Baochao Chen’s research while affiliated with Tianjin University and other places

Blockchain is widely applied in logistics, finance, and agriculture. As single blockchain users grow, scalability becomes crucial. However, existing works lack a comprehensive summary of blockchain scalability. They focus on single chains or cross-chain technologies. This survey summarizes scalability across the physical and logical layers, as well as inner-chain, inter-chain, and technology dimensions. The physical layer covers data and protocols, while the logical layer represents blockchain architecture. Each component is analyzed from inner-chain and inter-chain perspectives, considering technological factors. The aim is to enhance researchers' understanding of blockchain's architecture, data, and protocols to advance scalability research.

Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this paper proposes BSSN, a Blockchain Storage technology based on Social Networks. The combined use of storage cost and query cost is introduced for the first time to formulate the Node Allocation Optimization (NAO) problem, and the Multi-population Genetic Ant Colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges. (i) To predict the transactions that nodes will participate in in the future, we employ the social ties to obtain the access frequencies among users. (ii) To strike a balance between the storage cost and query cost, we jointly model the two costs as a multi-objective optimization problem to formulate the NAO problem. (iii) To solve the NP-hard NAO problem, we use MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.

As more and more Internet of Things (IoT) applications move to the edge clouds, the scarce resource cannot satisfy the users’ ever-growing demands. Usually, users are charged by Edge Service Providers (ESPs) and access the service according to their preference and budget. The usual non-interactive transactions aggravate the imbalance and inefficiency of the edge resource. Moreover, the network dynamics and uncertain user demands pose a considerable challenge to the operator. To achieve the load balancing and satisfy the users’ satisfaction simultaneously, we put forward an online congestion-aware dynamic pricing based resource allocation method. We aim at maximizing the operator’s profit and leverage the two-timescale Lyapunov optimization technique to jointly balance the utility maximization and system stability. More briefly, it allows the ESP operator to make the online decisions on how much the computing units should be charged at a coarse-grained timescale, when and where to serve the users’ demands at a fine-grained timescale. We conduct rigorous theoretical analysis and verify the performance guarantee of our online control algorithm. The simulation results show that the proposed method can effectively improve the total utility and control the deadline miss rate within a relatively low range. Meanwhile, it turns out to be robust to the uncertain request estimation errors.

Industrial mobile network is crucial for industrial production in Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However this characteristic might be utilized by spammers to attack others and influence industrial production. The users who only share spams such as links with virus and advertisement are called spammers. With the growth of mobile network members, spammers are organized in groups for the purpose of benefit maximization, which has caused confusion and heavy loss to industrial production. It is difficult to distinguish spammers from normal users due to characteristics of multidimensional data. To address this problem, this paper proposes a Spammer Identification scheme based on Gaussian Mixture Model (SIGMM) that utilizes machine learning for industrial mobile networks. It works on the intelligent identification of spammers without relying on the flexible and unreliable relationship. SIGMM combines the presentation of data. Each user node is classified to one class in the construction process of model. Finally, we validate SIGMM by comparing it with RMA, HFCM schemes using mobile network dataset from cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision and time complexity.

With the popularity of mobile devices and the development of the wireless technologies, humans can be connected ubiquitously. Because of the mobility of the devices, it is a hard task to maintain the end-to-end path between source node and destination node. Researchers have introduced analysis of nodes’ social behavior to solve the problem of data dissemination in the networks, which leads to the emersion of the Mobile Social Networks (MSNs). They increase the performance of data forwarding by the social relationships and interactions among nodes. Many schemes and algorithms have been proposed to enhance data forwarding performance and provide humanized service by introducing social features and digging social properties. In this paper, first, we investigate the architectures and evolutionary process of the MSNs. Then, the social features of nodes existing in the MSNs and main social properties of MSNs are described. In term of the state-of-the-art works, data forwarding strategies in the MSNs are divided into four categories, including only encounter history-based strategies, social-based strategies, incentive mechanisms, and forwarding methods that take social selfishness into consideration of this paper. Finally, the major issues and challenges are discussed.