ABSTRACT
In this paper, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor nodes that may be sufficient to reconstruct data for the entire sensor network. Then, during data gathering only the selected sensors need to be involved in communication. The selected set of sensors must also be connected, since they need to relay data to the data-gathering node. We define the problem of selecting such a set of sensors as the connected correlation-dominating set problem, and formulate it in terms of an appropriately defined correlation structure that captures general data correlations in a sensor network.We develop a set of energy-efficient distributed algorithms and competitive centralized heuristics to select a connected correlation-dominating set of small size. The designed distributed algorithms can be implemented in an asynchronous communication model, and can tolerate message losses. We also design an exponential (but non-exhaustive) centralized approximation algorithm that returns a solution within O(log n) of the optimal size. Based on the approximation algorithm, we design a class of efficient centralized heuristics that are empirically shown to return near-optimal solutions. Simulation results over randomly generated sensor networks with both artificially and naturally generated data sets demonstrate the efficiency of the designed algorithms and the viability of our technique -- even in dynamic conditions.
- K. M. Alzoubi, P.-J. Wan, and O. Frieder. Message-optimal connected dominating sets in mobile ad hoc networks. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2002. Google ScholarDigital Library
- P. Berman and V. Ramaiyer. Improved approximation algorithms for the steiner tree problem. J. Algorithms, 17, 1994. Google ScholarDigital Library
- A. Cerpa and D. Estrin. Ascent: Adaptive self-configuring sensor networks topologies. In Proceedings of the IEEE INFOCOM, 2002.Google ScholarCross Ref
- B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom), 2001. Google ScholarDigital Library
- Y. Chen and A. Liestman. Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2002. Google ScholarDigital Library
- J. Chou, D. Petrovic, and K. Ramchandran. A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks. In Proceedings of the IEEE INFOCOM, 2003.Google ScholarCross Ref
- T. Cormen, C. Lieserson, R. Rivest, and C. Stein. Introduction to Algorithms. McGraw Hill, 2001. Google ScholarDigital Library
- R. Cristescu, B. Beferull-Lozano, and M. Vetterli. On network correlated data gathering. In Proceedings of the IEEE INFOCOM, 2004.Google ScholarCross Ref
- R. Cristescu and M. Vetterli. Power efficient gathering of correlated data: optimization, NP-completeness and heuristics. SIGMOBILE Mob. Comput. Commun. Rev., 7(3):31--32, 2003. Google ScholarDigital Library
- D. Culler et al. TinyOS. http://www.tinyos.net, 2004.Google Scholar
- B. Das, R. Sivakumar, and V. Bhargavan. Routing in ad hoc networks using a spine. In Proceedings of the Intl. Conf. on Computer Communications and Networks (IC3N), 1997. Google ScholarDigital Library
- B. Deb, S. Bhatnagar, and B. Nath. Multi-resolution state retrieval in sensor networks. In Proceedings of Intl.\ Workshop on Sensor Network Protocols and Applications, 2003.Google ScholarCross Ref
- D. Dubhashi, A. Mei, A. Panconesi, J. Radhakrishnan, and A. Srinivasan. Fast distributed algorithms for (weakly) connected dominating sets and linear-size skeletons. In Proceedings of the ACM Symposium on Discrete Algorithms (SODA), 2003. Google ScholarDigital Library
- M. Enachescu, A. Goel, R. Govindan, and R. Motwani. Scale free aggregation in sensor networks. In Proceedings of the First International Workshop on Algorithmic Aspects of Wireless Sensor Networks (Algosensors), 2004.Google ScholarCross Ref
- A. Goel and D. Estrin. Simultaneous optimization for concave costs: single sink aggregation or single source buy-at-bulk. In Proceedings of the ACM Symposium on Discrete Algorithms (SODA), 2003. Google ScholarDigital Library
- S. Guha and S. Khuller. Approximation algorithms for connected dominating sets. Algorithmica, 20(4), 1998. Google ScholarDigital Library
- H. Gupta. Selection of views to materialize in a data warehouse. In Proceedings of the International Conference on Database Theory, 1997. Google ScholarDigital Library
- H. Gupta. Selection and Maintenance of Materialized Views in a Data Warehouse. PhD thesis, Stanford University, 1999. Google ScholarDigital Library
- J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. E. Culler, and K. S. J. Pister. System architecture directions for networked sensors. In Architectural Support for Programming Languages and Operating Systems, pages 93--104, 2000. Google ScholarDigital Library
- S. M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory, Vol. II. Prentice-Hall, 1998.Google Scholar
- A. Laouiti, A. Qayyum, and L. Viennot. Multipoint relaying: An efficient technique for flooding in mobile wireless networks. In Proc. of the Hawaii Intl. Conf. on System Sciences, 2002.Google Scholar
- D. Marco, E. Duarte-Melo, M. Liu, and D. L. Neuhoff. On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data. In Proceedings of the International Workshop on Information Processing in Sensor Networks (IPSN), 2003. Google ScholarDigital Library
- National Climatic Data Center. www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html.Google Scholar
- S. Pattem, B. Krishnamachari, and R. Govindan. The impact of spatial correlation on routing with compression in wireless sensor networks. In Proceedings of the International Workshop on Information Processing in Sensor Networks (IPSN), 2004. Google ScholarDigital Library
- A. Scaglione and S. D. Servetto. On the interdependence of routing and data compression in multi-hop sensor networks. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom), 2002. Google ScholarDigital Library
- V. Shnayder, M. Hempstead, B. Chen, G. W. Allen, and M. Welsh. Simulating the power consumption of large-scale sensor network applications. In Proceedings of the International Conference on Embedded Networked Sensor Systems (SenSys), 2004. Google ScholarDigital Library
- P. von Rickenbach and R. Wattenhofer. Gathering correlated data in sensor networks. In Proceedings of the joint workshop on Foundations of mobile computing (DIALM-POMC), 2004. Google ScholarDigital Library
- J. Wu and F. Dai. Broadcasting in ad hoc networks based on self-pruning. In Proceedings of the IEEE INFOCOM, 2003.Google ScholarCross Ref
- J. Wu and H. Li. A dominating-set-based routing scheme in ad hoc wireless networks. Telecommunication Systems Journal, 3, 2001.Google Scholar
- Y. Xu, J. S. Heidemann, and D. Estrin. Geography-informed energy conservation for ad hoc routing. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom), 2001. Google ScholarDigital Library
- F. Ye, G. Zhong, J. Cheng, S. Lu, and L. Zhang. PEAS: A robust energy conserving protocol for long-lived sensor networks. In Proceedings of the International Conference on Distributed Computing Systems, 2003. Google ScholarDigital Library
- S. Yoon and C. Shahabi. Exploiting spatial correlation towards an energy efficient clustered aggregation technique (cag). In Proceedings of the International Conference on Communications (ICC), 2005.Google Scholar
Index Terms
- Efficient gathering of correlated data in sensor networks
Recommendations
Efficient gathering of correlated data in sensor networks
In this article, we design techniques that exploit data correlations in sensor data to minimize communication costs (and hence, energy costs) incurred during data gathering in a sensor network. Our proposed approach is to select a small subset of sensor ...
A distributed clustering method for energy-efficient data gathering in sensor networks
Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable in prolonging the lifetime of a sensor network as long as possible. In this paper, we propose a novel clustering method where energy-...
Active node determination for correlated data gathering in wireless sensor networks
In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. Given that the sensor nodes that are selected as active form a ...
Comments