A global trust-based blockchain lightweight consensus mechanism

As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicles and reduce the probability of malicious nodes participating in the consensus, we propose to calculate the reputation of RSU nodes based on multi-weighted subjective logic (CRMWSL) model. Secondly, in order to ensure the efficiency of blockchain data consensus, we improve the consensus protocol of traditional double-layer PBFT, change the election method of the committee and the PBFT consensus process, and improve throughput by reducing the number of consensus nodes. For the committee, we combine the credibility value and hash method to ensure the credibility of nodes, but also to ensure a certain degree of election randomness. For the PBFT consensus process, the regional committee consensus is carried out first, and then the regional master node carries out the global consensus. Through experimental comparison, we show that the C-PBFT significantly reduces consensus time, network overhead, and is scalable for Internet of Vehicles.

The combination of blockchain technology with Industrial Internet of Things (IIoT) frameworks is promising in terms of building trust, data authenticity, and resilience. However, the efficiency and feasibility of integration largely rely upon the consensus mechanisms used. The present study is an overview of four renowned blockchain consensus schemes, namely Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), and the corresponding performance, security, efficiency, as well as compatibility under IIoT. The results reveal that low‐latency and lightweight consensus, such as PBFT and DPoS, will be helpful in IIoT applications, especially in applications with scarce resources. The paper offers practical guidance on the development of IIoT systems with integrated blockchain customized based on the requirements of the industry.

Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.

In emergency scenarios where the on-site information is completely lacking or the original environmental state has been completely changed, autonomous and mobile swarm robotics are used to quickly build a rescue support system to ensure the safety of follow-up rescuers and improve rescue efficiency. To address the data security problem caused by the complex and changeable topology of the heterogeneous swarm robotics network in the process of building the rescue support system, this paper introduced a decentralized data security communication scheme for heterogeneous swarm robotics. First, we built a decentralized network topology model by using base robot, communication robotics, and business robotics, and it can ensure the stability of the system. Moreover, based on the decentralized network topology model, we designed a storage model using the master–slave blockchain method. The master chain is composed of base robot and communication robotics, which mainly store the digests of robot data in multiple slave chains to reach the global data consensus of the system. The slave chains are composed of business robotics and communication robotics, which mainly store all data on the slave chains to reach the local data consensus of the system. The whole data storage system adopts the Delegated Proof of Stake consensus mechanism to elect proxy nodes to participate in the data consensus tasks in the system and to ensure the data consistency of each robot node in the decentralized network. Additionally, a prototype of the heterogeneous swarm robotics system based on the master–slave chains is constructed to verify the effectiveness of the proposed model. The experimental results show that the scheme effectively solves the data security problem caused by the unstable communication link of the heterogeneous swarm robotics system.

As the main food source of the world’s population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

In recent years, Blockchain is gaining prominence as a hot topic in academic research. However, the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance. Although Proof-of-Authority (PoA) consensus mechanism, as a lightweight consensus mechanism, is more efficient than traditional Proof-of-Work (PoW) and Proof-of-Stake (PoS), it suffers from the problem of centralization. To this end, on account of analyzing the shortcomings of existing consensus mechanisms, this paper proposes a dynamic reputation-based consensus mechanism for blockchain. This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node, which can monitor the behavior of validators in case that validators with excessive power cause harm to the blockchain network. At the same time, the reputation evaluation algorithm is also introduced to select nodes with high reputation to become validators in the network, thus increasing the cost of malicious behavior. In each consensus cycle, validators and monitoring nodes are dynamically updated according to the reputation value. Through security analysis, it is demonstrated that the scheme can resist the attacks of malicious nodes in the blockchain network. By simulation experiments and analysis of the scheme, the result verifies that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the time of consensus to guarantee the security of the system.

