The proliferation of mobile internet and sharing economy has catalyzed the emergence of Ride-Sharing Services (RSSs) as a paradigm of spatial crowdsourcing in intelligent transportation. Compared with ride-hailing, RSSs present heightened challenges in security and service quality management due to bidirectional disclosure of trip plans and complex matching logic. Existing secure RSS solutions predominantly operate under semi-honest threat models or suffer from prohibitive computational complexity in service composition. However, ensuring public verifiability of matching outcomes is equally critical to prevent manipulation and ensure accountability in decentralized environments. Moreover, achieving a harmonious trade-off among privacy preservation, public verifiability, and matching efficiency remains an open challenge in RSS systems. This work proposes TBRS, a novelTEE-Blockchain powered privacy-awareRide-Sharing framework, which innovatively addresses three core challenges in service computing: (1) formalizing aninclusive matching modelthat extends traditional identical matching through trajectory region overlap analysis and direction alignment verification; (2) designing anIndex-Preserving Bloom Filter (IP-BF)coupled with Hilbert R-tree spatial indexing, achieving$O(\log n)$matching complexity through computational geometry optimization; (3) implementing a hybrid trusted execution environment via SGX-enhanced consortium blockchain withprivate smart contracts, ensuring verifiable service operations management under malicious threats. The framework demonstrates significant advancements in service performance management through systematic experiments: 2${\times }$$\sim$11${\times }$acceleration in on-chain service composition, 6×$\sim$33× improvement in off-chain computation efficiency, while maintaining over 99% service matching accuracy. These results signify that TBRS effectively breaks the efficiency bottleneck of existing privacy-preserving RSS solutions, making decentralized ride-sharing practical for deployment.

With the widespread adoption of electric vehicles (EVs), Vehicle-to-Vehicle (V2V) charging technology offers a more flexible solution for EV charging, significantly alleviating charging inconvenience, particularly in remote areas. However, the current V2V energy trading landscape lacks reliable management platforms with transparent transaction protocols. Furthermore, notable deficiencies exist in data privacy and security, which hinder the broader implementation of V2V charging services. Addressing these challenges, particularly the provision of secure and publicly auditable V2V charging without relying on centralized platforms or disclosing user privacy, has become a critical concern. For this reason, this paper presents PET, a decentralized Privacy-preserving Energy Trading system with public verifiability for EVs. PET is built upon the emerging blockchain technology to decentralize energy trading while ensuring accountability. We model energy demand-supply matching as range matching of locations and charge amounts between a buyer and sellers, and employ reverse auction to select the winner. For efficient and privacy-aware range proofs, we propose a novel batched hash chain-based range proof (BHW) primitive. In addition, PET integrates zk-SNARKs to verify payment correctness while maintaining user privacy. Our system supports public verifiability, entitling any third party to independently verify the transaction integrity. We analyze the privacy guarantees and public verifiability of PET. Extensive experiments implemented on Hyperledger Fabric further validate that PET delivers robust performance with a 4 × reduction in verification cost compared to that without batch proofs.

With the rapid development of digital currencies, their anonymity has provided shelter for criminals, leading to an increasing number of abnormal transactions that threaten financial order. In response, researchers have proposed centralized methods and federated learning to detect abnormal transactions. However, existing methods suffer from insufficient feature fusion, leading to information loss. In addition, the averaging of client parameters in federated learning negatively affects transaction detection effectiveness. To tackle these issues, we propose a Federated learning-based Multi-angle feature fusion framework for abnormal Transactions Detection, named FMTD. First, an Adaptive Multi-angle Contrastive Learning model (AMCL) is presented as the local model for clients, which improves feature fusion by maximizing positive sample consistency, minimizing negative sample consistency, and assigning higher weights to more effective features. Second, a Federated adaptive algorithm with Adjustable Client Weights (Fed-ACW) is introduced, which reduces the negative impact of poor local models on the global model. While preserving transaction feature privacy, FMTD enhances the transaction detection rate. Experimental results demonstrate its superiority over existing methods.

