Abstract
Although there exists a large body of work on efficient data collection in sensor networks, the vast majority of proposed techniques have not been implemented on real networks or thoroughly studied on real data. As algorithm performance is highly dependent on the characteristics of the data being reported, it is very difficult to make suggestions as to the relative performance of any particular method. In this work we seek to compare and evaluate existing approaches to efficient data gathering in the specific context of environmental monitoring.We examine a choice algorithm that has not, to the best of our knowledge, been thoroughly studied on real data. We detail a number of algorithmic modifications necessary to bring it from theory to reality, and study the algorithm’s performance in simulation using extensive traces from real world sensor network deployments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arici, T., Altunbasak, Y.: Adaptive sensing for environment monitoring using wireless sensor networks. In: IEEE Wireless Communications & Networking Conf., Atlanta, GA, USA, pp. 2347–2352 (2004)
Arora, A., Dutta, P., Bapat, S., Kulathumani, V., Zhang, H., Naik, V., Mittal, V., Cao, H., Demirbas, M., Gouda, M.: A line in the sand: A wireless sensor network for target detection, classification, and tracking. Computer Networks 46(5), 605–634 (2004)
Baron, D., Wakin, M., Duarte, M., Sarvotham, S., Baraniuk, R.: Distributed compressed sensing (2005) (Preprint)
Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M.: The hitchhiker’s guide to successful wireless sensor network deployments. In: ACM Conf. on Embedded Networked Sensor Systems, Raleigh, NC, USA, pp. 43–56 (2008)
Center for Embedded Networked Sensing: SensorBase data repository, http://www.sensorbase.org/
Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: Int. Conf. on Data Engineering, Atlanta, GA, USA, pp. 48–48 (2006)
Gui, C., Mohapatra, P.: Power conservation and quality of surveillance in target tracking sensor networks. In: ACM Int. Conf. on Mobile Computing and Networking, Philadelphia, PA, USA, pp. 129–143 (2004)
Haupt, J., Nowak, R.: Signal reconstruction from noisy random projections. IEEE Transactions on Information Theory 52(9), 4036–4048 (2006)
Madden, S., Franklin, M., Hellerstein, J.: TinyDB: an acquisitional query processing system for sensor networks. ACM Transactions on Database Systems 30(1), 122–173 (2005)
Milenkovic, A., Otto, C., Jovanov, E.: Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer Communications 29(13-14), 2521–2533 (2006)
Otto, C., Milenkovic, A., Sanders, C., Jovanov, E.: System architecture of a wireless body area sensor network for ubiquitous health monitoring. Journal of Mobile Multimedia 1(4), 307–326 (2006)
PermaSense: PermaSense data frontend, http://data.permasense.ch/
Prorok, A., Cianci, C., Martinoli, A.: Towards optimally efficient field estimation with threshold-based pruning in real robotic sensor networks. In: IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, USA, pp. 5453–5459 (2010)
Sadagopan, N., Krishnamachari, B., Helmy, A.: Active query forwarding in sensor networks. Ad Hoc Networks 3(1), 91–113 (2005)
Madden, S.: Intel Berkeley Lab data, http://db.csail.mit.edu/labdata/labdata.html
Sensirion: Sht75 digital humidity sensor data sheet (2010), http://www.sensirion.com/
Sensorscope Sàrl: Climaps weather monitoring system, http://sensorscope.epfl.ch/climaps/
ShockFish: TinyNode 584 user’s manual (2010), http://tinynode.com/
Silberstein, A., Braynard, R., Yang, J.: Constraint chaining: On energy-efficient continuous monitoring in sensor networks. In: ACM SIGMOD Int. Conf. on Management of Data, Chicago, IL, USA, pp. 157–168 (2006)
Tulone, D., Madden, S.: An energy-efficient querying framework in sensor networks for detecting node similarities. In: ACM Int. Symp. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Torremolinos, Malaga, Spain, pp. 191–300 (2006)
Tulone, D., Madden, S.: PAQ: Time series forecasting for approximate query answering in sensor networks. In: European Conf. on Wireless Sensor Networks, Zurich, Switzerland, pp. 21–37 (2006)
Willett, R., Martin, A., Nowak, R.: Backcasting: adaptive sampling for sensor networks. In: ACM Information Processing in Sensor Networks, Berkeley, CA, USA, pp. 124–133 (2004)
Yang, X., Lim, H., Özsu, T., Tan, K.: In-network execution of monitoring queries in sensor networks. In: ACM SIGMOD Int. Conf. on Management of Data, Beijing, China, pp. 95–105 (2007)
Zytemp: Tn9 infrared thermometer user manual (2010), http://www.zytemp.com/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Evans, W.C., Bahr, A., Martinoli, A. (2013). Evaluating Efficient Data Collection Algorithms for Environmental Sensor Networks. In: Martinoli, A., et al. Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32723-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-32723-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32722-3
Online ISBN: 978-3-642-32723-0
eBook Packages: EngineeringEngineering (R0)