Data Gathering Protocols for Wireless Sensor Networks

Sensor network applications are often data centric and involve collecting data from a set of sensor nodes to be delivered to various consumers. Typically, nodes in a sensor network are resource-constrained, and hence the algorithms operating in these networks must be efficient. There may be several algorithms available implementing the same service, and efficient considerations may require a sensor application to choose the best suited algorithm. In this paper, we present a systematic evaluation of a set of algorithms implementing the data gathering service. We propose a modular infrastructure for implementing such algorithms in TOSSIM with separate configurable modules for various tasks such as interest propagation, data propagation, aggregation, and path maintenance. By appropriately configuring these modules, we propose a number of data gathering algorithms, each of which incorporates a different set of heuristics for optimizing performance. We have performed comprehensive experiments to evaluate the effectiveness of these heuristics, and we present results from our experimentation efforts.

Authors:

• Dhinu Johnson
• Gurdip Singh

Keywords:

• Data Centric Protocols
• Shortest Paths
• Sensor networks
• Message passing systems.

References:

[1] Y. Snakarasubramaniam, I. F. Akayildiz, W. Su, and E. Caryirci. A
survey of sensor networks. IEEE Communications, 40(8):102-114,
August 2002.
[2] Chee-Yee Chong and S.P. Kumar. Sensor networks: evolution, opportunities
and challenges. Proceedings of the IEEE, 91(8):1247-1256, 2003.
[3] B. Krishnamachari, D. Estrin, and S. Wicker. Modeling data-centric
routing in wireless sensor networks. In Proceedings of the IEEE
INFOCOM, 2002.
[4] C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A
scalable and robust communication paradigm for sensor networks. In
Proceedings of the ACM International Conference on Mobile Computing
and Networking, 2000.
[5] P. Levis, N. Lee, M. Welsh, and D. Culler. TOSSIM: Accurate and
scalable simulation of entire TinyOS applications. In Proceedings of
the First ACM Conference on Embedded Networked Sensor Systems,
2003.
[6] R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network flows: Theory,
Algorithms and Applications. Prentice Hall, 1993.
[7] B. Krishnamachari and J. Heidemann. Application-specific modelling of
information routing in sensor networks. In Proceedings of the Workshop
on Multiop Wireless Networks in conjunction with the Itnernational
Performance Computing and Communications Conference, 2004.
[8] J. Heidemann, F. Silva, and D. Estrin. Matching data dissemination
algorithms to application requirements. In Proceedings of the ACM
Conference on Embedded Networked Sensor Systems, 2003.
[9] Siyuan Chen, Yu Wang, Xiang-Yang Li, and Xinghua Shi. Order-optimal
data collection in wireless sensor networks: Delay and capacity. In IEEE
Annual IEEE Conference on Sensor, Mesh and Ad Hoc Communications
and Networks, 2009.
[10] I. Chatzigiannakis, A. Kinalis, and S. Nikoletseas. Sink mobility
protocols for data collection in wireless sensor networks. In Proceedings
of the 4th ACM International Workshop on Mobility Management and
Wireless Access, 2006.
[11] G. Anastasi, M. Conti, and M. Di Francesco. Reliable and energyefficient
data collection in sparse sensor networks with mobile elements.
Performance Evaluation, 66(12):791-810, December 2009.
[12] L. He, J. Pan, and J. Xu. Reducing data collection latency in wireless
sensor networks with mobile elements. In Proceedings of the International
Workshop on Sensor, Actuator and Robot Networks in conjunction
with IEEE INFOCOM, 2011.