As the application fields of permissioned blockchains broaden and the integration of related industries accelerates, there is a rising demand for permissioned blockchains to support scalable networks. This paper proposes a Partitioned, Parallel and Practicable permissioned blockchain, called as P3-Chain, which builds upon a multi-shard two-tier architecture. Its key design insight is to extend scalability in terms of consensus algorithm protocol, architecture, and scheduling. In particular, P3-Chain employs a dual-consensus algorithm with decoupled intra- and inter-shard operations, allowing them to run in parallel and asynchronously under practical scenarios. To resolve the conflicting transaction problem brought by this decoupled dual-consensus algorithm, P3-Chain incorporates a state-access locking mechanism. P3-Chain is implemented in Golang across multiple OSs, and it is evaluated on Hyperledger Caliper testbed, ensuring standardized and fair benchmarking. Through extensive experiments, the results indicate that P3-Chain can achieve TPS$3.3\times $that of FISCO,$3.3\times $that of partitioned FISCO,$2.7\times $that of Fabric,$2.3\times $that of AHL+,$2.2\times $that of SharPer and$7.4\times $that of Ethereum when system contains 32 nodes. Meanwhile, within the same experimental settings, P3-chain is scalable to 1024 nodes successfully, while Fabric and FISCO run with 64 nodes only. Furthermore, P3-Chain only sacrifice less than a 10% performance when the system scale expands$256\times $from 4 to 1024.

Permissioned blockchain systems provide a mutual-trust platform for data sharing and collaboration among organizations. However, performance bottlenecks limit their adoption in industrial applications requiring high transaction throughput and low latency. Recent advancements have focused on leveraging parallelism to improve performance, but transaction contention remains a significant challenge. The Optimistic Concurrency Control (OCC) mechanism, once widely used to manage transaction contention in permissioned blockchains, is valued for its simplicity and minimal design constraints. However, its reliance on the strategy of aborting conflicting transactions results in resource wastage and suboptimal performance, rendering it less favorable in recent research. This paper presents the Overpass Ledger (OPL), a high-performance permissioned blockchain system that utilizes an overpass-inspired workflow. To address transaction contention in such a highly parallelized workflow, the OCC mechanism is revisited and Re-Execution (ReX) is proposed, an enhanced OCC variant that efficiently re-executes conflicting transactions to eliminate transaction abortion and maximize resource utilization. By integrating ReX, OPL fully harnesses the advantages of parallel stage processing and concurrent transaction execution. Experimental results demonstrate that OPL achieves throughput improvements of$77\times$,$19\times$, and$4\times$compared to Hyperledger Fabric, BIDL, and FISCO BCOS, respectively, while maintaining consistently low latency.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

Due to the distributed characteristics of federated learning (FL), the vulnerability of the global model and the coordination of devices are the main obstacle. As a promising solution of decentralization, scalability, and security, leveraging the blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain-like proof of work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this article introduces a framework for empowering FL using direct acyclic graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in detail, and then, two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of the DAG-FL consensus mechanism. After that, a Poisson process model is formulated to discuss that how to set deployment parameters to maintain DAG-FL stably in different FL tasks. The extensive simulations and experiments show that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical existing on-device FL systems as the benchmarks.

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. The extensive simulations show that DAG-FL can achieve the better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.