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ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks
December 2007 (vol. 18 no. 12)
pp. 1766-1783
One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper we develop ASAP − an adaptive sampling approach to energyefficient periodic data collection in sensor networks. The main idea behind ASAP is to use a dynamically changing subset of the nodes as samplers such that the sensor readings of the sampler nodes are directly collected, whereas the values of the non-sampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed. ASAP can be effectively used to increase the network lifetime while keeping the quality of the collected data high, in scenarios where either the spatial density of the network deployment is superfluous relative to the required spatial resolution for data analysis or certain amount of data quality can be traded off in order to decrease the power consumption of the network. ASAP approach consists of three main mechanisms. First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation are used to determine the sampler nodes and to calculate the parameters of the probabilistic models that capture the spatial and temporal correlations among the sensor readings. Last, adaptive data collection and model-based prediction are used to minimize the number of messages used to extract data from the network. A unique feature of ASAP is the use of in-network schemes, as opposed to the protocols requiring centralized control, to select and dynamically refine the subset of the sensor nodes serving as samplers and to adjust the value prediction models used for non-sampler nodes. Such runtime adaptations create a data collection schedule which is self-optimizing in response to the changes in the energy levels of the nodes and environmental dynamics. We present simulation-based experimental results and study the effectiveness of ASAP under different system settings.

[1] D. Estrin, D. Culler, K. Pister, and G. Sukhatme, “Connecting the Physical World with Pervasive Networks,” IEEE Pervasive Computing, vol. 1, no. 1, Jan. 2002.
[2] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless Sensor Networks for Habitat Monitoring,” Proc. First ACM Workshop Wireless Sensor Networks and Applications (WSNA '02), 2002.
[3] M. Batalin, M. Rahimi, Y. Yu, S. Liu, G. Sukhatme, and W. Kaiser, “Call and Response: Experiments in Sampling the Environment,” Proc. Second ACM Int'l Conf. Embedded Networked Sensor Systems (SenSys '04), 2004.
[4] A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong, “Model-Driven Data Acquisition in Sensor Networks,” Proc. 30th Int'l Conf. Very Large Data Bases (VLDB '04), 2004.
[5] Taos Inc., Ambient Light Sensor (ALS), http://www.taosinc.com/images/product/document tsl2550-e58.pdf, Dec. 2004.
[6] D. Estrin, R. Govindan, J.S. Heidemann, and S. Kumar, “Next Century Challenges: Scalable Coordination in Sensor Networks,” Proc. ACM MobiCom, 1999.
[7] D.J. Abadi, S. Madden, and W. Lindner, “REED: Robust, Efficient Filtering and Event Detection in Sensor Networks,” Proc. 31st Int'l Conf. Very Large Data Bases (VLDB '05), 2005.
[8] D. Li, K. Wong, and Y. Hu, A. Sayeed, “Detection, Classification, Tracking of Targets in Micro-Sensor Networks,” IEEE Signal Processing Magazine, Mar. 2002.
[9] J. Liu, J. Reich, P. Cheung, and F. Zhao, “Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications,” Proc. Second IEEE Int'l Workshop Information Processing in Sensor Networks (IPSN '03), 2003.
[10] L. Liu, C. Pu, and W. Tang, “Continual Queries for Internet Scale Event-Driven Information Delivery,” IEEE Trans. Knowledge and Data Eng., vol. 11, no. 4, July-Aug. 1999.
[11] S. Madden, M. Franklin, J. Hellerstein, and W. Hong, “Tag: A Tiny Aggregation Service for Ad Hoc Sensor Networks,” Proc. Fifth Usenix Symp. Operating Systems Design and Implementation (OSDI'02), 2002.
[12] S. Madden, R. Szewczyk, M. Franklin, and D. Culler, “Supporting Aggregate Queries over Ad Hoc Wireless Sensor Networks,” Proc. Fourth IEEE Workshop Mobile Computing Systems and Applications (WMCSA '02), 2002.
