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, attainment of resilient performance-assurances in the face of threats, adaptation to meet goals despite perturbations and model uncertainties, accurate learning in adversarial conditions, adversarial machine learning, formal verification of machine learning, optimization under uncertainty, and resilient cyber-physical-human information fusion of contaminated inputs.