IEEE Military Communications Conference
28 October – 1 November 2024 // Washington, DC, USA
C5I Technologies for Military and Intelligence Operations Today and Tomorrow

WS-06: Workshop on the Internet of Things for Adversarial Environments

WS-06: Workshop on the Internet of Things for Adversarial Environments

This workshop solicits original work that advances the science of dynamically composing, operating, adapting, and assessing future intelligent, mission-critical IoT applications that operate in harsh, unfriendly, or adversarial environments. The motivating application examples include disaster response, first-responder support, rescue management, extreme environmental monitoring (e.g., monitoring volcanoes, nuclear plants, bio-chemical incidents, or contagious disease outbreaks), and systems that, by their very nature, are subject to frequent adversarial action such as security/anti-theft systems, intrusion detection systems, anti-jamming systems, and defense systems. A common thread across the above systems is the need for high resilience in the face of a broad array of threats, human or environmental. By soliciting original research on attaining resilient and dependable operation in such a broad spectrum of harsh IoT application contexts, the workshop aims to help the research community collectively distill fundamental insights, key concepts, best practices, and analytical foundations to support a next generation of IoT services for mission-critical applications in adversarial environments. Challenges such as heterogeneity, scale, and fast evolving dynamics are of great interest. Contributions may include, but are not limited to: 

- Resilient performance-assurances in the face of threats

- Robust edge AI capabilities

- Robust exploitation of LLMs in IoT settings

- Foundation models for mission-critical IoT systems

- IoT adaptation to meet goals despite perturbations

- Handling model and environment uncertainty

- Accurate learning in adversarial conditions (adversarial machine learning)

- Formal verification of machine learning

- Optimization under uncertainty

- Resilient cyber-physical-human information fusion of contaminated inputs.