Transactions In Order Research Articles

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies. The algorithm introduces an optimistic response assumption, employs a message aggregation tree to collect and validate votes, and uses a dynamically adjusted threshold mechanism to reduce communication delay and improve message delivery reliability. Additionally, a dynamic channel mechanism and an asynchronous leader multi-round mechanism are introduced to address multiple points of failure in the message aggregation tree structure, minimizing dependence on a single leader. This adaptation can be flexibly applied to real-world system conditions to improve performance and responsiveness. We conduct experimental evaluations to verify the algorithm's effectiveness and superiority. Compared to the traditional HotStuff algorithm, the improved algorithm demonstrates higher efficiency and faster response times in handling faulty copies and transaction ordering.

A Study on “Queuing up for a House”Fake News Incident’s Online Opinions Toward Real Estate Transaction Chaos

<p>On Oct. 20th, 2019, the news broke that a real estate agency used fake news to hype up pre-sale houses, which endangered the order of market transactions, and this incident triggered heated discussions online. This study aims to understand the impact of this fake news on society, understand the attitudes and opinions of the practitioners, and further analyze the impact from the perspective of information dissemination. The study used focus group interviews with real estate agents to understand the motivation and purpose of fake news use; then, we analyzed observed public opinion big data. The study found that the biggest problem is that laws and regulations are not implemented, causing many public grievances. Real estate transactions must be regulated and returned to self-occupancy demand rather than market speculation. Big data analysis found that the absence of stereotyped contracts for buying and selling is severe; in addition, fake news on social media has become a shortcut to hype new projects, further disturbing the market. This kind of chaos disrupts the order of transactions and discourages consumers. The study suggests that the policy of "real price registration 2.0" should be implemented, and only after the builder obtains the construction license can they start advertising to eliminate the phenomenon of “red slip transactions.” The government needs to strengthen the investigation of false advertisements, establish real estate advertising regulations, form a self-discipline committee, and improve the development of the industry through the guidance and assistance of external experts.</p> <p> </p>

A Geth-based real-time detection system for sandwich attacks in Ethereum

With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart contracts and blockchain transactions has attracted more and more attention. In particular, front-running attacks on the Ethereum platform have become a major security concern. These attack strategies exploit the transparency and certainty of the blockchain, enabling attackers to gain unfair economic benefits by manipulating the transaction order. This study proposes a sandwich attack detection system integrated into the go-Ethereum client (Geth). This system, by analyzing transaction data streams, effectively detects and defends against front-running and sandwich attacks. It achieves real-time analysis of transactions within blocks, quickly and effectively identifying abnormal patterns and potential attack behaviors. The system has been optimized for performance, with an average processing time of 0.442 s per block and an accuracy rate of 83%. Response time for real-time detection new blocks is within 5 s, with the majority occurring between 1 and 2 s, which is considered acceptable. Research findings indicate that as a part of the go-Ethereum client, this detection system helps enhance the security of the Ethereum blockchain, contributing to the protection of DeFi users’ private funds and the safety of smart contracts. The primary contribution of this study lies in offering an efficient blockchain transaction monitoring system, capable of accurately detecting sandwich attack transactions within blocks while maintaining normal operation speeds as a full node.

Rashnu: Data-Dependent Order-Fairness

Distributed data management systems use state Machine Replication (SMR) to provide fault tolerance. The SMR algorithm enables Byzantine Fault-Tolerant (BFT) protocols to guarantee safety and liveness despite the malicious failure of nodes. However, SMR does not prevent the adversarial manipulation of the order of transactions, where the order assigned by a malicious leader differs from the order in that transactions are received from clients. While order-fairness has been recently studied in a few protocols, such protocols rely on synchronized clocks, suffer from liveness issues, or incur significant performance overhead. This paper presents Rashnu , a high-performance fair ordering protocol. Rashnu is motivated by the fact that fair ordering among two transactions is needed only when both transactions access a shared resource. Based on this observation, we define the notion of data-dependent order fairness where replicas capture only the order of data-dependent transactions and the leader uses these orders to propose a dependency graph that represents fair ordering among transactions. Replicas then execute transactions using the dependency graph, resulting in the parallel execution of independent transactions. We implemented a prototype of Rashnu where our experimental evaluation reveals the low overhead of providing order-fairness in Rashnu.