The integration of electric vehicles (EVs) into the transportation network has positioned their battery packs not only as power sources for mobility but also as crucial components of energy storage. This dual role enables EVs to trade energy with the grid and among themselves, introducing an innovative approach to energy transactions. Their ability to mitigate peak energy demand and engage in vehicle-to-vehicle (V2V) exchanges represents a significant step toward a more interactive and adaptable energy infrastructure. This paper introduces a blockchain-based V2V energy trading system that utilizes EVs as mobile energy storage units to effectively promote the trustworthiness of energy trading in an environment of mutual distrust through decentralized protocols. The system includes a V2V energy trading protocol that facilitates secure energy transactions, guarantees data integrity, and builds trust among participants. Additionally, it employs a new group fairness-based trade matching algorithm for sustainable energy trading services, encouraging greater participation from EV owners and fostering a more responsive energy market. To support these decentralized and trusted V2V trading services, an efficient and scalable blockchain prototype is proposed. This prototype features a novel sharding consensus scheme, which has been implemented and deployed for real-world experiments and performance evaluation. This system contributes to the green energy economy by enabling a transparent and trust-based approach to energy trading, offering a transformative model for sustainable energy commerce.

Blockchain technology finds widespread application in the management of Internet of Things (IoT) devices. In response to the challenges posed by performance scalability and the convergence of multiple ledgers stemming from an expanding network, this study introduces the concept of Co-Sharding . Within this framework, the ledger maintained by sub-chains overseeing IoT operations in distinct geographic regions is conceptualized as a shard within the Large-scale Internet of Things (LIoT) ledger. Meanwhile, elected nodes within each region assume responsibility for maintaining a coordinating shard, facilitating cross-regional communication and data interaction. Furthermore, our work presents a multi-objective optimization algorithm grounded in the multi-shard paradigm to enact a scheduling strategy that spans various regions. We undertake a series of pertinent experiments and conduct a comparative analysis of scheduling algorithms within the context of a real-world cross-regional agricultural IoT system, utilizing actual operational data. The comparative results demonstrate that, in comparison to intra-sub-region scheduling, the Co-Sharding approach enhances machine utilization rates by approximately 30% and reduces scheduling time by around 18% when confronted with a task count of 12. In terms of performance, Co-Sharding also exhibits the capability to reduce the storage requirements of lightweight nodes within each region by approximately 39% while concurrently improving throughput by approximately 1.5 times when contrasted with a single-chain architecture.

Byzantine fault-tolerant (BFT) state machine replication (SMR) is adopted to support blockchain consensus by tolerating arbitrarily faulty behaviours. However, the inherent complexity of BFT protocols makes existing BFT protocols hard to adapt to large-scale applications that require high scalability and performance. In this paper, we propose a BFT parallelism protocol designed to enhance its scalability by using a hierarchical multi-committee architecture. It also encompasses a cross-layer consensus operation flow to improve safety and support trusted execution environments (TEEs). Our proposed approach allows the lower bound on the number of peers to be reduced to$2f+1$. We show the value of our proposed protocol in comparison to other state-of-the-art BFT protocols through experiments and performance evaluations on a testbed built on a cloud platform. The proposed protocol demonstrates a remarkable level of scalability, capable of accommodating a growing number of peers. Additionally, it exhibits improved performance when contrasted with HotStuff and FastBFT, with approximately 100% and 200% enhancements, respectively.

This article introduces a novel scalable multishard Byzantine fault tolerance (SharBFT) consensus protocol combined with a blockchain sharding optimization scheme.SharBFT builds upon the classic BFT state-machine replication approach and extends it into a hierarchical multishard prototype to enable scalable and concurrent Byzantine consensus. This prototype enhances scalability and bolsters the security of global consistency in comparison to existing protocols. Moreover,SharBFT employs a novel consensus voting mechanism based on the threshold signature scheme, resulting in linear message communication complexity and optimized consensus operations. In additional,SharBFT integrates a sharding optimization model (SOM) to enhance consensus efficiency in dynamic system environments. The proposed SOM aims to minimize the average consensus latency while ensuring security and scalability. This article presents experimental results conducted in a real-world cloud environment, illustrating significantly improved performance.

The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements.

Byzantine fault-tolerance (BFT) consensus is a fundamental building block of distributed systems such as blockchains. However, implementations based on classic PBFT and most linear PBFT-variants still suffer from message communication complexity, restricting the scalability and performance of BFT algorithms when serving large-scale systems with growing numbers of peers. To tackle the scalability and performance challenges, we proposeParBFT, a new Byzantine consensus parallelism scheme combining classic BFT protocols and a novel Bilevel Mixed-Integer Linear Programming (BL-MILP)-based optimisation model. The core aim of ParBFT is to improve scalability via parallel consensus while providing enhanced safety (i.e. ensuring consistent total order across all correct replicas). Another core novelty is the integration of the BL-MILP model into ParBFT. The BL-MILP allows us to compute optimal numerical decisions for parallel committees (i.e. the optimal number of committees and peer allocation for each committee) and improve consensus performance while ensuring security. Finally, we test the performance of the proposed ParBFT on Microsoft Azure Cloud systems with 20 to 300 peers and find that ParBFT can achieve significant improvement compared to the state-of-the-art protocols.

