Blockchain-based Federated Learning (BCFL) is widely recognized as a promising solution for collaboratively training machine learning models while maintaining system security. Since blockchain systems are transaction-driven, the efficiency of transaction processing is directly related to the performance and availability of the BCFL system. Previous research has primarily focused on optimizing storage mechanisms or integrating Trusted Execution Environment (TEE) to reduce transaction processing pressure. However, the performance of BCFL remains constrained by slow transaction processing. This critical bottleneck arises from scalar instruction operations in transaction execution engines and the inherent serial transaction processing mechanism. In this paper, we propose a novel hybrid-granularity parallelism architecture, HGP, to greatly accelerate transaction processing in BCFL systems. HGP achieves this through three major innovations: (1) a suite of extended vector instructions, which reduces the instruction number and execution latency by enabling vectorized data I/O and computation using very long instruction word (VLIW) techniques, (2) the scalable transaction grouping method that generates parallelizable transaction groups through transaction signature verification and read-write conflict detection, and (3) the multi-EVM (Ethereum Virtual Machine) parallel processing mechanism that processes a group of transactions using multiple execution engine threads, and maintains global consistency through group scheduling. Through these optimizations, HGP accelerates the transaction processing with both data-level and thread-level parallelism. We evaluate HGP by executing BCFL tasks over classic ResNet18, MobileNet, and SqueezeNet. The experimental results demonstrate that HGP achieves up to a$3.8 \times$improvement in CPU utilization and a$1.6 \times$improvement in memory utilization. Furthermore, HGP significantly speeds up the transaction processing performance of three critical tasks by up to$24.5 \times, 12.4 \times$, and$2.8 \times$, respectively.

Self-healing group key distribution (SGKD) protocols guarantee the security of group communications by allowing authorized users to independently recover missed previous session keys from the current broadcast without retransmission. However, existing SGKD protocols have flaws: (1) collusion resistance and revocable nodes are both upper-bounded by the degree of polynomials used, (2) the disclosure of personal secrets enables the recovery of group key, (3) temporary revocation of a group member is not possible, and (4) a revoked node may obtain the session key when initiating mutual healing, moreover, a malicious node may cause the recovery of false group keys. To address these limitations, we propose an SGKD protocol using the Chinese remainder theorem (CRT) and Physical Unclonable Function (PUF). Our proposed SGKD protocol generates a PUF-based dynamic secret by stimulating nodes’ PUF using a polynomial-based encrypted challenge. This secret is then employed to retrieve a CRT-based encrypted group key. By combining PUF and CRT, we can generate dynamic secrets on the fly and reduce computation time significantly. Utilizing such a technique, our protocol achieves superior security goals, including resistance to any coalition of group nodes even if nodes’ personal secrets were disclosed. Furthermore, the proposed protocol provides an unlimited number of revocable nodes. Additionally, a revoked node can rejoin its group in later sessions without affecting backward secrecy. Moreover, the protocol provides a backward secrecy guaranteed mutual-healing feature free from desynchronization. Our performance and security analyses (i.e., theorem-based formal analysis, NS3-based experiment, and formal verification using the AVIPSA tool) show that our proposed protocol achieves stronger security goals and better efficiency in terms of computation, communication, and storage costs compared to existing SGKD schemes.

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions.

This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between thehigh-tierand low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.