ABSTRACT As a new type of supply chain (SC) based on “Internet plus Innovation”, crowdsourcing supply chain (CSC) emphasizes mass participation and personalized demands more than traditional SC. Most of the current CSC systems are based on a centralized structure. With the development of crowdsourcing business, problems such as single point of failure, malicious data leakage or fairness are prone to occur. Deploying the CSC system onto the decentralized blockchain can solve the above problems to a certain extent. However, deploying CSC applications on the blockchain is facing issues like service matching efficiency and new security concerns. In this paper, a novel CSC platform is proposed based on ontology and blockchain. The matching of tasks and candidate workers is automatically achieved by designing some ontologies and semantic web rule language (SWRL) rules. The quality of the submitted solutions can be effectively evaluated by the proposed improved confidence‐weighted voting algorithm and semi‐monopoly dividend algorithm. To better ensure data confidentiality and identity anonymity, a task‐matching privacy protection algorithm combining ontology with proxy re‐encryption bilinear pairing technology is proposed. Finally, a software prototype is implemented on the Ethereum public test network by using the CSC dataset. The experimental results show that the time cost of the proposed scheme is within an acceptable range, while the gas consumption is saved by approximately 15%–25%.

The Social Internet of Things (SIoT) is a novel network that integrates social relations among objects and facilitates the interconnection between humans and smart devices through the Internet of Things (IoT). However, with the growing number of users and their devices in SIoT, ensuring high quality of service (QoS) and trust relations among users poses significant challenges. Therefore, this article proposes a SIoT services recommendation model based on trust and QoS. Our model combines user trust relations and QoS prediction, including the availability, reliability, and efficiency of services. To achieve this, we propose a three-layer services recommendation model consisting of a social network layer, a devices layer, and a services layer. We suggest using direct trust and joint trust to accurately calculate users' trust relations. Additionally, we introduce an Levenberg-Marquardt (L-M) -based algorithm for social network users' trust community clustering and a Random Service System (RSS) -based algorithm for QoS prediction. Experimental results show that our proposed model effectively recommends services in SIoT that exhibit high trustworthiness and quality.

Energy efficiency (EE) maximization problem for Cognitive Underwater Acoustic Network is investigated in this study. Available works on EE usually assume that spectrum sensing is accurate or that channel state information (CSI) is perfect, which is often impractical. Thus, an adaptive resource allocation scheme is proposed to maximize the EE, subject to the transmission power constraint of secondary user (SU) and the interference power constraint of primary user (PU). By taking the spectrum sensing errors into account, we add power interference from PU to SU in the objective function. Besides, interference tolerance factor is introduced to control the interference from SU to PU. Assuming CSI uncertainties of the involved channels are bounded, they are separately modeled as stochastic-case or worst-case according to their nature. Since the established optimization problem is nonconvex, it is converted into a convex one and then solved by the techniques of fractional programming and dual decomposition. Simulation results validate that the EE can be improved by classifying the CSI uncertainties and solving the expectation of the CSI correlation function. Furthermore, the interference from SU to PU can be controlled well by the adjustment of the interference tolerance factor.