Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers

Research Article

Mobile Sensing Enabled Robust Detection of Security Threats in Urban Environments

Download
427 downloads
  • @INPROCEEDINGS{10.1007/978-3-642-29222-4_7,
        author={Jie Yang and Jerry Cheng and Yingying Chen},
        title={Mobile Sensing Enabled Robust Detection of Security Threats in Urban Environments},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Networks. 7th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2010, and Dedicated Short Range Communications Workshop, DSRC 2010, Houston, TX, USA, November 17-19, 2010, Revised Selected Papers},
        proceedings_a={QSHINE},
        year={2012},
        month={10},
        keywords={Mobile sensing security threats Mean Shift Clustering},
        doi={10.1007/978-3-642-29222-4_7}
    }
    
  • Jie Yang
    Jerry Cheng
    Yingying Chen
    Year: 2012
    Mobile Sensing Enabled Robust Detection of Security Threats in Urban Environments
    QSHINE
    Springer
    DOI: 10.1007/978-3-642-29222-4_7
Jie Yang1,*, Jerry Cheng2,*, Yingying Chen1,*
  • 1: Stevens Institute of Technology
  • 2: Robert Wood Johnson Medical School, UMDNJ
*Contact email: jyang@stevens.edu, jcheng@stat.columbia.edu, yingying.chen@stevens.edu

Abstract

Mobile sensing enables data collection from large numbers of participants in ways that previously were not possible. In particular, by affixing a sensory device to a mobile device, such as smartphone or vehicle, mobile sensing provides the opportunity to not only collect dynamic information from environments but also detect the environmental hazards. In this paper, we propose a mobile sensing wireless network for surveillance of security threats in urban environments, e.g., environmental pollution sources or nuclear radiation materials. We formulate the security threats detection as a significant cluster detection problem. To make our approach robust to unreliable sensing data, we propose an algorithm based on the Mean Shift method to identify the significant clusters and determine the locations of threats. Extensive simulation studies are conducted to evaluate the effectiveness of the proposed detection algorithm.