부동산 중개사고 예방을 위한 실증연구

This study aims to recognize the seriousness of real estate brokerage accidents that may occur in real estate leasing contracts and to identify important factors that can be prioritized to prevent them. To prevent this from happening, let's take a look at the types of real estate brokerage accidents and how they occur, as well as the current state of lease damage. Then, based on the factors selected from government policies, reports, papers, and 1:1 in-depth interviews with experts, we prioritized the factors that are important to prevent real estate brokerage accidents in real estate lease contracts through the Analytical Hierarchy Process (AHP). The results of the analysis showed that the following steps were taken: researching and verifying the seniority of the security deposit and the date of occupancy for multifamily housing units, checking the seniority rights, checking and notifying the availability of a return guarantee for the security deposit, verifying the owner (trust property, registered owner), and checking the status of the property with public records (also checking the status of the building). The results of this study will be used as a basis for preventing real estate brokerage accidents when signing real estate leases to establish transaction order in the real estate market.

A Design Flow for Scheduling Spiking Deep Convolutional Neural Networks on Heterogeneous Neuromorphic System-on-Chip

Neuromorphic systems-on-chip (NSoCs) integrate CPU cores and neuromorphic hardware accelerators on the same chip. These platforms can execute spiking deep convolutional neural networks (SDCNNs) with a low energy footprint. Modern NSoCs are heterogeneous in terms of their computing, communication, and storage resources. This makes scheduling SDCNN operations a combinatorial problem of exploring an exponentially-large state space in determining mapping, ordering, and timing of operations to achieve a target hardware performance, e.g., throughput. We propose a systematic design flow to schedule SDCNNs on an NSoC. Our scheduler, called SMART ( S DCNN MA pping, Orde R ing, and T iming), branches the combinatorial optimization problem into computationally-relaxed sub-problems that generate fast solutions without significantly compromising the solution quality. SMART improves performance by efficiently incorporating the heterogeneity in computing, communication, and storage resources. SMART operates in four steps. First, it creates a self-timed execution schedule to map operations to compute resources, maximizing throughput. Second, it uses an optimization strategy to distribute activation and synaptic weights to storage resources, minimizing data communication-related overhead. Third, it constructs an inter-processor communication (IPC) graph with a transaction order for its communication actors. This transaction order is created using a transaction partial order algorithm, which minimizes contention on the shared communication resources. Finally, it schedules this IPC graph to hardware by overlapping communication with the computation, and leveraging operation, pipeline, and batch parallelism. We evaluate SMART using 10 representative image, object, and language-based SDCNNs. Results show that SMART increases throughput by an average 23%, compared to a state-of-the-art scheduler. SMART is implemented entirely in software as a compiler extension. It doesn’t require any change in a neuromorphic hardware or its interface to CPUs. It improves throughput with only a marginal increase in the compilation time. SMART is released under the open-source MIT licensing at https://github.com/drexel-DISCO/SMART to foster future research.

Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion.

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.

Research on Blockchain Architecture and Operating Principles Based on H-DAG

A hybrid blockchain structure (hybrid directed acyclic graph, or H-DAG) is proposed in this article to solve the existing problem of blockchain architectures using symmetric key encryption technology by combining the characteristics of single-chain blockchains and DAG distributed ledgers. By improving the block and transaction structures and optimizing the consensus mechanism, the H-DAG confirmed transaction orders while maintaining the high-throughput characteristics of a DAG, thus solving the transaction order dependence problem. We introduced a lightweight PoW mechanism to the H-DAG to improve the anti-fork ability of the blockchain. An incentive mechanism was adopted in our model to compel honest nodes to be more enthusiastic about participating in, maintaining, and enhancing the security of a given network. The blockchain states achieved strong levels of consistency, and their transaction confirmation times were predictable. We evaluated the performance of the H-DAG by comparing and analyzing multiple experiments, and we modeled a forking attack strategy, verifying the resistance of the H-DAG to this attack strategy. The experimental results demonstrated that the order of transactions in the H-DAG was globally consistent, and the confirmation time of transactions was predictable. The H-DAG improved the anti-fork ability and enhanced the security of the blockchain to ensure a degree of decentralization of the blockchain system. Therefore, the system throughput was enhanced by improving the block structure using symmetric key technology.

