High-Confidence Computing最新文献

In the field of food safety testing, variety, brand, origin, and adulteration are four important factors. In this study, a novel food safety testing method based on infrared spectroscopy is proposed to investigate these factors. Fourier transform infrared spectroscopy data are analyzed using negentropy-sorted kernel independent component analysis (NS-kICA) as the feature optimization method. To rank the components, negentropy is performed to measure the non-Gaussian independent components. In our experiment, the proposed method was run on four datasets to comprehensively investigate the variety, brand, origin, and adulteration of agricultural products. The experimental results show that NS-kICA outperforms conventional feature selection methods. The support vector machine model outperforms the backpropagation artificial neural network and partial least squares models. The combination of NS-kICA and support vector machine (SVM) is the best method for achieving high, stable, and efficient recognition performance. These findings are of great importance for food safety testing.

The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.

Mobile ad hoc networks (MANETs), which correspond to a novel wireless technology, are widely used in Internet of Things (IoT) systems such as drones, wireless sensor networks, and military or disaster relief communication. From the perspective of communication and data collection, the success rate of collaborations between nodes in mobile ad hoc networks and reliability of data collection mainly depend on whether the nodes in the network operate normally, namely, according to the established network rules. However, mobile ad hoc networks are vulnerable to attacks targeting transmission channels and nodes owing to their dynamic evolution, openness, and distributed characteristics. Therefore, during the network operation, it is necessary to classify and detect the behavior and characteristics of each node. However, most existing research only analyzes and considers responses against a single or small number of attacks. To address these issues, this article first systematically analyzed and classified common active attacks in MANETs. Then, a node trust model was proposed based on the characteristics of various attacks; subsequently, a new secure routing protocol, namely, TC-AODV, was proposed. This protocol has minimal effect on the original communication dynamics and can effectively deal with Packet drop, wormhole, Session hijacking, and other main attacks in MANETs. The NS3 simulation results show that the proposed routing protocol attains good transmission performance, can effectively identify various attacks and bypass malicious nodes, and securely complete the communication process.

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

Wireless sensor networks have been deployed in areas such as healthcare, military, transportation and home automation to collect data and forward it to remote users for further processing. Since open wireless communication channels are utilized for data transmissions, the exchanged messages are vulnerable to various threats such as eavesdropping and message falsifications. Therefore, many security solutions have been introduced to address these challenges. However, the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved security. Therefore, lightweight authentication protocols have been presented. Unfortunately, majority of these schemes are still insecure while others incur relatively higher energy, computation, communication and storage complexities. In this paper, a protocol that deploys only lightweight one-way hashing and exclusive OR operations is presented. Its formal security analysis using Real-or Random (ROR) model demonstrates its capability to uphold the security of the derived session keys. In addition, its semantic security evaluation shows that it offers user privacy, anonymity, untraceability, authentication, session key agreement and key secrecy. Moreover, it is shown to resist attacks such as side-channeling, physical capture, eavesdropping, offline guessing, spoofing, password loss, session key disclosure, forgery and impersonations. In terms of performance, it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56% and 33.33% respectively.

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research, particularly with the reliance on deep neural networks. These networks are integral to text reasoning processes, yet they present challenges in extracting and representing domain or commonsense knowledge, and they often lack robust logical reasoning capabilities. To address these issues, we introduce an innovative text reasoning framework. This framework is grounded in the use of a semantic relation graph and a graph neural network, designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.

Our framework operates by extracting knowledge from a broad range of texts. It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process. Beginning with the core question, the framework methodically deduces key knowledge, using it as a guide to iteratively establish a complete evidence chain, thereby determining the final answer. Leveraging the advanced reasoning capabilities of the graph neural network, this approach is adept at multi-hop logical reasoning. It demonstrates strong performance in tasks like machine reading comprehension and question answering, while also clearly delineating the path of logical reasoning.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

Since the data samples on client devices are usually non-independent and non-identically distributed (non-IID), this will challenge the convergence of federated learning (FL) and reduce communication efficiency. This paper proposes FedQMIX, a node selection algorithm based on multi-agent reinforcement learning(MARL), to address these challenges. Firstly, we observe a connection between model weights and data distribution, and a clustering algorithm can group clients with similar data distribution into the same cluster. Secondly, we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward, penalizing the use of more communication rounds and thereby improving the communication efficiency of FL. Finally, experiments show that FedQMIX can reduce the number of communication rounds by 11% and 30% on the MNIST and CIFAR-10 datasets, respectively, compared to the baseline algorithm (Favor).

The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.

Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance. At the same time, current adversarial attack schemes are implemented on static graphs, and the variability of attack models prevents these schemes from transferring to dynamic graphs. In this paper, we use the diffused attack of node injection to attack the DGNNs, and first propose the node injection attack based on structural fragility against DGNNs, named Structural Fragility-based Dynamic Graph Node Injection Attack (SFIA). SFIA firstly determines the target time based on the period weight. Then, it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject. Finally, an optimization function is designed to generate adversarial features for malicious nodes. Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches. When the graph is injected with 1% of the original total number of nodes through SFIA, the link prediction Recall and MRR of the target DGNN link decrease by 17.4% and 14.3% respectively, and the accuracy of node classification decreases by 8.7%.