DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments

Abstract

In this paper, we have proposed and designed DPHK (data prediction based on HMM according to activity pattern knowledge mined from trajectories), a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowledge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM (hidden Markov model) algorithm to predict the sensor node locations people will arrive at according to the received message. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows: (a) the procedure of DPHK is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.

Keywords

trajectory prediction / sensor data mining / wireless sensor networks / smart environments / hidden Markov model

References

[1] Guo S, Yang Y. A distributed optimal framework for mobile data gathering with concurrent data uploading in wireless sensor networks. In: Proceedings of IEEE INFOCOM. 2012, 1305–1313

[2] Seino W, Yoshihisa T, Hara T, Nishio S. A sensor data collection method with a mobile sink for communication traffic reduction by delivering predicted values. In: Proceedings of the 26th IEEE International Conference on Advanced Information Networking and Applications Workshops. 2012, 613–618

[3] Kulik L, Tanin E, Umer M. Efficient data collection and selective queries in sensor networks. Lecture Notes in Computer Science, 2008, 4540: 25–44

[4] Durmaz Incel O, Ghosh A, Krishnamachari B, Chintalapudi K. Fast data collection in tree-based wireless sensor networks. IEEE Transactions on Mobile Computing, 2012, 11(1): 86–99

[5] Cheng B, Xu Z, Chen C, Guan X. Spatial correlated data collection in wireless sensor networks with multiple sinks. In: Proceedings of 2011 IEEE Conference on Computer Communications Workshops. 2011, 578–583

[6] Wang C, De D, Song W. Trajectory mining from anonymous binary motion sensors in smart environment. Knowledge-Based Systems, 2013, 37: 346–356

[7] Hasan M, Rubaiyeat H, Lee Y, Lee S. Mapping of activity recognition as a distributed inference problem in sensor network. In: Proceedings of the 2008 International Conference on Artificial Intelligence. 2008, 280–285

[8] Yu G, Yuan J, Liu Z. Predicting human activities using spatiotemporal structure of interest points. In: Proceedings of the 20th ACM International Conference on Multimedi. 2012, 1049–1052

[9] Tastan B, Sukthankar G. Leveraging human behavior models to predict paths in indoor environments. Pervasive and Mobile Computing, 2011, 7(3): 319–330

[10] Li W, Zhang X, Yang Y, Cai S, Luo Q. Efficient data fusion for wireless sensor networks. In: Proceedings of 2011 IEEE International Workshop on Open-Source Software for Scientific Computation. 2011, 43–46

[11] Tsitsipis D, Dima S, Kritikakou A, Panagiotou C, Koubias S. Data merge: a data aggregation technique for wireless sensor networks. In: Proceedings of IEEE International Conference on Emerging Technologies and Factory Automation. 2011, 1–4

[12] Silberstein A, Braynard R, Yang J. Constraint chaining: On energyefficient continuous monitoring in sensor networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2006, 157–168

[13] Marin-Perianu M, Lombriser C, Amft O, Havinga P, Troster G. Distributed activity recognition with fuzzy-enabled wireless sensor networks. Lecture Notes in Computer Science, 2008, 5067: 296–313

[14] Amft O, Lombriser C, Stiefmeier T, Troster G. Recognition of user activity sequences using distributed event detection. Lecture Notes in Computer Science. 2007, 4793: 124–141

[15] Koodziej J, Xhafa F. Utilization of markov model and nonparametric belief propagation for activity-based indoor mobility prediction in wireless networks. In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems. 2011, 513–518

[16] De D, Song W, Xu M, Wang C, Cook D, Huo X. Findinghumo: realtime tracking of motion trajectories from anonymous binary sensing in smart environments. In: Proceedings of International Conference on Distributed Computing Systems. 2012, 163–172

[17] Lu G, De D, Xu M, Song W, Cao J. Telosw: enabling ultra-low power wake-on sensor network. In: Proceedings of the 7th International Conference on Networked Sensing Systems. 2010, 211–218

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2015 Higher Education Press and Springer-Verlag Berlin Heidelberg