15,775 research outputs found

    Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

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    Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013

    Emotions in context: examining pervasive affective sensing systems, applications, and analyses

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    Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

    Information reuse in dynamic spectrum access

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    Dynamic spectrum access (DSA), where the permission to use slices of radio spectrum is dynamically shifted (in time an in different geographical areas) across various communications services and applications, has been an area of interest from technical and public policy perspectives over the last decade. The underlying belief is that this will increase spectrum utilization, especially since many spectrum bands are relatively unused, ultimately leading to the creation of new and innovative services that exploit the increase in spectrum availability. Determining whether a slice of spectrum, allocated or licensed to a primary user, is available for use by a secondary user at a certain time and in a certain geographic area is a challenging task. This requires 'context information' which is critical to the operation of DSA. Such context information can be obtained in several ways, with different costs, and different quality/usefulness of the information. In this paper, we describe the challenges in obtaining this context information, the potential for the integration of various sources of context information, and the potential for reuse of such information for related and unrelated purposes such as localization and enforcement of spectrum sharing. Since some of the infrastructure for obtaining finegrained context information is likely to be expensive, the reuse of this infrastructure/information and integration of information from less expensive sources are likely to be essential for the economical and technological viability of DSA. © 2013 IEEE

    Not All Wireless Sensor Networks Are Created Equal: A Comparative Study On Tunnels

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    Wireless sensor networks (WSNs) are envisioned for a number of application scenarios. Nevertheless, the few in-the-field experiences typically focus on the features of a specific system, and rarely report about the characteristics of the target environment, especially w.r.t. the behavior and performance of low-power wireless communication. The TRITon project, funded by our local administration, aims to improve safety and reduce maintenance costs of road tunnels, using a WSN-based control infrastructure. The access to real tunnels within TRITon gives us the opportunity to experimentally assess the peculiarities of this environment, hitherto not investigated in the WSN field. We report about three deployments: i) an operational road tunnel, enabling us to assess the impact of vehicular traffic; ii) a non-operational tunnel, providing insights into analogous scenarios (e.g., underground mines) without vehicles; iii) a vineyard, serving as a baseline representative of the existing literature. Our setup, replicated in each deployment, uses mainstream WSN hardware, and popular MAC and routing protocols. We analyze and compare the deployments w.r.t. reliability, stability, and asymmetry of links, the accuracy of link quality estimators, and the impact of these aspects on MAC and routing layers. Our analysis shows that a number of criteria commonly used in the design of WSN protocols do not hold in tunnels. Therefore, our results are useful for designing networking solutions operating efficiently in similar environments
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