[13] D. Chu, A. Deshpande, J.M. Hellerstein, and W. Hong, “Approximate Data Collection in Sensor Networks Using Probabilistic Models,” Proc. 22nd IEEE Int'l Conf. Data Eng. (ICDE '06), 2006.
[14] A. Deshpande, C. Guestrin, and S.R. Madden, “Using Probabilistic Models for Data Management in Acquisitional Environments,” Proc. Second Biennial Conf. Innovative Data Systems Research (CIDR'05), 2005.
[15] A. Deshpande, C. Guestrin, W. Hong, and S. Madden, “Exploiting Correlated Attributes in Acquisitional Query Processing,” Proc. 21st IEEE Int'l Conf. Data Eng. (ICDE '05), 2005.
[16] T. Arici, B. Gedik, Y. Altunbasak, and L. Liu, “PINCO: A Pipelined In-Network Compression Scheme for Data Collection in Wireless Sensor Networks,” Proc. 12th IEEE Int'l Conf. Computer Comm. and Networks (ICCCN '03), 2003.
[17] G. Casella and R.L. Berger, Statistical Inference. Duxbury Press, June 2001.
[18] J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, Aug. 2000.
[19] Moteiv Corp., Telos B Datasheet, http://www.moteiv.com/pr2004-12-09-telosb.php , Dec. 2004.
[20] O. Chipara, C. Lu, and G.-C. Roman, “Efficient Power Management Based on Application Timing Semantics for Wireless Sensor Networks,” Proc. 25th IEEE Int'l Conf. Distributed Computing Systems (ICDCS '05), 2005.
[21] W. Ye, J. Heidemann, and D. Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks,” Proc. IEEE INFOCOM, 2002.
[22] The Network Simulator—ns-2, http://www.isi.edu/nsnamns/, Jan. 2006.
[23] Global Precipitation Climatology Project, http://www.ncdc.noaa. gov/oa/wmowdcamet-ncdc.html , Dec. 2004.
[24] C.I. Ramesh Govindan and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” Proc. ACM MobiCom, 2000.
[25] F. Ye, G. Zhong, S. Lu, and L. Zhang, “Peas: A Robust Energy Conserving Protocol for Long-Lived Sensor Networks,” Proc. 23rd IEEE Int'l Conf. Distributed Computing Systems (ICDCS '03), 2003.
[26] Q. Han, S. Mehrotra, and N. Venkatasubramanian, “Energy Efficient Data Collection in Distributed Sensor Environments,” Proc. 24th IEEE Int'l Conf. Distributed Computing Systems (ICDCS '04), 2004.
[27] Y. Kotidis, “Snapshot Queries: Towards Data-Centric Sensor Networks,” Proc. 21st IEEE Int'l Conf. Data Eng. (ICDE '05), 2005.
[28] M. Paskin and C. Guestrin, “A Robust Architecture for Distributed Inference in Sensor Networks,” Proc. Fourth IEEE Int'l Workshop Information Processing in Sensor Networks (IPSN '05), 2005.
[29] C. Guestrin, R. Thibaux, P. Bodik, M.A. Paskin, and S. Madden, “Distributed Regression: An Efficient Framework for Modeling Sensor Network Data,” Proc. Third IEEE Int'l Workshop Information Processing in Sensor Networks (IPSN '04), 2004.
[30] L. Xiao, S. Boyd, and S. Lall, “A Scheme for Robust Distributed Sensor Fusion Based on Average Consensus,” Proc. Fourth IEEE Int'l Workshop Information Processing in Sensor Networks (IPSN '05), 2005.
[31] V. Byckovskiy, S. Megerian, D. Estrin, and M. Potkonjak, “A Collaborative Approach to In-Place Sensor Calibration,” Proc. Second IEEE Int'l Workshop Information Processing in Sensor Networks (IPSN '03), 2003.

Index Terms:
C.2.7.c Sensor networks, C.2.0.b Data communications, H.2.1.a Data models
Citation:
Bugra Gedik, Ling Liu, Philip S. Yu, "ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 12, pp. 1766-1783, Dec. 2007, doi:10.1109/TPDS.2007.1110
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