The mobilization of Internet of Things (IoT) data is currently stifled by a fundamental market friction: the inherent value uncertainty of data assets. Economically, data functions as an experience good; its true utility is highly context-dependent and verifiable only after consumption. In dynamic environments, this opacity creates High-Entropy characteristics, where the variance of realized utility, stemming from factors ranging from sensor noise to model incompatibility, renders traditional exante pricing inefficient. Risk-averse buyers, unable to assess quality prior to purchase, rationally exit the market, leading to a “Market for Lemons.” To resolve this deadlock, we propose Trustworthy Data Equity, a decentralized protocol that shifts the transaction paradigm from static pricing to retrospective risk-hedging. We model the trading process as a Stackelberg game in a bilateral setting (single buyer–single seller) and derive a closed-form equilibrium for an optimal equity share (α∗), which achieves the Pareto-efficient risk allocation between participants based on their relative risk aversion. While α∗is invariant to the estimated variance, as in classical insurance theory, the base fee (p∗) adapts directly to market risk conditions, governing both value capture and market feasibility. To implement this trustlessly, we introduce a hybrid architecture leveraging Blockchain for immutable settlement and Trusted Execution Environments for privacy-preserving, ex-post utility verification. Our empirical evaluation highlights three core contributions. First, the protocol exhibits strong Economic Robustness by significantly extending market viability under extreme uncertainty, effectively preventing market collapse where traditional mechanisms fail. Second, we identify and mitigate the “Scarcity Multiplier,” a structural inequity that penalizes data-scarce small and medium-sized enterprises, thereby promoting Market Fairness and Inclusivity. Finally, our prototype confirms System Feasibility, achieving constant-time (O(1)) on-chain settlement complexity and sub-second latency, proving the architecture’s scalability for high-frequency IoT data markets.

Effective management of private keys is crucial to ensure the security and ownership of users’ data and digital assets in the Web3 environment. However, existing solutions often fail to adequately address private key management from the user’s perspective. Private key leakage and loss incidents occur frequently, resulting in significant losses of digital assets. Moreover, the conventional approach of revoking both the private and public keys after a leakage or loss accident is inconvenient in Web3, where the public key serves as the user’s wallet address or digital identity. To tackle the issue of user-side private key management in Web3, this paper presents KeyShield which is a leakage-and-loss-resilient private key protection scheme. KeyShield divides the user’s private key into three shares, securely stored across a primary device and a secondary device owned by the user, and a third storage module owned by the user or a semi-trusted service provider. For daily use of the private key, the user only needs to connect the primary and secondary devices. In the event of a leakage or loss, such as device theft or attack, an update process will be triggered to update the three shares, immediately invalidating the leaked or lost share while causing no changes to the public key. As a demonstration of KeyShield, we developed KeyShieldECC accessible on both Android and iOS platforms for managing Elliptic Curve Cryptography (ECC) private keys. The testing results show that for a 256-bit ECC private key, the daily use only needs 0.05 seconds and update needs 0.25 to 0.3 seconds on an ordinary smart phone.

The ever-increasing demand for different Internet of Things (IoT) platforms while satisfying the personalised quality of services has attracted both industry and academic interests. Especially for the personalised travel system of the tourism area, the existing tourism recommendation systems often struggle to provide highly relevant suggestions due to limitations in understanding complex and varied user preferences. Therefore, developing a personalized tourism recommendation platform that satisfies user privacy-protecting requirements presents a necessity. In this paper, we propose a blockchain-supported personalized tourism recommendation platform that integrates semantic search models with blockchain technology. Our approach combines semantic similarity and contextual similarity using advanced natural language processing (NLP) techniques, such as word embeddings, RoBERTa models, and attention mechanisms, to align entities effectively across multiple datasets. This integration ensures a deeper understanding of user inputs, overcoming the limitations of traditional keyword-based matching. Preliminary experiments suggest that our system significantly improves recommendation performance while maintaining transparency and accountability, offering a novel solution to the challenges facing current tourism recommendation systems.