- The explosion of Internet of Things (IoT) devices calls for the design of computationally light blockchain consensus mechanisms immune to quantum threats. The conventional consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) may have quantum cryptanalysis and incur high computational overhead on resource-limited IoT devices. In this paper, we introduce QR-LightChain, a new quantum-robust light weight consensus algorithm with the combination of lattice-based cryptography and a brand-new Proof-of-Lightweight-Work (PoLW). Our proposal is based on formalism Learning With Errors (LWE) as a quantum resistant based scheme, also, but with the use of the adaptive difficulty tuning and energy efficient mechanism to validate the hashing. Experimental results show that QR-LightChain reduces the computational overhead by 52.3% with respect to traditional quantum-resistant approaches, while preserving security against both classical and quantum adversaries. The protocol shows good performance in IoT: The average block validation time of 1.2 sec is achieved and there is 40% less energy consumed than for current quantum-resistant consensus in the literature. Our work fills the important research challenge of providing 1 Post-Quantum Cryptography and Blockchain Modern internet of things (IoT) blockchain net- works are being developed in resource-constrained environments such as smart cities, while QCs Key Words: Quantum resistance, IoT blockchain, lightweight consensus, lattice-based cryptography, post-quantum cryptography, Proof-of-Lightweight-Work, resource-constrained devices

In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageable groups of nodes that collaborate to achieve consensus without the overhead associated with traditional large-scale consensus protocols. CohortSync operates by first classifying nodes based on their network latency, data relevance, and operational load. This classification enables the protocol to intelligently form micro-cohorts that are geographically and contextually optimized, reducing the latency typically experienced in global consensus operations. Each micro-cohort is responsible for a subset of the reconciliation tasks, allowing for parallel processing and significantly reducing the time to reach consensus. The protocol incorporates a hybrid approach to consensus that combines elements of both deterministic and probabilistic consensus mechanisms. This hybrid model allows CohortSync to maintain high availability and consistency, even in the face of node failures or network partitions. By adapting the consensus mechanism based on real-time network performance and node responsiveness, CohortSync can dynamically adjust its operations to maintain system performance and data accuracy. Another key feature of CohortSync is its reconciliation process, which uses a version-controlled state reconciliation algorithm. This algorithm ensures that all nodes within a micro-cohort maintain a synchronized state, with conflicts resolved through a majority rule among the cohort members. This approach not only minimizes the risk of data divergence but also optimizes the reconciliation process to be both time-efficient and resource-conservative. CohortSync also integrates a continuous learning component that analyzes past consensus rounds to optimize future cohort formation and consensus strategies. This machine learning-driven adaptability makes the protocol robust against evolving network conditions and varying operational loads across nodes. The protocol has been tested in various simulated environments that mimic real-world distributed systems across different industries, including finance, healthcare, and e-commerce. The results demonstrate that CohortSync significantly outperforms existing consensus protocols in terms of scalability, fault tolerance, and operational efficiency. In conclusion, CohortSync presents a transformative approach to consensus and reconciliation in distributed systems. By decentralizing the consensus process into manageable micro-cohorts and integrating adaptive learning mechanisms, CohortSync offers a scalable, efficient, and robust solution that can meet the demands of contemporary distributed computing environments.

The consortium chain is the main form of application of blockchain technology in the actual industry, and its consensus mechanism mostly adopts the practical Byzantine fault tolerance (PBFT) algorithm. The traditional PBFT algorithm is only suitable for small-scale local area networks, but in large-scale wide-area network environments, its scalability bottleneck has a serious impact on the performance of the system. Therefore, in this paper, a scalable Byzantine fault tolerance algorithm based on a tree topology network is proposed (STBFT), which can take different steps to reach consensus according to the abnormal situation of the system. First, the STBFT algorithm divides the consensus nodes into different layers and groups based on the tree topology network structure, which transforms from global consensus to local consensus and drastically reduces communication consumption. Then, the division method of the group is based on a verifiable random function (VRF), with the purpose of preventing targeted attacks and colluding Byzantine nodes from affecting the normal consensus of the system. Finally, a feedback mechanism is proposed for the first time to reduce the influence of Byzantine failure on hierarchical network systems. The simulation results show that the proposed algorithm reduces the communication complexity and improves the fault tolerance of the system, and the scalability of the tree topology network structure can be better applied in large-scale scenarios such as IoT and health care.

