While the Industrial Internet of Things (IIoT) brings convenience to industry, it also brings security problems. Due to the massive amount of data generated by the surge of IIoT devices, it is impossible to ensure whether these data contain attack or untrustworthy data, so how to ensure the security and trustworthiness of IIoT devices becomes an urgent problem to be solved. Here, we design a new hinge classification algorithm based on mini-batch gradient descent with an adaptive learning rate and momentum (HCA-MBGDALRM) to minimize the effects of security attacks. The algorithm significantly improves the performance of deep network training compared with traditional neural networks, decision trees and logistic regression in terms of scale and speed. In addition, we have solved the data skew problem in the shuffle phase, and we implement a parallel framework for HCA-MBGDALRM to accelerate the processing speed of very large traffic datasets.

Many companies and institutions currently use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues, as these auditors may be malicious and tamper with or delete user data. This poses a significant challenge to ensuring the reliability of data verification results. Although introducing a third-party auditor can help address this issue, this auditor may also be untrustworthy and collude with the database service provider to forge false data verification results. In this paper, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that information stored on the blockchain is immutable and cannot be tampered with maliciously. Additionally, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not impacted by any malicious parties. We also leverage game theory to improve the reliability of multiple verifications, reducing verification time and improving overall performance. Our proposed scheme reduces total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

In the process of the wireless sensor network research, the issue on the energy consumption and coverage is an essential and critical one. According to the characteristic of the sensor nodes, it is homogeneous, and we proposed the k-degree coverage algorithm based on optimization nodes deployment. First, the algorithm gives the solving procedure of the maximum seamless coverage ratio, when the three-node joint coverage has been provided. Second, when the sensor nodes are covering the monitoring area, the algorithm gives the solving procedure of the expected coverage quality and the judgment methods of the coverage ratio, when the nodes are compared with the nearby ones. And when there is redundancy coverage in the given monitoring area, we have given the solving procedure of any sensor nodes that exist in the redundant nodes coverage. Finally, using the simulation experiment, the results of the coverage algorithm based on optimization nodes deployment are compared with other algorithms in terms of the coverage quality and the network lifetime, the performance indexes have enhanced to 13.36% and 12.92% on average. Thus, the effectiveness and viability of the coverage algorithm based on optimization nodes deployment have been proved.

The node deployment in mobile sensor networks (MSNs) is mostly performed in a random method. However, a large number of redundant nodes may exist due to the randomness. As a result, severe data congestion may be caused and the quality of coverage (QoC) is undermined. In order to solve this QoC problem, we propose an Energy-efficient Nonlinear Coverage Control Protocol (ENCP). This protocol utilizes the normal distribution to calculate the minimal number of sensors which is required to guarantee coverage over the monitoring area. We also balance the node energy consumption and achieve the collaborative scheduling among all the sensor nodes. Meanwhile, when a certain QoC is guaranteed, we present the calculation model for the normal distribution of the sensing ranges and the proportional relationship between different parameters in the QoC function. Finally, simulation results show that the ENCP could not only improve the network QoC and network coverage rate but also effectively control the energy exhaustion at the nodes. Therefore, the network lifetime can be effectively prolonged.