A Counter-Eavesdropping Technique for Optimized Privacy of Wireless Industrial IoT Communications

The Industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issues of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this paper is on establishing efficient privacy in an IIoT-MIMOME communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as Optimal Counter-Eavesdropping Channel Approximation (OPCECA) technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.