research-article

Cell-based snapshot and continuous data collection in wireless sensor networks

Authors:

Jing (Selena) He,

A. Selcuk Uluagac,

Yingshu LiAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 9, Issue 4

Article No.: 47, Pages 1 - 29

https://doi.org/10.1145/2489253.2489264

Published: 23 July 2013 Publication History

Abstract

Data collection is a common operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. However, most existing works focus on the network capacity of unicast, multicast or/and broadcast. In this article, we study the snapshot/continuous data collection (SDC/CDC) problem under the physical interference model for randomly deployed dense WSNs. For SDC, we propose a Cell-Based Path Scheduling (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is order-optimal. For CDC, a novel Segment-Based Pipeline Scheduling (SBPS) algorithm is proposed which combines the pipeline technique and the compressive data gathering technique. Theoretical analysis shows that SBPS significantly speeds up the CDC process and achieves a high network capacity.

References

[1]

Andrews, M. and Dinitz, M. 2009. Maximizing capacity in arbitrary wireless networks in the SINR model: Complexity and game theory. In Proceedings of the 28th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'09). 1332--1340.

[2]

Bhandari, V. and Vaidya, N. H. 2007. Connectivity and capacity of multi-channel wireless networks with channel switching constraints In Proceedings of the 26th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'07). 785--793.

[3]

Chafekar, D., Levin, D., Kumar, V. S. A., Marathe, M. V., Parthasarathy, S., and Srinivasan, A. 2008. Capacity of asynchronous random-access scheduling in wireless networks. In Proceedings of the 27th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'08). 13--18.

[4]

Cai, Z., Ji, S., He, J. (S.), and Bourgeois, A. G. 2012. Optimal distributed data collection for asynchronous cognitive radio networks. In Proceedings of the 32nd International Conference on Distributed Computing Systems (ICDCS'12). 245--254.

Digital Library

[5]

Chau, C.-K., Chen, M., and Liew, S. C. 2009. Capacity of large-scale CSMA wireless networks. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking (MobiCom'09). 97--108.

Digital Library

[6]

Chen, S., Wang, Y., Li, X.-Y., and Shi, X. 2009. Order-optimal data collection in wireless sensor networks: Delay and capacity. In Proceedings of the 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON'09). 1--9.

Digital Library

[7]

Chen, S., Wang, Y., Li, X.-Y., and Shi, X. 2009. Data collection capacity of random-deployed wireless sensor networks. In Proceedings of the Global Communications Conference (GLOBECOM'09). 1--6.

Digital Library

[8]

Chen, S., Tang, S., Huang, M., and Wang, Y. 2010. Capacity of data collection in arbitrary wireless sensor networks. In Proceedings of the 29th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'10). 356--360.

Digital Library

[9]

Chipara, O., Lu, C., and Stankovic, J. 2006. Dynamic conflict-free query scheduling for wireless sensor networks. In Proceedings of the 14th IEEE International Conference on Network Protocols (ICNP'06). 321--331.

Digital Library

[10]

Dai, H.-N., Ng, K.-W., Wong, R. C.-W., and Wu, M.-Y. 2008. On the capacity of multi-channel wireless networks using directional antennas. In Proceedings of the 27th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'08). 628--636.

[11]

Duarte-Melo, E. J. and Liu, M. 2003. Data-gathering wireless sensor networks: Organization and capacity. Comput. Netw. 43, 4, 519--537.

Digital Library

[12]

Franceschetti, M., Dousse, O., Tse, D. N. C., and Thiran, P. 2007. Closing the gap in the capacity of wireless networks via percolation theory. IEEE Trans. Inf. Theory 53, 3, 1009--1018.

Digital Library

[13]

Gamal, H. E. 2003. On the scaling laws of dense wireless sensor networks. IEEE Trans. Inf. Theory 51, 3, 1229--1234.

Digital Library

[14]

Garetto, M., Giaccone, P., and Leonardi, E. 2007. On the capacity of ad hoc wireless networks under general node mobility. In Proceedings of the 26th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'07). 357--365.

[15]

Goussevskaia, O., Wattenhofer, R., Halldorsson, M. M., and Welzl, E. 2009. Capacity of arbitrary wireless networks. In Proceedings of the 28th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'09). 1872--1880.

[16]

Gupta, P. and Jumar, P. R. 2000. The capacity of wireless networks. IEEE Trans. Inf. Theory 46, 2, 388--404.

Digital Library

[17]

Huang, W., Wang, X., and Zhang, Q. 2010. Capacity scaling in mobile wireless ad hoc network with infrastructure support. In Proceedings of the 30th International Conference on Distributed Computing Systems (ICDCS'10). 848--857.

Digital Library

[18]

Ji, S., Li, Y., and Jia, X., 2011. Capacity of dual-radio multi-channel wireless sensor networks for continuous data collection. In Proceedings of the 30th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'11). 1062--1070.

