Mining GPS Traces for Map Refinement
Data Mining and Knowledge Discovery
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Tracking multiple targets using binary proximity sensors
Proceedings of the 6th international conference on Information processing in sensor networks
A Multiple Pairs Shortest Path Algorithm
Transportation Science
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Data Mining and Knowledge Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Towards cooperative localization of wearable sensors using accelerometers and cameras
INFOCOM'10 Proceedings of the 29th conference on Information communications
Efficient k-nearest neighbor search on moving object trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Mining Discriminative Patterns for Classifying Trajectories on Road Networks
IEEE Transactions on Knowledge and Data Engineering
Analysis of Deterministic Tracking of Multiple Objects Using a Binary Sensor Network
ACM Transactions on Sensor Networks (TOSN)
RASS: A real-time, accurate and scalable system for tracking transceiver-free objects
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
BFPkNN: an efficient k-nearest-neighbor search algorithm for historical moving object trajectories
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
ICDCS '12 Proceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems
ActiSen: Activity-aware sensor network in smart environments
Pervasive and Mobile Computing
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One of the key applications of Smart Environment (which is deployed with anonymous binary motion sensors [1,2]) is user activity behavior analysis. The necessary prerequisite to finding behavior knowledge of users is to mine trajectories from the massive amount of sensor data. However, it becomes more challenging when the Smart Environment has to use only non-invasive and binary sensing because of user privacy protection. Furthermore, the existing trajectory tracking algorithms mainly deal with tracking object either using sophisticated invasive and expensive sensors [3,4], or treating tracking as a Hidden Markov Model (HMM) which needs adequate training data set to obtain model's parameter [5]. So, it is imperative to propose a framework which can distinguish different trajectories only based on collected data from anonymous binary motion sensors. In this paper, we propose a framework - Mining Trajectory from Anonymous Binary Motion Sensor Data (MiningTraMo) - that can mine valuable and trust-worthy motion trajectories from the massive amount of sensor data. The proposed solution makes use of both temporal and spatial information to remove the system noise and ambiguity caused by motion crossover and overlapping. Meanwhile, MiningTraMo introduces Multiple Pairs Best Trajectory Problem (MPBT), which is inspired by the multiple pairs shortest path algorithm in [6], to search the most possible trajectory using walking speed variance when there are several trajectory candidates. The time complexity of the proposed algorithms are analyzed and the accuracy performance is evaluated by some designed experiments which not only have ground truth, but also are the typical situation for real application. The mining experiment using real history data from a smart workspace is also finished to find the user's behavior pattern.