For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minimum segments (LEMS, for short) is proposed in this paper. LEMS begins by measuring the distance between paired nodes, which is completed along with network initialization. Then, each unlocated node constructs its own sub-network, including it, based on the error characteristics among anchor nodes. Finally, each unlocated node searches for its estimated location in its sub-region based on the objective function generated by the chaotic mapping. Simulation results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art regarding efficiency, accuracy, and stability for various irregular networks. Specifically, our proposed algorithm achieves a median improvement in localization accuracy of 0.62 to 29.57 times and a reduction in the range of localization errors of 0.06 to 16.8 times.

Accurate geographical information of nodes is crucial for network applications. However, many existing positioning algorithms face challenges in achieving efficient, accurate, and robust performance when applied to irregular networks with holes or obstacles. Therefore, we introduce a new algorithm, named Network Division Positioning (NDP), to tackle this issue. In NDP, we use a similarity function to derive the distance between neighboring nodes and explore routing paths concurrently, facilitating efficient distance measurement. Next, we analyze measurement errors between landmark nodes to define a threshold that filters out incorrect distances, ensuring measuring and positioning accuracy. To enhance robustness, we first identify collinearity issues by examining the positional relationship between unpositioned nodes and their nearest landmark. Subsequently, we addressed the poor positioning results and built the subnetwork utilizing the nearest landmark node and its associated measurement distance, seeking the most accurate and robust estimated position within this subnetwork. The simulation results demonstrate that NDP outperforms state-of-the-art algorithms in terms of efficiency, accuracy, and robustness when dealing with various irregular networks. Specifically, NDP enhances positioning accuracy by at least 40.82% in terms of the median.

Data binding has been widely adopted by popular web frameworks due to its convenience of automatically binding web request parameters to the web program's properties. However, its improper implementation in web frameworks exposes sensitive properties, leading to data binding vulnerabilities, which can be exploited to launch severe attacks, such as the Spring4Shell remote code execution. Despite their criticalness, these issues are overlooked, and there is no systematic study addressing them. This paper presents the first automatic analysis of the data binding vulnerabilities in Java web frameworks. We develop an automatic Data bInding Vulnerabilities dEtectoR, named DIVER, to analyze data binding vulnerabilities. DIVER employs three new techniques: the Nested Property Graph-based Extraction to extract nested properties, the Bind-Site Instrumentation-based Identification to identify bindable nested properties, and the Property-aware Fuzzing to trigger and detect data binding vulnerabilities. We evaluated DIVER on two widely used Java web frameworks, Spring and Grails, and discovered 81 data binding vulnerabilities. These vulnerabilities can be exploited to launch remote code execution, arbitrary file read, and denial of service attacks. We have responsibly reported these vulnerabilities to the corresponding teams and helped to fix them. Three new CVEs with critical and high severity ratings have been assigned to us, including the infamous Spring4Shell.

Node positioning is an essential function of wireless networks and serves as the foundation for many applications. In the existing works, the cooperative positioning approaches have been extensively studied and are shown to be effective for scenarios with energy and cost constraints. However, these approaches may not perform well in concave networks with holes or obstacles. To address this issue, this paper proposes adistributed cooperative positioning model with adaptive hop-range selection(DCP-AHS for short) for concave networks. DCP-AHS first uses a low-complexity and fast convergent distance estimation method based on the local neighbor nodes. It then uses an adaptive hop-range selection method based on the residual analysis between pairs of anchors. Within the hop range, an unknown node uses multi-lateration with the optimal weight function to determine its estimated position. Finally, a weighted Bounding-Box method with the virtual anchor is employed to avoid significant position estimation errors caused by the collinearity issues. Simulation results demonstrated that the proposed DCP-AHS significantly outperformed the existing algorithms regarding efficiency, accuracy, and stability in various concave networks. Specifically, our proposed model achieved a median improvement of 16.62% to 81.65% in positioning accuracy compared to the comparison algorithms.

