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Data fusion improves the coverage of wireless sensor networks

Published: 20 September 2009 Publication History

Abstract

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. In particular, for signal path loss exponent of k (typically between 2.0 and 5.0), rho_f=O(rho_d^(1-1/k)), where rho_f and rho_d are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

References

[1]
DARPA SensIT project. http://www.ece.wisc.edu/~sensit/.
[2]
N. Ahmed, S. S. Kanhere, and S. Jha. Probabilistic coverage in wireless sensor networks. In LCN, 2005.
[3]
N. Bisnik, A. Abouzeid, and V. Isler. Stochastic event capture using mobile sensors subject to a quality metric. In MobiCom, 2006.
[4]
P. Brass. Bounds on coverage and target detection capabilities for models of networks of mobile sensors. ACM Trans. Sen. Netw., 3(2), 2007.
[5]
Z. Chair and P. Varshney. Optimal data fusion in multiple sensor detection systems. IEEE Trans. Aerosp. Electron. Syst., 22(1), 1990.
[6]
W.-P. Chen, J. C. Hou, and L. Sha. Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans. Mobile Comput., 3(3), 2004.
[7]
S. Y. Cheung, S. Coleri, B. Dundar, S. Ganesh, C. W. Tan, and P. Varaiya. A sensor network for traffic monitoring (plenary talk). In IPSN, 2004.
[8]
T. Clouqueur, K. K. Saluja, and P. Ramanathan. Fault tolerance in collaborative sensor networks for target detection. IEEE Trans. Comput., 53(3), 2004.
[9]
D. Davis and C. Davis. Sound System Engineering. Focal Press, 1997.
[10]
M. Duarte and Y. H. Hu. Distance based decision fusion in a distributed wireless sensor network. In IPSN, 2003.
[11]
M. Duarte and Y. H. Hu. Vehicle classification in distributed sensor networks. J. Parallel and Distributed Computing, 64(7), 2004.
[12]
B. P. Flanagan and K. W. Parker. Robust distributed detection using low power acoustic sensors. Technical report, The MITRE Corporation, 2005.
[13]
L. Gu, D. Jia, P. Vicaire, T. Yan, L. Luo, A. Tirumala, Q. Cao, T. He, J. Stankovic, T. Abdelzaher, and H. Bruce. Lightweight detection and classification for wireless sensor networks in realistic environments. In SenSys, 2005.
[14]
C. Gui and P. Mohapatra. Power conservation and quality of surveillance in target tracking sensor networks. In MobiCom, 2004.
[15]
M. Hata. Empirical formula for propagation loss in land mobile radio services. IEEE Trans. Veh. Technol., 29, 1980.
[16]
T. He, S. Krishnamurthy, J. A. Stankovic, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hui, and B. Krogh. Energy-efficient surveillance system using wireless sensor networks. In MobiSys, 2004.
[17]
M. Hefeeda and H. Ahmadi. A probabilistic coverage protocol for wireless sensor networks. In ICNP, 2007.
[18]
S. Kumar, T. H. Lai, and A. Arora. Barrier coverage with wireless sensors. In MobiCom, 2005.
[19]
S. Kumar, T. H. Lai, and J. Balogh. On k-coverage in a mostly sleeping sensor network. In MobiCom, 2004.
[20]
D. Li and Y. H. Hu. Energy based collaborative source localization using acoustic micro-sensor array. EUROSIP J. Applied Signal Processing, (4), 2003.
[21]
D. Li, K. Wong, Y. H. Hu, and A. Sayeed. Detection, classification and tracking of targets in distributed sensor networks. IEEE Signal Process. Mag., 19(2), 2002.
[22]
X. Li, P. Wan, and O. Frieder. Coverage in Wireless Ad Hoc Sensor Networks. IEEE Trans. Comput., 52(6), 2003.
[23]
B. Liu, O. Dousse, J. Wang, and A. Saipulla. Strong barrier coverage of wireless sensor networks. In MobiHoc, 2008.
[24]
