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Abstract \ Wireless sensor networks are prone to node misbehaviorarising from tampering by an adversary (Byzantineattack), or due to other factors such as node failure resulting fromHardware or software degradation. In this system, we consider theproblem of decentralized detection in wireless sensor networksin the presence of one or more classes of misbehaving nodes.Binary hypothesis testing is considered where the honest nodestransmit their binary decisions to the fusion center (FC), while themisbehaving nodes transmit fictitious messages. The goal of theFC is to identify the misbehaving nodes and to detect the state ofnature. We identify each class of nodes with an operating point(false alarm and detection probabilities) on the receiver operatingcharacteristic (ROC) curve. Maximum likelihood estimation of thenodes’ operating points is then formulated and solved using theexpectation maximization (EM) algorithm with the nodes’ identitiesas latent variables.
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Decentralized Hypothesis Testing in Wireless Sensor Networks in the Presence of Misbehaving Nodes
Abstract \ Wireless sensor networks are prone to node misbehavior arising from tampering
by an adversary (Byzantine attack), or due to other factors such as node failure resulting
from Hardware or software degradation. In this system, we consider the problem of
decentralized detection in wireless sensor networks in the presence of one or more
classes of misbehaving nodes. Binary hypothesis testing is considered where the honest
nodes transmit their binary decisions to the fusion center (FC), while the misbehaving
nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving
nodes and to detect the state of nature. We identify each class of nodes with an
operating point (false alarm and detection probabilities) on the receiver operating
characteristic (ROC) curve. Maximum likelihood estimation of the nodes’ operating
points is then formulated and solved using the expectation maximization (EM)
algorithm with the nodes’ identities as latent variables. The solution from the
EMalgorithm is then used to classify the nodes and to solve the decentralized
hypothesis testing problem. Numerical results compared with those from the
reputation-based schemes show a significant improvement in both classifications of the
nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node
classification problem which can be resolved if the honest nodes are in majority.
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