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We propose an optimal destination scheduling scheme to improve the physicallayer security (PLS) of a power-line communication (PLC) basedInternet-of-Things system in the presence of an eavesdropper. We consider apinhole (PH) architecture for a multi-node PLC network to capture the keyholeeffect in PLC. The transmitter-to-PH link is shared between the destinationsand an eavesdropper which correlates all end-to-end links. The individualchannel gains are assumed to follow independent log-normal statistics.Furthermore, the additive impulsive noise at each node is modeled by anindependent Bernoulli-Gaussian process. Exact computable expressions for theaverage secrecy capacity (ASC) and the probability of intercept (POI)performance over many different networks are derived. Approximate closed-formexpressions for the asymptotic ASC and POI are also provided. We find that theasymptotic ASC saturates to a constant level as transmit power increases. Weobserve that the PH has an adverse effect on the ASC. Although the shared linkaffects the ASC, it has no effect on the POI. We show that by artificiallycontrolling the impulsive to background noise power ratio and its arrival rateat the receivers, the secrecy performance can be improved.

Destination Scheduling for Secure Pinhole-Based Power-Line Communication