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1. Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

   https://doi.org/10.1109/ICRA48891.2023.10160385  

   Cavorsi, Matthew ; Akgün, Orhan Eren ; Yemini, Michal ; Goldsmith, Andrea J. ; Gil, Stephanie ( May 2023 , 2023 IEEE International Conference on Robotics and Automation (ICRA))

   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 decisionmaking 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 toGoogleMaps, 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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2. Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots

   Cavorsi, M. ; Akgün, O.E. ; Yemini, M. ; Goldsmith, A. ; Gil, S. ( September 2022 , arXiv:2209.12285v1)

   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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3. Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model

   https://doi.org/10.1109/TSP.2021.3077302  

   Chen, Yicheng ; Blum, Rick S. ; Sadler, Brian M ( May 2021 , IEEE Transactions on Signal Processing)

   null (Ed.)

   A quickest change detection problem is considered in a sensor network with observations whose statistical dependency structure across the sensors before and after the change is described by a decomposable graphical model (DGM). Distributed computation methods for this problem are proposed that are capable of producing the optimum centralized test statistic. The DGM leads to the proper way to collect nodes into local groups equivalent to cliques in the graph, such that a clique statistic which summarizes all the clique sensor data can be computed within each clique. The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic. In order to further improve communication efficiency, an ordered transmission approach is proposed where transmissions of the clique statistics to the fusion center are ordered and then adaptively halted when sufficient information is accumulated. This procedure is always guaranteed to provide the optimal change detection performance, despite not transmitting all the statistics from all the cliques. A lower bound on the average number of transmissions saved by ordered transmissions is provided and for the case where the change seldom occurs the lower bound approaches approximately half the number of cliques provided a well behaved distance measure between the distributions of the sensor observations before and after the change is sufficiently large. We also extend the approach to the case when the graph structure is different under each hypothesis. Numerical results show significant savings using the ordered transmission approach and validate the theoretical findings. 

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4. Optimal Quickest Change Detection in Sensor Networks Using Ordered Transmissions

   https://doi.org/10.1109/SPAWC48557.2020.9154270  

   Chen, Yicheng ; Blum, Rick S. ; Sadler, Brian M. ( May 2020 , 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   Quickest change detection in a sensor network is considered where each sensor observes a sequence of random variables and transmits its local information on the observations to a fusion center. At an unknown point in time, the distribution of the observations at all sensors changes. The objective is to detect the change in distribution as soon as possible, subject to a false alarm constraint. We consider minimax formulations for this problem and propose a new approach where transmissions are ordered and halted when sufficient information is accumulated at the fusion center. We show that the proposed approach can achieve the optimal performance equivalent to the centralized cumulative sum (CUSUM) algorithm while requiring fewer sensor transmissions. Numerical results for a shift in mean of independent and identically distributed Gaussian observations show significant communication savings for the case where the change seldom occurs which is frequently true in many important applications. 

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5. Adaptive detection and accommodation of communication attacks on infinite dimensional systems with multiple interconnected actuator/sensor pairs

   https://doi.org/10.23919/ACC45564.2020.9147565  

   Demetriou, Michael A. ( July 2020 , 2020 American Control Conference (ACC))

   The work provides a general model of communication attacks on a networked infinite dimensional system. The system employs a network of inexpensive control units consisting of actuators, sensors and control processors. In an effort to replace a reduced number of expensive high-end actuating and sensing devices implementing an observer-based feedback, the alternate is to use multiple inexpensive actuators/sensors with static output feedback. In order to emulate the performance of the high-end devices, the controllers for the multiple actuator/sensors implement controllers which render the system networked. In doing so, they become prone to communication attacks either as accidental or deliberate actions on the connectivity of the control nodes. A single attack function is proposed which models all types of communication attacks and an adaptive detection scheme is proposed in order to (i) detect the presence of an attack, (ii) diagnose the attack and (iii) accommodate the attack via an appropriate control reconfiguration. The reconfiguration employs the adaptive estimates of the controller gains and restructure the controller adaptively in order to minimize the detrimental effects of the attack on closed-loop performance. Numerical studies on a 1D diffusion PDE employing networked actuator/sensor pairs are included in order to further convey the special architecture of detection and accommodation of networked systems under communication attacks. 

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