[19]

Ji, S., Uluagac, A. S., Beyah, R., and Cai, Z. 2012. Practical unicast and convergecast scheduling for cognitive radio networks. J. Combin. Optim. 26, 1, Springer, 161--177.

Digital Library

[20]

Ji, S., Cai, Z., Li, Y., and Jia, X. 2012. Continuous data collection capacity of dual-radio multi-channel wireless sensor networks. IEEE Trans. Parall. Distrib. Syst. 23, 10, 1844--1855.

Digital Library

[21]

Ji, S., Beyah, R., and Cai, Z. 2012. Snapshot/continuous data collection capacity for large-scale probabilistic wireless sensor networks. In Proceedings of the 31st IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'12). 25--30.

[22]

Ji, S. and Cai, Z. 2012. Distributed data collection and its capacity in asynchronous wireless sensor networks. In Proceedings of the 31st IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'12). 2113--2121.

[23]

Keshavarz-Haddad, A., Ribeiro, V., and Riedi, R. 2006. Broadcast capacity in multihop wireless networks. In Proceedings of the 12th Annual International Conference on Mobile Computing and Networking (MobiCom'06). 239--250.

Digital Library

[24]

Kulkarni, S. R. and Viswanath, P. 2004. A deterministic approach to throughput scaling in wireless networks. IEEE Trans. Inf. Theory 50, 6, 1041--1049.

Digital Library

[25]

Kumar, V., S. A., Marathe, M. V., Parthasarathy, S., and Srinivasan, A. 2005. Algorithmic aspects of capacity in wireless networks. In Proceedings of the International Conference on Measurements and Modeling of Computer Systems (SIGMETRICS'05). 133--144.

Digital Library

[26]

Kyasanur, P. and Vaidya, N. H. 2005. Capacity of multi-channel wireless networks: Impact of number of channels and interfaces. In Proceedings of the 11th Annual International Conference on Mobile Computing and Networking (MobiCom'05). 45--57.

Digital Library

[27]

Li, P. and Fang, Y. 2009. Impacts of topology and traffic pattern on capacity of hybrid wireless networks. IEEE Trans. Mobile Comput. 8, 12, 1585--1595

Digital Library

[28]

Li, X.-Y. 2009. Multicast capacity of wireless ad hoc networks. IEEE/ACM Trans. Netw. 17, 3, 950--961.

Digital Library

[29]

Li, X.-Y., Tang, S., and Frieder, O. 2007. Multicast capacity for large scale wireless ad hoc networks. In Proceedings of the 13th Annual International Conference on Mobile Computing and Networking (MobiCom'07). 266--277.

Digital Library

[30]

Li, X.-Y., Zhao, J., Wu, Y. W., Tang, S. J., Xu, X. H., and Mao, X. F. 2008. Broadcast capacity for wireless ad hoc networks. In Proceedings of the 5th International Conference on Mobile Ad-hoc and Sensor Systems (MASS'08). 114--123.

[31]

Li, X.-Y., Xu, X., Wang, S., Tang, S., Dai, G., Zhao, J., and Qi, Y. 2009. Efficient data aggregation in multi-hop wireless sensor networks under physical interference model. In Proceedings of the 6th International Conference on Mobile Ad-hoc and Sensor Systems (MASS'09). 353--362.

[32]

Li, X.-Y., Liu, Y., Li, S., and Tang, S. 2010. Multicast capacity of wireless ad hoc networks under Gaussian channel model. IEEE/ACM Trans. Netw. 18, 4, 1145--1157.

Digital Library

[33]

Liu, B., Towsley, D., and Swami, A. 2008. Data gathering capacity of large scale multihop wireless networks. In Proceedings of the 5th International Conference on Mobile Ad-hoc and Sensor Systems (MASS'08). 124--132.

[34]

Luo, C., Wu, F., Sun, J., and Chen, C. W. 2009. Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking (MobiCom'09). 145--156.

Digital Library

[35]

Mao, X., Li, X.-Y., and Tang, S. 2008. Multicast capacity for hybrid wireless networks. In Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'08). 189--198.

Digital Library

[36]

Marco, D., Duarte-Melo, E. J., Liu, M., and Neuhoff, D. L. 2003. On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data. In Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks (IPSN'03). Lecture Notes in Computer Science, vol. 2634, Spring-Verlag, Berlin Heidelberg, 1--16.

Digital Library

[37]

Moscibroda, T. 2007. The worst-case capacity of wireless sensor networks. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks (IPSN'07). 1--10.

Digital Library

[38]

Niesen, U., Gupta, P., and Shah, D. 2009. On capacity scaling in arbitrary wireless networks. IEEE Trans. Inf. Theory 55, 9, 3959--3982.