Research on smart contract vulnerability detection method based on domain features of solidity contracts and attention mechanism

The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses.

Directed Acyclic Graph-Type Distributed Ledgers via Young-Age Preferential Attachment

Distributed ledger technologies provide a mechanism to achieve ordering among transactions that are scattered on multiple participants with no prerequisite trust relations. This mechanism is essentially based on the idea of new transactions referencing older ones in a chain structure. Recently, directed acyclic graph (DAG)-type distributed ledgers that are based on DAGs were proposed to increase the system scalability through sacrificing the total order of transactions. In this paper, we develop a mathematical model to study the process that governs the addition of new transactions to the DAG-type distributed ledger. We propose a simple model for DAG-type distributed ledgers that are obtained from a recursive young-age preferential attachment scheme (i.e., new connections are made preferably to transactions that have not been in the system for very long). We determine the asymptotic degree structure of the resulting graph and show that a forward component of linear size arises if the edge density is chosen sufficiently large in relation to the “young-age preference” that tunes how quickly old transactions become unattractive. Funding: The research of C. Mönch is supported by the Deutsche Forschungsgemeinschaft [Grant 443916008].

Directed Acyclic Graph-Type Distributed Ledgers via Young-Age Preferential Attachment

Distributed ledger technologies provide a mechanism to achieve ordering among transactions that are scattered on multiple participants with no prerequisite trust relations. This mechanism is essentially based on the idea of new transactions referencing older ones in a chain structure. Recently, directed acyclic graph (DAG)-type distributed ledgers that are based on DAGs were proposed to increase the system scalability through sacrificing the total order of transactions. In this paper, we develop a mathematical model to study the process that governs the addition of new transactions to the DAG-type distributed ledger. We propose a simple model for DAG-type distributed ledgers that are obtained from a recursive young-age preferential attachment scheme (i.e., new connections are made preferably to transactions that have not been in the system for very long). We determine the asymptotic degree structure of the resulting graph and show that a forward component of linear size arises if the edge density is chosen sufficiently large in relation to the “young-age preference” that tunes how quickly old transactions become unattractive. Funding: The research of C. Mönch is supported by the Deutsche Forschungsgemeinschaft [Grant 443916008].

위약벌에 관한 민법 제398조 제2항의 유추적용 가능성

Recently, the Supreme Court ruled that the penalty agreement cannot be reduced because it is different from the liquidated damages. These results are the same position as conventional precedents, and it can be seen that the current precedent is valid and should be maintained as it is. In other words, as a reaffirmation of the validity of the current precedent, penalty cannot be reduced in principle, and penalty can be exceptionally reduced only if they have the nature of liquidated damages at the same time. There are similar aspects of the liquidated damages and the penalty regarding function, and as a result, there are continuous precedents that do not distinguish the concept of the liquidated damages and the penalty. Rather than distinguishing the nature of the damages for breach of contract between the parties, Article 398 of the Civil Code should be improved in a way that allows the party who fails to fulfill the contract or violates the contract to compensate the creditor for a reasonable amount. In other words, it would be desirable to integrate the two concepts into one rather than separate and regulate them in different ways. Accordingly, I agreed that the liquidated damages and the penalty under the 2013 revision of the Ministry of Justice are not separately distinguished, but are integrated into the upper concept of penalty. In Paragraph 3 of the amendment, if the penalty is unfairly excessive, it is reasonable for the court to reduce the penalty. In addition, it is reasonable to delete Paragraph 2 of the amendment, which assumes that damages for breach of contract is to be the liquidated damages. Through this, we intend to establish a fair transaction order by pursuing legal stability while respecting private autonomy between the parties regarding the damages for breach of contract.

Chronos: An Efficient Asynchronous Byzantine Ordered Consensus

Abstract Byzantine ordered consensus, introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s.

Unsupervised Abnormal Transaction Order Detection Method Based on Deep Learning Time Factor

Abnormal transaction order detection plays an important role in identifying transaction data changes and abnormal transaction behaviors of users. The existing abnormal order detection methods have low generalization ability in different transaction time series, and the exception itself has a variety of patterns. In addition, the positive sample points with a large proportion carry much more information than the abnormal sample points, which will cause the classifier to be disturbed in the learning process and inaccurate classification. The research proposes an unsupervised abnormal transaction order detection method based on the depth learning time factor. By processing the original data column, the first feature sequence is obtained, and then the later feature sequence is trained through the depth learning model. The abnormal order is detected by comparing the differences between the two feature sequences. The results show that the research effectively solves the practical problem of too few abnormal transaction samples, can be applied to the transaction process of different modes, reduces the noise in the original transaction data, and has obvious improvement in each evaluation index compared with the traditional method.

Явище maximal extractable value (mev) в мережах блокчейн та його вплив на блокчейн екосистему

The advent of smart contract technology in blockchain networks has ushered in a new era of possibilities for implementing complex decentralized finance protocols. Over time, these protocols have gained significant traction, reaching a Total Value Locked (TVL) of over 150 billion US dollars. While blockchain networks offer inherent benefits such as immutability, transparency, decentralization, and security, they still grapple with a critical challenge – the inability to ensure a predictable order of transactions within produced blocks. This limitation has given rise to the Maximal Extractable Value (MEV) phenomenon. MEV represents the maximum potential benefit that certain network participants, primarily miners and validators, can extract by wielding their exclusive capability to influence transaction order. In this work, we embark on an exhaustive exploration of the MEV phenomenon and delve deep into its impact on the broader blockchain ecosystem. We shed light on the pressing issue of transaction ordering in blockchain networks and provide an in-depth survey of the vast body of scholarly publications focused on MEV extraction. This comprehensive review allowed us to conduct a retrospective analysis of the MEV phenomenon, categorize its most common manifestations, and uncover current development trends. Intriguingly, during this analysis, parallels were drawn with similar manipulations witnessed in the realm of high-frequency algorithmic trading within traditional financial markets. A vital conclusion that emerged from our study pertains to possible strategies for addressing the MEV problem within decentralized finance protocols. We systematically outline the current research directions concerning MEV, explore the methodologies and tools employed in these studies, and present concrete examples of MEV extraction within the Ethereum network, accompanied by quantitative estimations. In summary, the MEV phenomenon has cast an overwhelming negative impact on blockchain networks and decentralized finance. Our analysis of existing publications within a specific subcategory reveals the current absence of an effective solution to the MEV extraction problem. This underscores the importance of further research aimed at mitigating the adverse effects of MEV on blockchain networks and decentralized finance protocols.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class. This will mitigate the feature vector's dimensionality by eliminating redundant, irrelevant, or noisy data. This work presents a new hybrid recommender system based on optimized feature selection and systolic tree. The features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), feature selection with the utilization of River Formation Dynamics (RFD), and the Particle Swarm Optimization (PSO) algorithm. The systolic tree is used for pattern mining, and based on this, the recommendations are given. The proposed methods were evaluated using the MovieLens dataset, and the experimental outcomes confirmed the efficiency of the techniques. It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborative filtering, the precision of 0.89 was achieved.

Social Characteristic-Based Propagation-Efficient PBFT Protocol to Broadcast in Unstructured Overlay Networks

