Byzantine fault-tolerant (BFT) consensus protocols are the core components of blockchain. In the process of improving the performance of BFT protocols, existing work faces the following three problems: 1) the binary dilemma between the leader’s performance bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; 2) two- or three-round protocols restrict the phase number of one proposal, thereby limiting the number of concurrent proposals and causing high latency. 3) The fixed timeout makes the protocol sensitive to varying network delays. Therefore, this paper proposesCrackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of$(\mathcal {N}\textrm {-} 1)/3$. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into$2\mathsf {\kappa }$phases to increase the basic pipeline scale. We refine the timer strategy so that the timeout$\Delta $is adjusted with the proposal submission to cope with the changing network environment. We then address two technical challenges: 1) to ensure Quorum Certificate (QC) validation, we design a$\mathit {voteMap} s$field within each block, and verify QC by the signature aggregation of$\mathit {voteMap} s$in continuous$\mathsf {\kappa }$phases; and 2) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that relaxes the conditions for the leader to send new proposals. We provide comprehensive theoretical proof of the correctness ofCrackle, including safety and$\mathit{liveness}$. Moreover, we implementCracklebased on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that ourCrackleprotocol achieves up to 10.36x higher throughput and can dynamically adapt to network delay compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

To enable parallel transaction processing in blockchain systems, recent consensus protocols have adopted directed acyclic graph (DAG) structures where DAG is used to organize and parallelize the blocks. Unfortunately, these protocols suffer from high communication overhead. Our experiment on the state-of-the-art Graded DAG[12] reveals that dissemination of transaction and consensus vote messages account for the majority of network traffic. We analyze that the overall overhead is O (N2) per replica and O (N3) for the entire system, where N is the number of replicas, and note that existing approaches have not succeeded in reducing this overhead.

The application scenarios of drone collaboration are rapidly expanding, such as the low-altitude economy and wildfire protection. Blockchain-based drone collaboration requires a consensus mechanism to ensure efficient and secure consistency among large-scale distributed nodes. However, the existing consensus mechanism has problems with poor fault tolerance of topology and rigid proposal concurrency. To this end, this paper proposes AirBFT, an efficient and robust consensus mechanism for large-scale drone collaboration. First, this paper designs a new four-layer network topology, using upper-member and lower-member communication, while ensuring the maximum 1/3 resilience and fanout of √N. Secondly, this paper proposes a dynamic pipelining algorithm to adjust the parallelism of proposals according to the real-time network status. Finally, this paper proposes a committee sampling technology based on the EigenTrust algorithm to reduce the impact of the malicious behavior of Byzantine nodes. Experiments based on the public consensus framework show that compared with Kauri and HotStuff, the proposed AirBFT reduces transaction confirmation delay by 58%, the throughput is increased by 1.9 times, and it can ensure efficient operation with a 1/3 Byzantine node ratio.

Blockchain offers distinct advantages in terms of data credibility and provenance certification, and its fusion with Internet of Things (IoT) technology holds great promise. Nevertheless, IoT environments are marked by extensive node networks and intricate communication patterns, especially the sensing environment. The conventional blockchain consensus mechanism, hampered by its heavy reliance on computing resources and communication bandwidth, faces difficulties in ensuring seamless data exchange among IoT edge servers. The issues encountered by state-of-the-art Byzantine Fault Tolerance (BFT) consensus include: (i) high communication complexity between nodes; and (ii) the detrimental impact of Byzantine behavior on system performance. To overcome the above problems, we propose the lightweight blockchain consensus called AntB, firstly introducing the concept of sampling into the consensus and significantly reducing the number of participating consensus nodes from$N$to$n$, which lowers the consensus complexity to$\mathbf{2\cdot O(n)+O(N)}$. We design a dynamic reputation mechanism so that Byzantine nodes cannot control the sampling set to affect the activity of the consensus in the long term. When implementing AntB, we address three significant technical challenges: (i) to determine the optimal sample size, we propose a sampling calculation method based on statistical confidence intervals, where the sample size is primarily determined by the chosen confidence level and margin of error; (ii) to prevent Byzantine behavior, we devise a weighted random sampling mechanism utilizing reputation coefficients based on edge servers’ behaviors; and (iii) to maintain consensus activity and consistency after sampling, we propose the consensus mechanism for partial sampling and global verification to avert potential issues. We implement AntB and conduct performance evaluations in a server with 32 cores and 64GB of memory. The evaluation results indicate that, the more nodes participating in the process of consensus, the better the performance of AntB will be. Especially, compared to HotStuff, AntB has a 24.94% higher success rate and Transactions Per Second (TPS) can improve by 102.10% when the number of nodes is 300.

