The emerging paradigm Social Internet of Things (SIoT) integrates social networking elements into the Internet of Things, enabling smart devices to establish and manage interactions autonomously. This enhances collaboration and adaptability but also increases complexity and vulnerability, particularly from malicious devices exploiting these relationships. To address this, trust evaluation of devices becomes crucial. Traditional approaches, like weighted sums and Bayesian inference, struggle with the dynamic nature of SIoT environments. Recent advancements in Graph Neural Networks (GNNs) show promise, yet existing models often fail to capture the complexities of SIoT's dynamic and heterogeneous nature. In this paper, we propose TrustLive, a GNN-based framework for real-time trust evaluation in dynamic SIoT settings. TrustLive first employs a heterogeneous graph to represent smart devices and their interactions, which are then encoded via a customized graph embedding technique for trust feature extraction. It further incorporates Graph Convolutional Networks for trust aggregation and Temporal Convolutional Networks to capture trust evolution. Moreover, a Memory-Augmented Incremental Update mechanism is added to ensure low-latency updates by processing only the latest data while preserving accuracy with historical results. Experimental results demonstrate that TrustLive outperforms current methods in both accuracy and efficiency, offering a robust solution for trust evaluation in SIoT.

Centralized trading platforms have long been the preferred choice for users, despite growing concerns regarding data privacy. Users have to place their trust in these platforms and provide sensitive personal information, like identities and financial accounts. However, these centralized platforms often lack transparency, making it challenging to ensure fairness, privacy, and security against both external and internal risks. In contrast, a decentralized fair trading paradigm, harnessing the potential of blockchain technology, is rapidly emerging. It empowers individuals to engage in the exchange of digital assets with others while guaranteeing fairness, efficiency, and privacy. In this paper, we conduct a comprehensive survey of decentralized fair trading. We commence by providing fundamental definitions of fair trading and tracing its evolution over time. We then delve into the essential framework of on-chain and off-chain trading and highlight key improvements that enhance the efficiency of decentralized fair trading within various application scenarios. Furthermore, we undertake a thorough analysis of privacy and security enhancements within the scope, summarizing defenses against known attacks. Finally, we outline the challenges and offer insights into the future prospects of decentralized fair trading, with the aim of inspiring the development of more innovative and promising designs in this evolving trend.

Recently, blockchain-based decentralized mobile crowdsensing systems have emerged to eliminate traditional centralized trust and to achieve transparent task assignments via smart contracts. It allows workers to select tasks freely, thereby maximizing their benefits. However, prior designs rarely considered the globally optimal task assignment that significantly impacts the efficiency and quality of task performance, like maximizing the task completion ratio and minimizing the total travel distance of workers. So in this paper, we propose DCrowd, a new blockchain-based mobile crowdsensing system, to realize the decentralized, transparent, and globally optimal task assignment. In brief, we first introduce the Proof of Task Assignment consensus mechanism. This allows miners to conduct globally optimal task assignments off-chain, leverages smart contracts to perform lightweight verification for task assignment results on-chain, and stores the globally optimal task assignment in a customized block. Then, we devise the Weight-Prioritized Task Selection strategy and Threshold-based Adaptive Minimum Cost Flow algorithm, to further optimize the system performance and guide miners in competing for minting rights. A thorough theoretical analysis is provided. Extensive experiments on real-world datasets indicate that DCrowd can reduce the broadcast and consensus latency by over 50% and improve the throughput by over 87% compared with existing systems.

An increasing number of energy-recycling consensus mechanisms are being employed to address the drawback of proof of work (PoW) wasting computation and energy. For instance, the computing power wasted in solving difficult but meaningless PoW puzzles is used to conduct practical federated learning tasks and train deep learning models. However, there remains a neglected issue of task difficulty adjustment. To address this problem, we propose a method for measuring task difficulty and an algorithm for adjustment to achieve controlled minting and stable transaction processing capacity for cryptocurrency based on energy-recycling consensus mechanisms. Our research evaluates the effectiveness of this algorithm and highlights the potential benefits of this approach.