2,053 research outputs found
A Message Passing Approach for Decision Fusion in Adversarial Multi-Sensor Networks
We consider a simple, yet widely studied, set-up in which a Fusion Center
(FC) is asked to make a binary decision about a sequence of system states by
relying on the possibly corrupted decisions provided by byzantine nodes, i.e.
nodes which deliberately alter the result of the local decision to induce an
error at the fusion center. When independent states are considered, the optimum
fusion rule over a batch of observations has already been derived, however its
complexity prevents its use in conjunction with large observation windows.
In this paper, we propose a near-optimal algorithm based on message passing
that greatly reduces the computational burden of the optimum fusion rule. In
addition, the proposed algorithm retains very good performance also in the case
of dependent system states. By first focusing on the case of small observation
windows, we use numerical simulations to show that the proposed scheme
introduces a negligible increase of the decision error probability compared to
the optimum fusion rule. We then analyse the performance of the new scheme when
the FC make its decision by relying on long observation windows. We do so by
considering both the case of independent and Markovian system states and show
that the obtained performance are superior to those obtained with prior
suboptimal schemes. As an additional result, we confirm the previous finding
that, in some cases, it is preferable for the byzantine nodes to minimise the
mutual information between the sequence system states and the reports submitted
to the FC, rather than always flipping the local decision
A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines
Optimum decision fusion in the presence of malicious nodes - often referred
to as Byzantines - is hindered by the necessity of exactly knowing the
statistical behavior of Byzantines. By focusing on a simple, yet widely
studied, set-up in which a Fusion Center (FC) is asked to make a binary
decision about a sequence of system states by relying on the possibly corrupted
decisions provided by local nodes, we propose a game-theoretic framework which
permits to exploit the superior performance provided by optimum decision
fusion, while limiting the amount of a-priori knowledge required. We first
derive the optimum decision strategy by assuming that the statistical behavior
of the Byzantines is known. Then we relax such an assumption by casting the
problem into a game-theoretic framework in which the FC tries to guess the
behavior of the Byzantines, which, in turn, must fix their corruption strategy
without knowing the guess made by the FC. We use numerical simulations to
derive the equilibrium of the game, thus identifying the optimum behavior for
both the FC and the Byzantines, and to evaluate the achievable performance at
the equilibrium. We analyze several different setups, showing that in all cases
the proposed solution permits to improve the accuracy of data fusion. We also
show that, in some instances, it is preferable for the Byzantines to minimize
the mutual information between the status of the observed system and the
reports submitted to the FC, rather than always flipping the decision made by
the local nodes as it is customarily assumed in previous works
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
In this paper, a sequential probing method for interference constraint
learning is proposed to allow a centralized Cognitive Radio Network (CRN)
accessing the frequency band of a Primary User (PU) in an underlay cognitive
scenario with a designed PU protection specification. The main idea is that the
CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire
the binary ACK/NACK packet. This feedback indicates whether the probing-induced
interference is harmful or not and can be used to learn the PU interference
constraint. The cognitive part of this sequential probing process is the
selection of the power levels of the Secondary Users (SUs) which aims to learn
the PU interference constraint with a minimum number of probing attempts while
setting a limit on the number of harmful probing-induced interference events or
equivalently of NACK packet observations over a time window. This constrained
design problem is studied within the Active Learning (AL) framework and an
optimal solution is derived and implemented with a sophisticated, accurate and
fast Bayesian Learning method, the Expectation Propagation (EP). The
performance of this solution is also demonstrated through numerical simulations
and compared with modified versions of AL techniques we developed in earlier
work.Comment: 14 pages, 6 figures, submitted to IEEE JSTSP Special Issue on Machine
Learning for Cognition in Radio Communications and Rada
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