The growing use of Android software has made mobile devices the main platform for information services such as mobile social media and financial services. Mobile software provides great convenience but also brings challenges to the software community. For example, mobile malware, a malicious software specifically designed to target mobile devices, creates security concerns for the business network and the data stored on it. Therefore, it is becoming more and more important to effectively identify and classify malware. Most of the current malware-classification methods rely on the specific (static/dynamic) behaviour information from Android software for improved malware-detection capability. Nevertheless, these methods cannot detect new types of fraud software due to the limited generalisability. To address these issues, this paper proposes the AMC-FN, i.e. an Android-based malware classification method using feature fusion and natural language processing technologies. The proposed AMC-FN aims to improve the dimension and performance of classification and also some specific functions of natural language processing, i.e. mutual information method, n-gram word segmentation and feature mapping. The AMC-FN framework improves the classification dimensions by leveraging the information from Android APK permission, API calls and realistic network traffic. Moreover, the framework also contains a novel multi-level feature fusion algorithm (MFFA) designed to improve the weighted feature fusion. To obtain better fine granularity and generalisability, the fusion features are used by the optimized SVM (Support Vector Machine) classifier for training. Our experimental measurements and comparisons show the improved performance based on the proposed AMC-FN framework.

Commercialized 5G technology will provide reliable and efficient connectivity of motor vehicles that could support the dissemination of information under an intelligent transportation system. However, such service still suffers from risks or threats due to malicious content producers. The traditional public key infrastructure (PKI) cannot restrain such untrusted but legitimate publishers. Therefore, a trust-based service management mechanism is required to secure information dissemination. The issue of how to achieve a trust management model becomes a key problem in the situation. This paper proposes a novel prototype of the decentralized trust management system (DTMS) based on blockchain technologies. Compared with the conventional and centralized trust management system, DTMS adopts a decentralized consensus-based trust evaluation model and a blockchain-based trust storage system, which provide a transparent evaluation procedure and irreversible storage of trust credits. Moreover, the proposed trust model improves blockchain efficiency by only allowing trusted nodes participating in the validation and consensus process. Additionally, the designed system creatively applies a trusted execution environment (TEE) to secure the trust evaluation process together with an incentive model that is used to stimulate more participation and penalize malicious behaviours. Finally, to evaluate our new design prototype, both numerical analysis and practical experiments are implemented for performance evaluation.

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.

New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model.

Infrastructure as a Service (IaaS) is a service mode of cloud, which provides Virtual Machines (VMs) to customers. With the increasingly fierce competition in market, cloud providers pay more attention to Quality of service (Qos). As the practical measurement costs a large amount of resource, deriving performance metrics from analytical model is a good choice. Recently, Stochastic Petri Net (SPN) has been used to construct analytical models, but they lack the ability to exhibit hierarchies of IaaS cloud. Compare to SPN, Performance Evaluation Process Algebra (PEPA) has the feature of compositionality, which imports the hierarchial strategy into the semantics. So, PEPA models of IaaS cloud are closed to actual architecture and can be more easily accepted by engineers. This paper provides a PEPA model of IaaS cloud and gives some numerical results.

As product of the combination of Internet technology and sensor network, Internet of things (IoT) greatly facilitates the mutual interaction of the information world and physical world. Furthermore, with the rapid development of modern transportation systems, as a typical successful application of IoT in the real life, intelligent traffic system (ITS) has a great significance on management of the traffic information and transport infrastructure. Therefore, in order to achieve the efficient use of transportation resources, it is particularly important to evaluate and analyze the performance of ITS. In this paper, an advanced high level formalization — Performance Evaluation Process algebra (PEPA), a kind of stochastic process algebra (SPA), is adopted to model the process of real-time traffic status query in ITS. Meanwhile, by using fluid approximation approach to analyze performance of the model and then the performance parameters of the system in practical application can be achieved accurately.