Simulation files for the paper "Decision fusion with corrupted reports in multi-sensor networks: A game-theoretic approach" published at 53rd IEEE Conference on Decision and Control, Los Angeles, USA
Authors: Andrea Abrardo, Mauro Barni, Kassem Kallas, Benedetta Tondi
Abstract: Decision fusion in adversarial setting is receiving increasing attention due to its relevance in several applications including sensor networks, cognitive radio, social networks, distributed network monitoring. In most cases, a fusion center has to make a decision based on the reports provided by local agents, e.g. the nodes of a multi-sensor network. In this paper, we consider a setup in which the fusion center makes its decision on the status of an observed system by relying on the decisions made by a pool of local nodes and by taking into account the possibility that some nodes maliciously corrupt their reports to induce a decision error. We do so by casting the problem into a game-theoretic framework and looking for the existence of an equilibrium point defining the optimum strategies for the fusion center and the malicious nodes. We analyze two different strategies for the fusion center: a strategy recently introduced by Varshney et al. in a cognitive radio setup and a new approach based on soft identification of malicious nodes. The superior performance of the new decision scheme are demonstrated by resorting to the game-theoretic framework introduced previously.
To run the simulations, set the parameters in the file test_script.m
The paper can be found at: https://ieeexplore.ieee.org/document/7039431
For any inquiries please contact Dr. Kassem Kallas at [email protected]