Resource-aware State Estimation in Visual Sensor Networks with Dynamic Clustering

Melanie Schranz, Bernhard Rinner

Abstract

Generally, resource-awareness plays a key role in wireless sensor networks due the limited capabilities in processing, storage and communication. In this paper we present a resource-aware cooperative state estimation facilitated by a dynamic cluster-based protocol in a visual sensor network (VSN). The VSN consists of smart cameras, which process and analyze the captured data locally. We apply a state estimation algorithm to improve the tracking results of the cameras. To design a lightweight protocol, the final aggregation of the observations and state estimation are only performed by the cluster head. Our protocol is based on a marketbased approach in which the cluster head is elected based on the available resources and a visibility parameter of the object gained by the cluster members. We show in simulations that our approach reduces the costs for state estimation and communication as compared to a fully distributed approach. As resource-awareness is the focus of the cluster-based protocol we can accept a slight degradation of the accuracy on the object’s state estimation by a standard deviation of about 1.48 length units to the available ground truth.

References

  1. 7 Bhuvana, V., Schranz, M., Huemer, M., and Rinner, B. (2013). Distributed object tracking based on cubature kalman filter. In Signals, Systems and Computers, 2013 Asilomar Conference on, pages 423-427.
  2. Chaurasiya, S. K., Pal, T., and Bit, S. D. (2011). An enhanced energy-efficient protocol with static clustering for wsn. In Proceedings of the International Conference on Information Networking, pages 58-63.
  3. Chen, C.-H., Yao, Y., Page, D., Abidi, B., Koschan, A., and Abidi, M. (2008). Camera handoff with adaptive resource management for multi-camera multi-target surveillance. In Fifth IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 79-86.
  4. Dieber, B., Micheloni, C., and Rinner, B. (2011). Resourceaware coverage and task assignment in visual sensor networks. IEEE Transactions on Circuit and Systems for Video Technology, 21:1424 - 1437.
  5. Ding, C., Song, B., Morye, A., Farrell, J., and RoyChowdhury, A. (2012). Collaborative sensing in a distributed ptz camera network. IEEE Transactions on Image Processing, 21(7):3282-95.
  6. Esterle, L., Lewis, P. R., Yao, X., and Rinner, B. (2014). Socio-economic vision graph generation and handover in distributed smart camera networks. Transactions on Sensor Networks, 10(2):20:1-20:24.
  7. Hooshmand, M., Soroushmehr, S. M. R., Khadivi, P., Samavi, S., and Shirani, S. (2013). Visual sensor network lifetime maximization by prioritized scheduling of nodes. Journal of Network and Computer Applications, 36:409-419.
  8. Mallett, J. (2006). The Role of Groups in Smart Camera Networks. PhD thesis, Massachusetts Institute of Technology.
  9. Medeiros, H., Park, J., and Kak, A. (2008). Distributed object tracking using a cluster-based kalman filter in wireless camera networks. IEEE Journal of Selected Topics in Signal Processing, 2(4):448-463.
  10. Monari, E. and Kroschel, K. (2010). Task-oriented object tracking in large distributed camera networks. In IEEE Seventh International Conference on Advanced Video and Signal Based Surveillance, pages 40-47.
  11. Olfati-Saber, R. and Sandell, N. (2008). Distributed tracking in sensor networks with limited sensing range. In Proceedings of the American Control Conference, pages 3157-3162. IEEE.
  12. Qureshi, F. and Terzopoulos, D. (2008). Smart camera networks in virtual reality. Proceedings of the IEEE, 96(10):1640-1656.
  13. Ren, W. and Beard, R. (2005). Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 50(5):655-661.
  14. SanMiguel, J. and Cavallaro, A. (2014). Cost-aware coalitions for collaborative tracking in resourceconstrained camera networks. IEEE Sensors Journal, PP(99):12.
  15. Schranz, M. and Rinner, B. (2014). Demo: VSNsim - a simulator for control and coordination in visual sensor networks. In Eight ACM/IEEE International Conference on Distributed Smart Cameras, page 3.
  16. Song, B., Ding, C., Kamal, A. T., Farrell, J. A., and RoyChowdhury, A. K. (2011). Distributed camera networks. IEEE Signal Processing Magazine, 28(3):20- 31.
  17. Soro, S. and Heinzelman, W. (2009). A survey of visual sensor networks. Advances in Multimedia, 2009:21.
  18. Soto, C. and Roy-Chowdhury, A. (2009). Distributed multitarget tracking in a self-configuring camera network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1486-1493.
  19. Taj, M. and Cavallaro, A. (2011). Distributed and decentralized multi-camera tracking : a survey. IEEE Signal Processing Magazine, 28(3):46-58.
  20. Torshizi, E. S. and Ghahremanlu, E. S. (2013). Energy efficient sensor selection in visual sensor networks based on multi-objective optimization. International Journal on Computational Sciences and Applications, 3:37-46.
  21. Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed tenders. The Journal of finance, 16(1):8-37.
  22. Younis, O. and Fahmy, S. (2004). Heed: A hybrid, energyefficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4):366-379.
  23. Zahmati, A. S., Abolhassani, B., Asghar, A. B. S., and Bakhtiari, A. S. (2007). An energy-efficient protocol with static clustering for wireless sensor networks. International Journal of Electronics, Circuits and Systems, 1(2):135-138.
Download


Paper Citation


in Harvard Style

Schranz M. and Rinner B. (2015). Resource-aware State Estimation in Visual Sensor Networks with Dynamic Clustering . In Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-086-4, pages 15-24. DOI: 10.5220/0005239200150024


in Bibtex Style

@conference{sensornets15,
author={Melanie Schranz and Bernhard Rinner},
title={Resource-aware State Estimation in Visual Sensor Networks with Dynamic Clustering},
booktitle={Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2015},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005239200150024},
isbn={978-989-758-086-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Resource-aware State Estimation in Visual Sensor Networks with Dynamic Clustering
SN - 978-989-758-086-4
AU - Schranz M.
AU - Rinner B.
PY - 2015
SP - 15
EP - 24
DO - 10.5220/0005239200150024