  We develop a resilient binary hypothesis testing framework for decision
making in adversarial multi-robot crowdsensing tasks. This framework exploits
stochastic trust observations between robots to arrive at tractable, resilient
decision making at a centralized Fusion Center (FC) even when i) there exist
malicious robots in the network and their number may be larger than the number
of legitimate robots, and ii) the FC uses one-shot noisy measurements from all
robots. We derive two algorithms to achieve this. The first is the Two Stage
Approach (2SA) that estimates the legitimacy of robots based on received trust
observations, and provably minimizes the probability of detection error in the
worst-case malicious attack. Here, the proportion of malicious robots is known
but arbitrary. For the case of an unknown proportion of malicious robots, we
develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses
both the reported robot measurements and trust observations to estimate the
trustworthiness of robots, their reporting strategy, and the correct hypothesis
simultaneously. We exploit special problem structure to show that this approach
remains computationally tractable despite several unknown problem parameters.
We deploy both algorithms in a hardware experiment where a group of robots
conducts crowdsensing of traffic conditions on a mock-up road network similar
in spirit to Google Maps, subject to a Sybil attack. We extract the trust
observations for each robot from actual communication signals which provide
statistical information on the uniqueness of the sender. We show that even when
the malicious robots are in the majority, the FC can reduce the probability of
detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively.