Blockchain allows for secure management of a shared ledger by agreement protocols, where transactions are validated over network without central authorities. Although the agreement protocol has been thoroughly conducted of propagation and consensus researches, mobility of nodes in unstructured overlay networks has not received much attention. Besides, current dynamic propagation schemes waste travel hops and are low of delivery ratio. In this article, we propose a social characteristic-based propagation-efficient protocol NefSBFT to agree on system state plus consensus mechanisms in public blockchains. We devise a propagation technique (travel hops of at least <inline-formula><tex-math notation="LaTeX">$\frac{1}{3}$</tex-math></inline-formula> savings, delivery ratio above 0.93, etc.) for message multicasting when exploiting real nodes’ social characteristics of intermittent connectivity and frequent partitions. This propagation technique is executed in the improved FastBFT to achieve transaction ordering and block verification, thus, no controllable mobility is required during the whole system’s execution. NefSBFT achieves fast propagation, small message complexity and few resource consumption of travel hops and running nodes for complete protocol execution. We analyze NefSBFT’s security against DDOS attack of non-primary failure. The experiments show the performance tradeoff under different parameters, compare the propagation efficiency with Erlay and Flooding, and clarify NefSBFT’s impact on the whole system performance through comparison.

Benchmarking state-of-the-art imbalanced data learning approaches for credit scoring

The goal of credit scoring is to identify abnormalities, aiding decision making and maintaining the order of financial transactions. Due to the small number of default records, one inevitably faces a class imbalance problem when handling financial data. The class imbalance problem has received a lot of attention because of the economic loss that can occur when one fails to accurately identify default samples. To solve this problem, there are various classic and mature approaches to learning imbalanced data, including resampling approaches, cost-sensitive strategies, and so on. Especially in recent years, generative adversarial networks (GANs) have attracted the attention of researchers to explore these networks’ effects as imbalanced data learning tools. However, no attention has been paid to the systematic scoring and comparison of these traditional and state-of-the-art imbalanced data learning approaches in relation to credit scoring. Therefore, choosing several related datasets, we compare the performance of the traditional approaches and GANs in solving the class imbalance problem of credit scoring; at the same time, with the help of benchmark analysis, we provide some suggestions for relevant research.

가상자산거래와 소비자보호법의 과제

E-commerce on virtual assets is rapidly developing. However, laws governing virtual asset transactions have not been enacted. However, the Specific Financial Information Act was revised in 2020 to regulate virtual assets, but there are limitations in that it does not regulate specific details on additional asset transactions because the purpose is to establish a transparent financial transaction order. Therefore, the need to regulate legal relations for rapidly increasing virtual asset transactions has been raised. In this paper, i examined whether virtual asset transactions can be properly regulated, focusing on the laws related to consumer protection among the current laws. Of course, e-commerce law etc. are applied since virtual assets are a kind of digital content. However, it is not reasonable to apply the withdrawal of offer to speculative transactions and it causes a bigger problem when applying regulations with many contradictions. In addition, the Terms and Conditions Regulation Act has a limitation in that it cannot regulate third-party terms and conditions. These points can be said to be inevitable limitations or problems because the current laws on consumer protection were enacted before virtual assets appeared. Even so, it is not reasonable to stand by even when virtual asset transactions are active. If so, how do you solve the consumer problem arising from virtual asset transactions? In response, it is possible to propose a plan to regulate virtual asset transactions by amending the current consumer protection laws or enact a special law on virtual asset transactions. However, it would be desirable to solve it in the latter way since it is not appropriate to apply the current consumer protection law to highly speculative virtual asset transactions. Considering this, the special law on virtual asset transactions needs to be enacted as soon as possible.

A coalitional game theoretic energy transaction algorithm for networked microgrids

With the large-scale application of microgrid technology in distribution networks, emerging energy transactions among multiple microgrids as well as between microgrids and the grid increase the operational burden to the power system. To coordinate these energy transactions, a coalitional game theoretic energy transaction algorithm for networked microgrids is developed in this paper to improve energy exchange efficiency. First, a distance-oriented method for maximizing coalition utilities is proposed to guide the order of transactions in a coalition, and the corresponding coalition utility would be fairly allocated by the Shapley value. Second, a modified coalition formation algorithm consisting of perturbation, merge and split procedures is designed to obtain a Dc-stable otherwise a better Dhp-stable partition on the network, in which no microgrid has an incentive to leave this partition, and the corresponding theoretical proof is given in this paper. Finally, relative to the non-cooperation mode and the Merge&Split procedure, simulation results show the advantages of the proposed algorithm in reducing costs and facilitating energy transactions in the network.