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.

Sharding has become a significant approach to enhance blockchain scalability. However, existing sharding techniques applied in IoT scenarios suffer from transaction congestion due to imbalanced distribution of transactions across shards, which hinders intra-shard transaction processing capacity. To overcome the above problems, this paper proposes HydraChain for IoT scenarios, the first multi-agent reinforcement learning based sharding blockchain system with account graph relationships, for a throughput improvement of shards under realtime load balancing. Agents collaborate by sharing information and jointly optimizing decisions, enhancing the accuracy and efficiency of the decision-making process. We first construct a sharding blockchain environment integrated with an embedded graph encoder. Concurrently, we propose a SG-MAPPO multiagent model with decoder, which enables agents to cooperatively learn to optimize account allocation strategies based on real-time shard load and global system information. When implementing HydraChain, we address two technical challenges: (i) to extract granular behavioral features from accounts with diverse and time-varying patterns, we design a graph data encoder, which constructs a graph network based on transactional relationship; and (ii) to ensure real-time load balancing under the constraints of dynamic transaction patterns, we propose a multi-agent model (SG-MAPPO), which matches graph encoding features within the environment. Our approach leverages the ability of multi-agent model to collaborate and adapt to the changing environment, enabling efficient resource allocation and improved system performance. Moreover, we implement HydraChain and conduct experiments on a high-performance server equipped with 48 cores and 125GB of memory. Our comprehensive experiments, comparing HydraChain with DQN-Based, SAC-Based and SPRING, reveal that our solution outperforms state-of-theart solutions by achieving a notable 22% increase in transaction throughput and a 5.2% reduction in workload imbalance across shards.

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.

Asynchronous Complete Secret Sharing (ACSS) in Byzantine fault-tolerant systems has become one of the essential building blocks in multiple threshold cryptosystems. However, current ACSS schemes scale poorly due to high communication costs, which are quadratic in the number of participants n. In this paper, we propose a new scheme ALCES to reduce such communication costs from O(n2) to O(cn) with a negligible probability of failure ${e^{ - \frac{c}{{18}}}}$, while guaranteeing completeness and agreement properties. The key point of ALCES is to sample c parties to construct a committee, which then verifies and distributes the encrypted shares to other parties. Additionally, we introduce a new mechanism, referred to as secret labels in ALCES, by encoding the information of labels in polynomial coefficients. This mechanism allows an arbitrary string to act as the label, binding it to a specific secret while efficiently ensuring security and privacy with minimal communication cost. Experimental results show that our technique reduces the overall communication cost in a single sharing process by 66% and 83% for very large quantities, such as 4096 and 8192 parties, respectively, when compared with prior work.

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.