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M. Pajic, I. Lee, G. J. Pappas.  2017.  Attack-Resilient State Estimation for Noisy Dynamical Systems. IEEE Transactions on Control of Network Systems. 4:82-92.
F. Miao, Q. Zhu, M. Pajic, G. J. Pappas.  2017.  Coding Schemes for Securing Cyber-Physical Systems Against Stealthy Data Injection Attacks. IEEE Transactions on Control of Network Systems. 4:106-117.
S. Han, U. Topcu, G. J. Pappas.  2017.  Quantification on the efficiency gain of automated ridesharing services. 2017 American Control Conference (ACC). :3560-3566.
F. Miao, S. Han, S. Lin, G. J. Pappas.  2015.  Robust taxi dispatch under model uncertainties. 2015 54th IEEE Conference on Decision and Control (CDC). :2816-2821.
C. Nowzari, J. Cortes, G. J. Pappas.  2015.  Team-triggered coordination of robotic networks for optimal deployment. acc. :5744-5751.

This paper introduces a novel team-triggered algorithmic solution for a distributed optimal deployment problem involving a group of mobile sensors. Distributed self-triggered algorithms relieve the requirement of synchronous periodic communication among agents by providing opportunistic criteria for when communication should occur. However, these criteria are often conservative since worst-case scenarios must always be considered to ensure the monotonic evolution of a relevant objective function. Here we introduce a team-triggered algorithm that builds on the idea of `promises' among agents, allowing them to operate with better information about their neighbors when they are not communicating, over a dynamically changing graph. We analyze the correctness of the proposed strategy and establish the same convergence guarantees as a coordination algorithm that assumes perfect information at all times. The technical approach relies on tools from set-valued stability analysis, computational geometry, and event-based systems. Simulations illustrate our results.