Digital Library

[39]

Niesen, U., Gupta, P., and Shah, D. 2010. The balanced unicast and multicast capacity regions of large wireless networks. IEEE Trans. Inf. Theory 56, 5, 2249--2271.

Digital Library

[40]

Sharma, G., Mazumdar, R., and Shroff, N. B. 2007. Delay and capacity trade-offs in mobile ad hoc networks: A global perspective. IEEE/ACM Trans. Netw. 15, 5, 981--992

Digital Library

[41]

Sirkeci-Mergen, B. and Gastpar, M. 2007. On the broadcast capacity of wireless networks. IEEE Trans. Inf. Theory 56, 8, 3847--3861.

Digital Library

[42]

Tang, S., Li, X.-Y., Mao, X., and Wang, C. 2011. Impact of deployment size on the asymptotic capacity for wireless ad hoc networks under Gaussian channel model. Wirel. Netw. 17, 4, 817--832.

Digital Library

[43]

Wan, P.-J., Huang, S. C.-H., Wang, L., Wan, Z., and Jia, X. 2009. Minimum-latency aggregation scheduling in multihop wireless networks. In Proceedings of the 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'09). 185--194.

Digital Library

[44]

Wang, C., Liu, Y., Li, X.-Y., and Tang, S. 2011. Aggregation capacity of wireless sensor networks: Extended network case. In Proceedings of the 30th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'11). 1701--1709.

[45]

Wang, C., Jiang, C., Li, X.-Y., Tang, S., He, Y., Mao, X., and Liu, Y. 2012. Scaling laws of multicast capacity for power-constrained wireless networks under Gaussian channel model. IEEE Trans. Comput. 61, 5, 713--725.

Digital Library

[46]

Wang, X., Bei, Y., Peng, Q., and Fu, L. 2011. Speed improves delay-capacity trade-off in MotionCast. IEEE Trans. Parall. Distrib. Syst. 22, 5, 729--742.

Digital Library

[47]

Wang, Z., Sadjadpour, H. R., and Garcia-Luna-Aceves, J. J. 2008. A unifying perspective on the capacity of wireless ad hoc networks. In Proceedings of the 27th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'08). 211--215.

[48]

Wang, Z., Sadjadpour, H. R., and Garcia-Luna-Aceves, J. J. 2008. The capacity and energy efficiency of wireless ad hoc networks with multi-packet reception. In Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'08). 179--188.

Digital Library

[49]

Xu, Y. and Wang, W. 2009. Scheduling partition for order optimal capacity in large-scale wireless networks. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking (MobiCom'09). 109--120.

Digital Library

[50]

Yan, M., He, J. (S.), Ji, S., and Li, Y. 2011. Minimum latency scheduling for multi-regional query in wireless sensor networks. In Proceedings of the 30th IEEE International Performance Computing and Communications Conference (IPCCC'11). 17--19.

Digital Library

[51]

Zhang, G., Xu, Y., Wang, X., and Guizani, M. 2010. Capacity of hybrid wireless networks with directional antenna and delay constraint. IEEE Trans. Commun. 58, 7, 2097--2106.

Digital Library

[52]

Zhu, X., Tang, B., and Gupta, H. 2005. Delay efficient data gathering in sensor networks. In Proceedings of the 1st International Converence on Mobile Ad-hoc and Sensor Networks (MSN'05). 380--389.

Digital Library

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Index Terms

Cell-based snapshot and continuous data collection in wireless sensor networks
1. Networks
  1. Network protocols

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 9, Issue 4

July 2013

523 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/2489253

Issue’s Table of Contents

Copyright © 2013 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 23 July 2013

Accepted: 01 September 2012

Revised: 01 July 2012

Received: 01 April 2012

Published in TOSN Volume 9, Issue 4

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Kim WKim SSeo DLee I(2021)Broadcast Proxy Reencryption Based on Certificateless Public Key Cryptography for Secure Data SharingWireless Communications & Mobile Computing10.1155/2021/15670192021Online publication date: 1-Jan-2021
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Y R SN C(2020)IoT Streaming Data Outlier Detection and Sensor Data Aggregation2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC49090.2020.9243509(150-155)Online publication date: 7-Oct-2020
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Akber SKhan IMuhammad SMohsin SKhan IShamshirband SChronopoulos ADesai BFlesca SZumpano EMasciari ECaroprese L(2018)Data Volume Based Data Gathering in WSNs using Mobile Data CollectorProceedings of the 22nd International Database Engineering & Applications Symposium10.1145/3216122.3216166(199-207)Online publication date: 18-Jun-2018
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Akber SChen HJin H(2017)Exploring the Efficiency of Data Collection Schemes in Wireless Sensor Networks2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS.2017.00105(773-778)Online publication date: Dec-2017
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