As vehicular networks become popular, more and more people want to access data from their vehicles. When many vehicles want to access data through a roadside unit, data scheduling becomes an important issue. In this paper, we identify some challenges in vehicle-roadside data access. As vehicles move pretty fast, the requests should be served quickly. Also, vehicles may upload data to the roadside unit, and hence the download and upload requests compete for the same bandwidth. To address these challenges, we propose several scheduling schemes. We first propose a basic scheduling scheme called D * S to consider both service deadline and data size. We then enhance it by using a single broadcast to serve multiple requests. Finally, we identify the effects of upload requests on data quality, and propose a Two-Step scheduling scheme to provide a balance between serving download and update requests. Simulation results show that the Two-Step scheduling scheme outperforms other scheduling schemes.

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Abstract-Some recent studies have shown that cooperative cache can improve the system performance in wireless P2P networks such as ad hoc networks and mesh networks. However, all these studies are at a very high level, leaving many design and implementation issues unanswered. In this paper, we present our design and implementation of cooperative cache in wireless P2P networks, and propose solutions to find the best place to cache the data. We propose a novel asymmetric cooperative cache approach, where the data requests are transmitted to the cache layer on every node, but the data replies are only transmitted to the cache layer at the intermediate nodes that need to cache the data. This solution not only reduces the overhead of copying data between the user space and the kernel space, it also allows data pipelines to reduce the end-to-end delay. We also study the effects of different MAC layers, such as 802.11-based ad hoc networks and multi-interface-multichannel-based mesh networks, on the performance of cooperative cache. Our results show that the asymmetric approach outperforms the symmetric approach in traditional 802.11-based ad hoc networks by removing most of the processing overhead. In mesh networks, the asymmetric approach can significantly reduce the data access delay compared to the symmetric approach due to data pipelines.

In recent years, trust-aware routing protocol plays a vital role in security of wireless sensor networks (WSNs), which is one of the most popular network technologies for smart city. However, several key issues in conventional trust-aware routing protocols still remain to be solved, such as the compatibility of trust metric with QoS metrics and the control of overhead produced by trust evaluation procedure. This paper proposes a trust-aware secure routing framework (TSRF) with the characteristics of lightweight and high ability to resist various attacks. To meet the security requirements of routing protocols in WSNs, we first analyze features of common attacks on trust-aware routing schemes. Then, specific trust computation and trust derivation schemes are proposed based on analysis results. Finally, our design uses the combination of trust metric and QoS metrics as routing metrics to present an optimized routing algorithm. We show with the help of simulations that TSRF can achieve both intended security and high efficiency suitable for WSN-based networks.

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BackgroundThe digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models.MethodsDatasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation.ResultsWithin each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined.ConclusionsWe have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two.