Wireless sensor networks (WSNs) play an important role in the Industrial Internet of Things (IIoT), and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only accessed by valid user becomes an key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous more or less have their own weaknesses, such as not providing user anonymity and other ideal functions, or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

In IoT-based healthcare, medical devices are more vulnerable to numerous security threats and attacks than other network devices. Current solutions are able to provide protection to patients' data during data transmission to some extent, but cannot prevent some sophisticated threats and attacks such as collusion attacks and data leakage. In this article, we first investigate the challenges with privacy protected data collection. Then we propose a practical framework called PrivacyProtector, patient privacy protected data collection, with the objective of preventing these types of attacks. PrivacyProtector includes the ideas of secret sharing and share repairing (in case of data loss or compromise) for patients' data privacy. Since it is the first time, we apply the Slepian- Wolf-coding-based secret sharing (SW-SSS) in PrivacyProtector. In the framework, we use a distributed database consisting of multiple cloud servers, which ensures that the privacy of patients' personal data can remain protected as long as one of the servers remains uncompromised. We also present a patient access control scheme in which multiple cloud servers collaborate in shared construction to offer patients' data to healthcare providers without revealing the content of the data. The privacy performance analysis has shown that the PrivacyProtector framework is secure and privacy-protected against various attacks.

The IoT-Cloud combines the Internet of Things (IoT) and cloud computing, which not only enhances the IoT’s capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

The development of the Internet of Things (IoT) and intelligent vehicles bring a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this paper, we consider the vehicular computation offloading problem in mobile edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multi-agent deep reinforcement learning (DRL) based computation offloading scheme, in which the uncertainty of a multi-vehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. Firstly, we formalize a formula for the computation offloading problem. The goal of this paper is to determine the optimal offloading decision to MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multi-agent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.