B. Liu and D. Towsley. A study on the coverage of large-scale sensor networks. In MASS, 2004.
[25]
A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson. Wireless sensor networks for habitat monitoring. In WSNA, 2002.
[26]
S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. B. Srivastava. Coverage problems in wireless ad-hoc sensor networks. In INFOCOM, 2001.
[27]
S. Meguerdichian, F. Koushanfar, G. Qu, and M. Potkonjak. Exposure in wireless ad-hoc sensor networks. In MobiCom, 2001.
[28]
NIST/SEMATECH. e-Handbook of Statistical Methods.
[29]
R. Niu and P. K. Varshney. Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J. Wireless Communications and Networking, (4), 2005.
[30]
A. Nordio, C. Chiasserini, and E. Viterbo. Quality of field reconstruction in sensor networks. In INFOCOM, 2007.
[31]
A. Nordio, C. Chiasserini, and E. Viterbo. The impact of quasi-equally spaced sensor layouts on field reconstruction. In IPSN, 2007.
[32]
S. Ren, Q. Li, H. Wang, X. Chen, and X. Zhang. Design and analysis of sensing scheduling algorithms under partial coverage for object detection in sensor networks. IEEE Trans. Parallel Distrib. Syst., 18(3), 2007.
[33]
S. Shakkottai, R. Srikant, and N. Shroff. Unreliable sensor grids: coverage, connectivity and diameter. In INFOCOM, 2003.
[34]
X. Sheng and Y. H. Hu. Energy based acoustic source localization. In IPSN, 2003.
[35]
D. Stroock and S. Varadhan. Multidimensional Diffusion Processes. Springer, 1979.
[36]
C. Taylor, A. Rahimi, J. Bachrach, H. Shrobe, and A. Grue. Simultaneous localization, calibration, and tracking in an ad hoc sensor network. In IPSN, 2006.
[37]
P. Varshney. Distributed Detection and Data Fusion. Springer, 1996.
[38]
P.-J. Wan and C.-W. Yi. Coverage by randomly deployed wireless sensor networks. IEEE/ACM Trans. Netw., 14, 2006.
[39]
W. Wang, V. Srinivasan, and K. C. Chua. Trade-offs between mobility and density for coverage in wireless sensor networks. In MobiCom, 2007.
[40]
W. Wang, V. Srinivasan, K.-C. Chua, and B. Wang. Energy-efficient coverage for target detection in wireless sensor networks. In IPSN, 2007.
[41]
G. Xing, X. Wang, Y. Zhang, C. Lu, R. Pless, and C. Gill. Integrated coverage and connectivity configuration for energy conservation in sensor networks. ACM Trans. Sen. Netw., 1(1), 2005.
[42]
T. Yan, T. He, and J. A. Stankovic. Differentiated surveillance for sensor networks. In SenSys, 2003.
[43]
H. Zhang and J. Hou. On deriving the upper bound of alpha-lifetime for large sensor networks. In MobiHoc, 2004.

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cover image ACM Conferences
MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking
September 2009
368 pages
ISBN:9781605587028
DOI:10.1145/1614320
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 September 2009

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Author Tags

  1. coverage
  2. data fusion
  3. performance limits
  4. target detection
  5. wireless sensor network

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  • (2022)Deployment of Unmanned Aerial Vehicles for Anisotropic Monitoring TasksIEEE Transactions on Mobile Computing10.1109/TMC.2020.301279121:2(495-513)Online publication date: 1-Feb-2022
  • (2022)PEACE: Towards Optimizing Monitoring Utility of Unmanned Aerial Vehicles with Adverse Effect Constraints2022 Tenth International Conference on Advanced Cloud and Big Data (CBD)10.1109/CBD58033.2022.00057(13-18)Online publication date: Nov-2022
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