshop on Internet of Things for Adversarial Environments, INFOCOM, Paris, France, 2019 April 29 - May 2
First Page
1
Last Page
6
Publisher
IEEE
City or Country
Paris, France
Citation
MISRA, Archan; WEERAKOON MUDIYANSELAGE, Dulanga Kaveesha Weerakoon; and JAYARAJAH, Kasthuri.
The challenge of collaborative IoT-based inferencing in adversarial settings. (2019). Proceedings of the 1st International Workshop on Internet of Things for Adversarial Environments, INFOCOM, Paris, France, 2019 April 29 - May 2. 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/4787
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.