We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved.

翻译：我们提出了一种最优目的端调度方案，用于提升存在窃听者时基于电力线通信的物联网系统的物理层安全性。针对多节点电力线网络，我们采用微孔架构以捕捉电力线通信中的密钥孔效应。发射端与微孔之间的链路由所有目的端与窃听者共享，这一特性使得所有端到端链路相互关联。假设各信道增益服从独立对数正态分布。此外，每个节点的加性脉冲噪声由独立的伯努利-高斯过程建模。推导了不同网络场景下平均保密容量和截获概率的精确可计算表达式，同时给出了渐进保密容量与渐进截获概率的近似闭式表达式。研究发现，随着发射功率增加，渐进保密容量趋于常数水平。观察表明，微孔对保密容量具有不利影响。尽管共享链路影响保密容量，但对截获概率无影响。通过人为调控接收端的脉冲噪声与背景噪声功率比及其到达率，可有效提升系统安全性能。