Blockchain and Internet of Things (IoT) are two trending technologies, when combined together can strengthen the security of various applications. However, security of blockchain depend on its consensus mechanism. The consensus mechanisms used by cryptocurrencies requires high computations and hence cannot be applied to IoT. Applying light weight consensus such as PBFT (Practical Byzantine Fault Tolerance) requires an authority or a protocol to select leader nodes and the nodes to be involved in consensus. However the current node selection process in many blockchain applications involves a central authority or is based on traditional round robin techniques. Hence we propose simple and efficient node selection mechanism that can perform consensus without wasting energy. Our approach uses PBFT, where the nodes to participate in the consensus are not predetermined by a central authority. Nodes are selected based on their performance in the blockchain.

Private blockchains tend to apply deterministic con-sensus mechanisms as a more efficient alternative to the proof-based consensus. Deterministic mechanisms tolerate two types of failures, byzantine and crash-fault. Byzantine fault-tolerant consensus assumes restrictive assumptions of time and number of failures to guarantee validity, while the termination depends on message broadcasting among nodes. Crash-Fault tolerant consensus is less rigorous to ensure termination and higher throughput while sacrificing agreement. This paper proposes a lightweight consensus mechanism based on vicinity voting with confirmed message broadcasting. Formation rules in the neighborhoods of the peer-to-peer network relax the trade-off between agreement and termination. Experimental results show that the proposal guarantees agreement and termination in case of more permissive formation rules. Besides, the cost of achieving consensus is reduced by up to 46% in more rigorous formation rules with limited impact on termination and agreement.

Proof of Random Trust Consensus Mechanism for Power Resource Sharing System

Autoencoder based Consensus Mechanism for Blockchain-enabled Industrial Internet of Things

Blockchain technology has been applied to various applications (e.g., smart buildings and smart cities) that typically run in an environment of smart devices, known as Internet-of-Things (IoT). To support these applications, different blockchain architectures, data structures and consensus algorithms have been proposed, tailored to IoT. One such proposal, appendable-block blockchain, is a promising blockchain framework for use in IoT environments. It provides a scalable data structure that allows parallel insertions between independent nodes. However, it has some limitations, in particular related to the possible eclipse attack by malicious gateways and the lack of consensus for transactions insertion. To solve these issues, we propose a new consensus mechanism for appendable-block blockchains, called context-based consensus. Using context-based consensus, information can be inserted in parallel across devices (called context) while ensuring that light-weight consensus is performed to guarantee that a transaction is well-formed and it is placed in the correct order. We implemented context-based consensus and show that using multiple contexts reduces latency and increases the throughput of transaction insertions when compared to consensus without contexts or using single transaction consensus.

Blockchain can effectively deal with the security and trust issues in Internet of Things (IoT) due to its salient features including decentralization, immutability, traceability, openness, and transparency. However, most IoT devices have too limited computing, storage, and bandwidth resources to maintain the complete operation of a blockchain system. To this end, we propose a hierarchical blockchain framework called HLOChain for IoT scenarios. First, according to computing and storage capabilities, the IoT devices are classified into three levels, i.e., high, medium, and low. They are deployed on different layers. In this way, a hierarchical blockchain architecture is designed. Second, we propose a lightweight proof of random (PoR) consensus mechanism to provide low-energy block mining, so that even the medium nodes can participate in the consensus task. Third, in order to reduce the ledger storage overhead, we design a blockchain storage optimization strategy based on the account model. Finally, the security analysis demonstrates that our HLOChain is secure against double-spend attack, Sybil attack, and so on. The experimental evaluation shows that our HLOChain achieves better performance in ledger storage cost, consensus computing cost, throughput, and transaction confirmation latency.