As social networks are integrated into the Vehicular ad hoc networks (VANETs), the emerging Vehicular social networks (VSNs) have gained massive interests. However, the security and privacy of data generated by various applications in VSNs is a great challenge, which blocks the further development of VSNs. The emerging Blockchain technology seems to be a good catalyst for the development of VSN with its high security and irreversible features, which can be also a data management tool for rapidly generated data of VSNs with tamper proof. However, the full duplicates of Blockchain data need to stored in each nodes to ensure security, which is unacceptable for vehicles with limited resource. In this paper, to address the above storage challenge, a lightweight Directed Acyclic Graph (DAG) based Blockchain (LDV) is proposed for resource-constrained VSNs. Specifically, based on the in-depth analysis of VSNs, we propose the social-based data reduction approach. In detail, each node only stores the interested data within the topic groups of interest and ignore the irrelevant data. To avoid the huge storage cost within large-scale groups with large amounts of data, we further present the historical data pruning method within a group, which reduces the storage requirement by reducing the number of duplicates stored in each node. Experimental results show that LDV can save 97.13% storage space and has good scalability.

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP, a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra-and inter-clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users' data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

To reduce latency and communication overhead of asynchronous Byzantine Fault Tolerance (BFT) consensus, an optimistic path is often added, with Ditto and BDT as state-of-the-art representatives. These protocols first attempt to run an optimistic path that is typically adapted from partially-synchronous BFT and promises good performance in good situations. If the optimistic path fails to make progress, these protocols switch to a pessimistic path after a timeout, to guarantee liveness in an asynchronous network. This design crucially relies on an accurate estimation of the network delay Δ to set the timeout parameter correctly. A wrong estimation of Δ can lead to either premature or delayed switching to the pessimistic path, hurting the protocol's efficiency in both cases. To address the above issue, we propose ParBFT, which employs a parallel optimistic path. As long as the leader of the optimistic path is non-faulty, ParBFT ensures low latency without requiring an accurate estimation of the network delay. We propose two variants of ParBFT, namely ParBFT1 and ParBFT2, with a trade-off between latency and communication. ParBFT1 simultaneously launches the two paths, achieves lower latency under a faulty leader, but has a quadratic message complexity even in good situations. ParBFT2 reduces the message complexity in good situations by delaying the pessimistic path, at the cost of a higher latency under a faulty leader. Experimental results demonstrate that ParBFT outperforms Ditto or BDT. In particular, when the network condition is bad, ParBFT can reach consensus through the optimistic path, while Ditto and BDT suffer from path switching and have to make progress using the pessimistic path.