Toolbox Release: A WiFi-Based Relative Bearing Framework for Robotics

Jadhav, Ninad; Wang, Weiying; Zhang, Diana; Kumar, Swarun; Gil, Stephanie

doi:10.1109/iros47612.2022.9981230

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Malicious robots perform a Sybil attack where they spoof additional robots into the network. We use the opensource toolbox in [53] to obtain trust values from communicated WiFi signals by analyzing the similarity between different fingerprints to detect spoofed transmissions. The works in [31]- [33] model these trust values α i ∈ [0, 1] as a continuous random variable.…”

Section: Hardware Experiments and Numerical Resultsmentioning

confidence: 99%

“…We determined the parameters in Table I by first running an experiment without performing hypothesis tests and observing the behavior of the system compared to ground truth. The trust values gathered using the toolbox in [53] led to the empirical probabilities p α (a i = 1|t i = 1) = 0.8350 and p α (a i = 1|t i = 0) = 0.1691 (see Fig. 4).…”

Section: A Hardware Experimentsmentioning

confidence: 99%

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Adaptive Malicious Robot Detection in Dynamic Topologies

Cavorsi

Jadhav

Saldaña

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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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.

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Section: Hardware Experiments and Numerical Resultsmentioning

confidence: 99%

Section: A Hardware Experimentsmentioning

confidence: 99%