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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 paper 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 ROC (receiver operating characteristic) 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 EM algorithm 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 classification 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.

Objective Brown adipose tissue (BAT) generates heat in response to cold, and low BAT activity has been linked to obesity. However, recent studies are inconclusive as to whether BAT is involved in diet-induced thermogenesis and mitigates weight gain from prolonged overeating. We therefore investigated whether BAT activity is related to metabolic adaptation arising from 8 weeks of overfeeding in humans. Methods Fourteen men (aged 24±3 years, BMI 24.5±1.6 kg/m2) were overfed by 40% for 8 weeks. Before and after, energy expenditure and metabolic adaptation were measured by whole-room respiratory calorimetry. A marker of BAT activity was measured using infrared imaging of the supraclavicular BAT depot. Results At the end of 8 weeks of overfeeding, metabolic adaptation—defined as the percent increase in sleeping energy expenditure beyond that expected from weight gain—rose from −0.9 ± 3.9% to 4.7±5.6% (p=0.001). However, BAT thermal activity was unchanged (p=0.81). Moreover, BAT thermal activity did not correlate with the degree of metabolic adaptation (p=0.32), nor with the change in body weight (p=0.51). Conclusions BAT thermal activity does not change in response to overfeeding, nor does it correlate with adaptive thermogenesis. Our data suggest that BAT does not mediate metabolic adaptation to overeating in humans.