Optimizing in-network aggregate queries in wireless sensor networks for energy saving
Data & Knowledge Engineering
Retrieving k-nearest neighboring trajectories by a set of point locations
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Security requirements for a cyber physical community system: a case study
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
A novel key management scheme for wireless embedded systems
ACM SIGAPP Applied Computing Review
Trustworthiness analysis of sensor data in cyber-physical systems
Journal of Computer and System Sciences
Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding abnormal data in vehicular cyber physical systems
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
A framework of traveling companion discovery on trajectory data streams
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational)components to form a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios. One key issue in CPS research is trustworthiness analysis of the observed data: Due to technology limitations and environmental influences, the CPS data are inherently noisy that may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this paper, we propose a method called Tru-Alarm which finds out trustworthy alarms and increases the feasibility of CPS. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inferences based on linked information in the graph. Extensive experiments show that Tru-Alarm filters out noises and false information efficiently and guarantees not missing any meaningful alarms.