Satellite Internet of Things (S-IoT), which integrates satellite networks with IoT, is a new mobile Internet to provide services for social networks. However, affected by the dynamic changes of topology structure and node status, the efficient and secure forwarding of data packets in S-IoT is challenging. In view of the abovementioned problem, this paper proposes an adaptive routing strategy based on improved double Q-learning for S-IoT. First, the whole S-IoT is regarded as a reinforcement learning environment, and satellite nodes and ground nodes in S-IoT are both regarded as intelligent agents. Each node in the S-IoT maintains two Q tables, which are used for selecting the forwarding node and for evaluating the forwarding value, respectively. In addition, the next hop node of data packets is determined depending on the mixed Q value. Second, in order to optimize the Q value, this paper makes improvements on the mixed Q value, the reward value, and the discount factor, respectively, based on the congestion degree, the hop count, and the node status. Finally, we perform extensive simulations to evaluate the performance of this adaptive routing strategy in terms of delivery rate, average delay, and overhead ratio. Evaluation results demonstrate that the proposed strategy can achieve more efficient and secure routing in the highly dynamic environment compared with the state-of-the-art strategies.

Abstract Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.

The deployment of Mobile Edge Computing (MEC) servers on Low Earth Orbit (LEO) satellites to form MEC satellites is of increasing concern. A routing strategy is the key technology in MEC satellites. To solve the uncertainty problem of LEO satellite link information caused by complex space environments, a routing strategy for LEO satellite networks based on membership degree functions is proposed. First, a routing model based on uncertain link information is established. In particular, the membership function is designed to describe the uncertain link information. Based on this, the comprehensive evaluation of the path is calculated, and the routing model considering uncertainty is established with the comprehensive evaluation of the path as the optimization objective. Second, in order to quickly calculate the path, a grey wolf optimization algorithm is designed to solve the routing model. Finally, simulation results show that the proposed strategy can achieve efficient and secure routing in complex space environments and improve the overall performance compared with the performances of traditional routing strategies.

The Underwater Sensor Network (USN) has great advantages in marine environmental monitoring. When collecting the perception information of sensor nodes, the mobile node can effectively compensate for the shortcomings of traditional multi-hop transmission modes. However, the complex marine environment causes the location uncertainty of nodes. Therefore, a path planning method based on the location uncertainty of water surface nodes in USN is proposed in this paper. Firstly, the structure of the USN based on the mooring model is introduced and the problem model of path planning is proposed. Secondly, the inevitable communication circle is obtained by analyzing the deviation range and communication range of water surface nodes. Thirdly, Convex Hull algorithm is used to plan the path in accordance with the inevitable communication circle of water surface nodes. Finally, simulation results show that the proposed method can obtain a shorter path under the premise of ensuring the completion of information collection.

The hop‐based localisation algorithm uses hop‐by‐hop propagation to establish node‐to‐anchor distance estimation, which does not require costly and complicated ranging hardware. This helps boost system performance, while minimising the cost of localising the nodes within the network. However, the application of hop‐based localisation algorithms is restricted due to their dramatic accuracy degradation in irregular network, which is mainly caused by the large error of distance estimation. The authors find that the error variance of the estimated distance increases as the hop count increases, i.e. there is a heteroscedasticity problem in the distance estimation process, which will affect the location estimation. In this study, by exploring the error during the location estimation, they aim to find and employ the optimal weighted function to improve localisation accuracy. A geometric constraint algorithm is also devised to correct the incorrectly estimated location by mitigating the adverse effects from flip ambiguity. By combining the optimal weighted function and the geometric constraint algorithm, a novel hop‐based localisation algorithm is proposed in this study. Both the theoretical analysis and experimental results show that the proposed method has not only maintained the economic characteristics of hop‐based localisation, but also has the high localisation accuracy where it can be adapted to various networks with different node distributions.