Liang Feng

Blockchain technology has gained widespread adoption across diverse applications; however, its peer-to-peer network architecture remains susceptible to eclipse attacks via malicious neighbor infiltration. Existing defense mechanisms typically rely either on historical data to detect attacks post hoc or on diverse neighbor selection to prevent them. These approaches, however, exhibit critical limitations: detection-based strategies are inherently reactive, while diversity-based selection lacks rigorous quantitative models to characterize the differences between neighbors. To bridge this gap, this paper proposes integrating data analysis techniques directly into the neighbor selection process. Specifically, the proposed method dynamically evaluates peers’ block propagation performance and quantifies inter-peer differences using Wasserstein distance metrics. This enables the application of determinantal point processes (DPPs) to select an optimal set of high-performing and diverse peers as neighbors. Empirical evaluation utilizing Bitcoin network data demonstrates that the proposed scheme simultaneously achieves adaptive neighbor selection and robust protection against eclipse attacks.

Blockchain has been widely applied in many industries to provide secure and reliable services, in which the activities of the participating nodes are recorded as transactions. Although the original design assumes nodes disseminate the transactions voluntarily, they may be reluctant to provide transactions for others due to the lack of cooperative incentives. To fill the gap, we study the transaction collecting process in the blockchain system under the leader-based consensus protocol. Specifically, we design an incentive scheme to reward the followers if they provide unique transactions to the leader. Considering the selfish nature of different nodes, we model the transaction trading process between nodes as an Iterated Prisoner’s Dilemma (IPD), and a modified zero-determinant (ZD) strategy is proposed such that the follower could correlate the leader’s payoff with the leader’s cooperation probability. We theoretically prove the effectiveness of our proposed algorithm. Simulation results show the leader’s payoff changes under the follower’s different control functions. The proposed scheme can regulate the behavior of blockchain nodes during the transaction trading process.

With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF.