Fog assisted task allocation and secure deduplication using 2FBO2 and MoWo in clusterbased industrial IoT (IIoT) Abstract   Fogassisted InternetofThings have dynamically received interest in the research community. There are more duplicate data transmitted over the Internet due to the rise in IoT devices. In this paper, task allocation and secure deduplication are implemented over four layers of Fog assisted Clusterbased Industrial IoT. IoT device layer used to sense data and mitigate security threats. Devices presented in this layer are registered to the cloud server using Elliptic Curve Cryptography based Hybrid Multiplier. MultiObjective based Whale Optimization algorithm is accessible for cluster section. SHA3 is presented in the fog layer for secure data deduplication. ECC HM private key is used for data encryption before transmitted the data. In cloud, layer indexing is constructed using Merkle Hash Tree. This is endeavored to provide query search results for IoT users at the service layer. The simulation results prove enhancement in average latency, user satisfaction, network lifetime, energy consumption, and security strength.

Robust and Efficient Privacy Preservation in Industrial IoT via correlation completion and tracking The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog  cloud computing platforms, and artificial intelligence (AI) to create smart, selfoptimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. In this work we address the problem of efficiently and effectively tracking the structure of multivariate streams recorded in a network of IIoT devices. The time varying correlation data values are used to add noise which maximally preserves privacy, in the sense that it is very hard to be removed. To improve communication efficiency between connected IoT devices, we exploit low rank properties of the correlation matrices, and track the essential correlations from a small subset of correlation values estimated by a subset of network nodes. Extensive simulation studies, validate the correctness, efficiency, and effectiveness of our approach in terms of computational complexity, transmission energy efficiency and privacy preservation.