330 research outputs found

    Security and Privacy for Modern Wireless Communication Systems

    Get PDF
    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    2019 GREAT Day Program

    Get PDF
    SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp

    CROWDSOURCED DATA FOR MOBILITY ANALYSIS

    Get PDF
    The importance of data in transportation research has been widely recognized since it plays a crucial role in understanding and analyzing the movement of people, identifying inefficiencies in transportation systems, and developing strategies to improve mobility services. This use of data, known as mobility analysis, involves collecting and analyzing data on transport infrastructure and services, traffic flows, demand, and travel behavior. However, traditional data sources have limitations. The widespread use of mobile devices, such as smartphones, has enabled the use of Information and Communications Technology (ICT) to improve data sources for mobility analysis. Mobile crowdsensing (MCS) is a paradigm that uses data from smart devices to provide researchers with more detailed and real-time insights into mobility patterns and behaviors. However, this new data also poses challenges, such as the need to fuse it with other types of information to obtain mobility insights. In this thesis, the primary source of data that is being examined and leveraged is the popularity index of local businesses and points of interest from Google Popular Times (GPT) data. This data has significant potential for mobility analysis as it overcomes limitations of traditional mobility data, such as data availability and lack of reflection of demand for secondary activities. The main objective of this thesis is to investigate how crowdsourced data can contribute to reduce the limitations of traditional mobility datasets. This is achieved by developing new tools and methodologies to utilize crowdsourced data in mobility analysis. The thesis first examines the potential of GPT as a source to provide information on the attractiveness of secondary activities. A data-driven approach is used to identify features that impact the popularity of local businesses and classify their attractiveness based on these features. Secondly, the thesis evaluates the possible use of GPT as a source to estimate mobility patterns. A tool is created to use the crowdness of a station to estimate transit demand information and map the precise volume and temporal dynamics of entrances and exits at the station level. Thirdly, the thesis investigates the possibility of leveraging the popularity of activities around stations to estimate flows in and out of stations. A method is proposed to profile stations based on the dynamic information of activities in catchment areas. Through this data, machine learning techniques are used to estimate transit flows at the station level. Finally, this study concludes by exploring the possibility of exploiting crowdsourced data not only for extracting mobility insights under normal conditions but also to extract mobility trends during anomalous events. To this end, we focused on analyzing the recovery of mobility during the first outbreak of COVID-19 for different cities in Europe

    Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

    Full text link
    Public transit systems have faced challenges and opportunities from emerging Shared Autonomous Mobility Services (SAMS). This study addresses a city-scale multimodal transit network design problem, with shared autonomous vehicles as both transit feeders and a direct interzonal mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and designs transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, while satisfying existing transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each clustered zone are characterized with continuous approximation. Afterward, the decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. To demonstrate the framework efficiency, this study uses transit demand from the Chicago metropolitan area to redesign a multimodal transit network. The computational results present savings in travelers' journey time and operators' costs, demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.Comment: 44 pages, 15 figures, under review for the 25th International Symposium on Transportation and Traffic Theory (ISTTT25

    LIPIcs, Volume 274, ESA 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 274, ESA 2023, Complete Volum

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

    Get PDF
    No abstract available

    Geo-Information Technology and Its Applications

    Get PDF
    Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

    Transport systems analysis : models and data

    Get PDF
    Funding: This research project has been funded by Spanish R+D Programs, specifcally under Grant PID2020-112967GB-C31.Rapid advancements in new technologies, especially information and communication technologies (ICT), have significantly increased the number of sensors that capture data, namely those embedded in mobile devices. This wealth of data has garnered particular interest in analyzing transport systems, with some researchers arguing that the data alone are sufficient enough to render transport models unnecessary. However, this paper takes a contrary position and holds that models and data are not mutually exclusive but rather depend upon each other. Transport models are built upon established families of optimization and simulation approaches, and their development aligns with the scientific principles of operations research, which involves acquiring knowledge to derive modeling hypotheses. We provide an overview of these modeling principles and their application to transport systems, presenting numerous models that vary according to study objectives and corresponding modeling hypotheses. The data required for building, calibrating, and validating selected models are discussed, along with examples of using data analytics techniques to collect and handle the data supplied by ICT applications. The paper concludes with some comments on current and future trends
    • …
    corecore