The integration and sharing of chemical-biological data have become increasingly crucial in advancing research in drug discovery, materials science, and catalysis. However, several key challenges persist, including ensuring accurate semantic alignment, protecting data privacy, and providing reliable decision support. Addressing these challenges is essential to improving the efficiency and security of data-sharing and analysis processes. This paper proposes a comprehensive solution that leverages deep learning for semantic alignment and blockchain technology for secure data sharing. Our platform utilizes natural language processing (NLP) and graph neural networks (GNN) to align heterogeneous chemical-biological datasets, ensuring consistency and completeness across different sources. Additionally, blockchain technology is employed to establish a decentralized and tamper-resistant data-sharing framework, enhancing security and trust among stakeholders. Through extensive experimentation using the Open Catalyst dataset, our results demonstrate the effectiveness of the proposed approach in achieving high-precision data alignment, secure data transactions, and reliable decision support. This work presents an innovative and integrated platform that addresses long-standing challenges in chemical-biological data integration and sharing, paving the way for more efficient and secure collaborative research.

Autonomous exploration is a critical application of multi-vehicle systems, where a team of networked robots collaboratively explores an unknown environment. This technique is crucial for applications like search and rescue, fault detection, and mapping. Traditional frontier-based methods struggle with cooperative exploration, while multi-agent deep reinforcement learning (MARL) approaches suffer from inefficient task assignments due to shortsighted decision-making and lack of long-term planning. To address these limitations, we propose HAFE (Hierarchical Attention-based Frontier Exploration), a hierarchical reinforcement learning (HRL) framework that integrates temporal decision modeling into multi-robot exploration. The high-level policy, built on an LSTM-Attention network, captures long-term dependencies in exploration history and dynamically allocates goal frontiers for each robot, ensuring structured and non-redundant coverage. The low-level MADDPG policy then executes navigation and obstacle avoidance. Additionally, we introduce frontier utility embedding into the state representation, enabling robots to prioritize unexplored regions more effectively. Experimental results in Gazebo and ROS demonstrate that HAFE significantly outperforms MADDPG, achieving faster coverage, higher rewards, and fewer collisions by leveraging historical information for more informed goal selection.

This research introduces a cutting-edge Web3 literary analysis platform, harnessing the power of blockchain and deep learning technologies. By employing the immutable and transparent nature of blockchain, the platform ensures robust copyright protection while offering readers enhanced interactive features. It applies deep learning techniques for comprehensive analyses of sentiment, topic, and stylistic elements, which are instrumental in predicting potential Nobel Prize laureates. This methodology not only enhances the accuracy of predictions but also sheds light on the evaluation criteria and historical trends associated with the Nobel Prize. Moreover, the platform adopts a directed graph model alongside the struc2vec algorithm to create text vectors for comparative studies, uncovering similarities between works that have won awards and those that have been nominated. Utilizing the LESS model for detailed content examination, the platform delves into sequence relationships within semantic networks, thus improving interpretability and visualization. The integration of blockchain technology guarantees access to unbiased datasets, enabling more precise literary analyses and predictions. This innovative approach has been validated using works that have either won or been nominated for the Nobel Prize, proving its efficacy in identifying the textual characteristics favored by the Nobel Prize committee.

A significant feature of smart door lock systems is the Temporary Key Function (TKF) for visitors. However, existing TKFs in smart door lock systems in use are either vulnerable to attacks or inconvenient for use. This paper thereby proposes elliptic curve cryptography (ECC)-based innovative authentication module for smart door lock systems which enables highly secure and convenient TKF. Based on the authentication module, two protocols are designed for the host and visitor respectively to unlock the door lock system. Security features for the two protocols are verified via Gong Needham Yahalom (GNY) logic. Besides, the proposed prototypes are realized, and the simulation experiments are carried out to study the performance of the protocols. The results show that our scheme is secure and efficient.

The more and more connected devices and rapidly developing Internet of Things (IoT) applications are the foundations of the future smart cities which provide ubiquitous services. The extension and proliferation of the technology brings huge security challenges, especially for the infrastructural IoT applications in the open environments. The traditional access model, such as Role-Based Access Control (RBAC) cannot provide the flexible fine-grained access control which is required due to dynamic changing users and environments. On the other hand, some other features of the IoT applications like constrained-resources devices and large-scale deployments make it very difficult to apply Attribute-Based Access Control (ABAC). Furthermore, the ABAC mechanism cannot control the way that the requester uses the services once the requester obtains the access permission. To address these issues, in this paper, we propose an access control model based on ABAC with Contexts-States-Awareness. The proposed model is implemented by using Semantic Web technologies with a sample ontology for the model and some access control policies in SWRL (Semantic Web Rule Language). We also give a logical architecture which is the extension from the reference architecture of XACML eXtensible Access Control Markup Language specification.