Adaptive networking control method and application of 5G vehicle networking node

at present, with the development of information technology, 5G network has been born, which also makes the Internet of things and the Internet based on the network develop rapidly. In this context, vehicular ad hoc network technology has attracted much attention. This technology has promoted the development of intelligent transportation from the research, birth and application in practice, so that intelligent transportation has been supported at the technical level, and its intelligent system has been optimized. In addition, vehicular ad hoc network technology plays a key role and value in road rescue, driverless and remote dispatching management. Therefore, this paper not only strengthens the greedy traffi c aware routing, but also optimizes the problems existing in its operation, and puts forward improved methods for the reference of the industry

Design and evaluation of advanced collusion attacks on collaborative intrusion detection networks in practice

Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016, Tianjin, China, 23-26 August 2016To encourage collaboration among single intrusion detection systems (IDSs), collaborative intrusion detection networks (CIDNs) have been developed that enable different IDS nodes to communicate information with each other. This distributed network infrastructure aims to improve the detection performance of a single IDS, but may suffer from various insider attacks like collusion attacks, where several malicious nodes can collaborate to perform adversary actions. To defend against insider threats, challenge-based trust mechanisms have been proposed in the literature and proven to be robust against collusion attacks. However, we identify that such mechanisms depend heavily on an assumption of malicious nodes, which is not likely to be realistic and may lead to a weak threat model in practical scenarios. In this paper, we analyze the robustness of challenge-based CIDNs in real-world applications and present an advanced collusion attack, called random poisoning attack, which derives from the existing attacks. In the evaluation, we investigate the attack performance in both simulated and real CIDN environments. Experimental results demonstrate that our attack can enables a malicious node to send untruthful information without decreasing its trust value at large. Our research attempts to stimulate more research in designing more robust CIDN framework in practice.Department of Computing2016-2017 > Academic research: refereed > Refereed conference paperbcw

Towards highly selective ethylene epoxidation catalysts using hydrogen peroxide and tungsten- or niobium-incorporated mesoporous silicate (KIT-6)

This is the published version. Copyright 2014 Royal Society of ChemistrySignificant ethylene epoxidation activity was observed over Nb- and W-incorporated KIT-6 materials with aqueous hydrogen peroxide (H2O2) as the oxidant and methanol as solvent under mild operating conditions (35 °C and 50 bar) where CO2 formation is avoided. The Nb-KIT-6 materials generally show greater epoxidation activity compared to the W-KIT-6 materials. Further, the ethylene oxide (EO) productivity observed with these materials [30–800 mg EO h−1 (g metal)−1] is of the same order of magnitude as that of the conventional silver (Ag)-based gas phase ethylene epoxidation process. Our results reveal that the framework-incorporated metal species, rather than the extra-framework metal oxide species, are mainly responsible for the observed epoxidation activity. However, the tetrahedrally coordinated framework metal species also introduce Lewis acidity that promotes their solvolysis (which in turn results in their gradual leaching) as well as H2O2 decomposition. These results and mechanistic insights provide rational guidance for developing catalysts with improved leaching resistance and minimal H2O2 decomposition

Structure and electronic transport in graphene wrinkles

Wrinkling is a ubiquitous phenomenon in two-dimensional membranes. In particular, in the large-scale growth of graphene on metallic substrates, high densities of wrinkles are commonly observed. Despite their prevalence and potential impact on large-scale graphene electronics, relatively little is known about their structural morphology and electronic properties. Surveying the graphene landscape using atomic force microscopy, we found that wrinkles reach a certain maximum height before folding over. Calculations of the energetics explain the morphological transition, and indicate that the tall ripples are collapsed into narrow standing wrinkles by van der Waals forces, analogous to large-diameter nanotubes. Quantum transport calculations show that conductance through these collapsed wrinkle structures is limited mainly by a density-of-states bottleneck and by interlayer tunneling across the collapsed bilayer region. Also through systematic measurements across large numbers of devices with wide folded wrinkles, we find a distinct anisotropy in their electrical resistivity, consistent with our transport simulations. These results highlight the coupling between morphology and electronic properties, which has important practical implications for large-scale high-speed graphene electronics.Comment: 5 figures supplemental information in separated fil

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field

Prior works on joint Information Extraction (IE) typically model instance (e.g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding. We find that the previous models generally consider binary type dependency scoring of a pair of instances, and leverage local search such as beam search to approximate global solutions. To better integrate cross-instance interactions, in this work, we introduce a joint IE framework (CRFIE) that formulates joint IE as a high-order Conditional Random Field. Specifically, we design binary factors and ternary factors to directly model interactions between not only a pair of instances but also triplets. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method, which achieves consistent learning and inference. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work