A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Proceedings of the 3rd international conference on Embedded networked sensor systems
Efficient In-Network Moving Object Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
CarWeb: A Traffic Data Collection Platform
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Tru-Alarm: Trustworthiness Analysis of Sensor Networks in Cyber-Physical Systems
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
MoveMine: Mining moving object data for discovery of animal movement patterns
ACM Transactions on Intelligent Systems and Technology (TIST)
Computing with Spatial Trajectories
Computing with Spatial Trajectories
IEEE Transactions on Signal Processing
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A Cyber-Physical System (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. The CPS has wide applications in scenarios such as environment monitoring, battlefield surveillance and traffic control. One key research problem of CPS is called "mining lines in the sand". With a large number of sensors (sand) deployed in a designated area, the CPS is required to discover all the trajectories (lines) of passing intruders in real time. There are two crucial challenges that need to be addressed: (1) the collected sensor data are not trustworthy; (2) the intruders do not send out any identification information. The system needs to distinguish multiple intruders and track their movements. In this study, we propose a method called LiSM (Line-in-the-Sand Miner) to discover trajectories from untrustworthy sensor data. LiSM constructs a watching network from sensor data and computes the locations of intruder appearances based on the link information of the network. The system retrieves a cone-model from the historical trajectories and tracks multiple intruders based on this model. Finally the system validates the mining results and updates the sensor's reliability in a feedback process. Extensive experiments on big datasets demonstrate the feasibility and applicability of the proposed methods.