With the rapid expansion of blockchain data, the demand for data exchange between chains has grown significantly. Authenticated queries have become one of the crucial methods for retrieving on-chain data due to their efficient performance and ability to ensure data security. However, existing intra-chain query approaches either face substantial maintenance overhead or exhibit low query efficiency, when dealing with the explosive growth of data in cross-chain scenarios; while current cross-chain query approaches suffer from issues including limited query types and poor scalability. To this end, this paper takes the lead to propose a novel generalized framework named CubeChain which provides various query types for intra- and cross-chain authenticated queries. We propose a highly scalable authenticated data structure (ADS) named Cube as the core structure of our framework which excels in achieving high performance while minimizing maintenance overhead by establishing data bridges between vertexes. When implementing CubeChain, we address two challenges: (i) implementing lightweight verification while supporting various query types by using a two-layer hashing structure, and (ii) further improving the query efficiency by suppressing vertexes. We substantiate the superior performance of Cube in terms of query efficiency, update overhead, and scalability through theoretical analysis. Finally, we implement the CubeChain framework based on the open-source Fabric v2.2. Real experiments with YCSB benchmark demonstrate that, compared with the state-of-the-art Bs+tree-based ADSs in MSTDB and SEBDB, our query performance improved by 23.75x in intra-chain scenarios and 10.72x in cross-chain scenarios, while maintaining a 30% reduction of cross-chain query load.

Blockchain systems widely employ Byzantine fault-tolerant (BFT) protocols to ensure consistency. Improving BFT protocols’ throughput is crucial for large-scale blockchain systems. Frontier protocols face crucial problems: (i) the binary dilemma between leader bottleneck in star-based linear communication and compromised resilience in tree-based sublinear communication; and (ii) 2- or 3-round protocols restrict the phase number of one proposal, thereby limiting the scalability and parallelism of the pipeline. To overcome the above problems, this paper proposes Crackle, the first sector-based pipelined BFT protocol with a sublinear communication complexity, for a throughput improvement of consensus protocol with max resilience of (N-1)/3. We propose a sector-based communication mode to disseminate messages from the leader to a subset of replicas in each phase to accelerate consensus and split the traditional two-round protocol into 2κ phases to increase the basic pipeline scale. When implementing Crackle, we address two technical challenges: (i) to ensure Quorum Certificate (QC) validation during continuous κ phases, we design a voteMap field within each block, and verify QC by the aggregation of continuous κ voteMaps; and (ii) to achieve pipeline decoupling among shorter phases, we propose a vote-appending mechanism that accelerates the leader’s transition to the next phase. We provide comprehensive theoretical proof of the correctness of Crackle, including safety and liveness. Moreover, we implement Crackle based on a public BFT framework and deploy it on 64 cloud servers. Real experimental results reveal that Crackle achieves up to 10.36x higher throughput compared with state-of-the-art BFT protocols such as Kauri and Hotstuff.

Multi-dimensional performance evaluation is crucial for blockchain systems as it enables appropriate blockchain choosing for a given scenario and helps to pinpoint the bottleneck module of a blockchain system to optimize its performance. However, the existing evaluation frameworks for blockchain suffer from low system generality, inefficient workload execution, and incomprehensible evaluation metrics. In order to overcome their limitations, we design and implement the Generalized and Fine-grained Blockchain Evaluation (GFBE) framework. Specifically, we abstract 3 types of Universal Evaluation Interface (UEI) via the dynamic proxying approach to enable generalized evaluation of heterogeneous blockchain systems. Through the design of Lua-based workloads plugin with high flexibility and reusability, GFBE improves the efficiency of workload execution. To achieve comprehensive measurement, we define 15 key performance metrics across hierarchical layers of blockchain architecture. We also implement and deploy GFBE on 16 machines each with 8 CPUs and 16GB RAM, and evaluate three open-source blockchain systems namely Ethereum, ChainMaker, and Haihe smart chain. The experimental results demonstrate that GFBE efficiently and accurately measure 15 key performance metrics such as Contract Execution Efficiency at the contract layer, Consensus Agreement Time Ratio at the consensus layer, and State Query Time at the data layer. Compared with state-of-the-art frameworks such as BLOCKBENCH, Log-based, and Caliper, GFBE distinguishes itself as the only framework that encompasses the appealing features of universal interface, reusable workload, and all-layer metrics.