Abstract
To extend the lifetime of wireless sensor networks, reducing and balancing energy consumptions are main concerns in data collection due to the power constrains of the sensor nodes. Unfortunately, existing data collection schemes mainly focus on energy saving while overlook balancing the energy consumption of the sensor nodes. In addition, most of them assume that each sensor has a global knowledge about the network topology. However, in many real applications, such a global knowledge is not desired due to the dynamic features of the wireless sensor network. In this paper, we propose an Approximate Self-Adaptive data collection technique (ASA), to approximately collect data in a distributed wireless sensor network. ASA investigates the spatial correlations between sensors to provide an energy-efficient and balanced route to the sink, while each sensor does not know any global knowledge on the network. Based on our synthetic experiences, we demonstrate that ASA can provide significant communication (and hence energy) savings and equal energy consumption of the sensor nodes.
The work is partially supported by the National Basic Research Program of China (973 Program) (No. 2012CB316201), the National Natural Science Foundation of China (Nos. 61322208, 61272178, 61129002), the Doctoral Fund of Ministry of Education of China (No. 20110042110028), and the National Science Foundation (No. CCF-1441253).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tan, H., Körpeoǧlu, İ.: Power efficient data gathering and aggregation in wireless sensor networks. SIGMOD Record 32(4), 66–71 (2003)
Silberstein, A., Braynard, R., Yang, J.: Constraint chaining: On energy-efficient continuous monitoring in sensor networks. In: SIGMOD (2006)
Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: Balancing energy efficiency and quality of aggreagation data in sensor networks. VLDB.J 13, 384–403 (2004)
Xu, Y., Heidemann, J., Estrin, D.: Geography informed energy conservation for ad hoc routing. In: MobiCom, pp. 70–84 (2001)
Moore, D., Leonard, J., Rus, D., Teller, S.: Robust distributed network localization with noisy range measurements. In: SenSys (2004)
Kempe, D., Kleinberg, J., Demers, A.: Spatial gossip and resource location protocols. In: STOC (2002)
Crossbow Inc. Mpr-mote processor radio board user’s manual
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: a tiny aggregation service for ad-hoc sensor networks. In: OSDI (2002)
Buragohain, C., Agrawal, D., Suri, S.: Power aware routing for sensor databases. In: INFOCOM (2005)
Kotidis, Y.: Snapshot queries: Towards data-centric sensor networks. In: ICDE (2005)
Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., Hong, W.: Model-driven data acquisition in sensor network. In: VLDB (2004)
Jain, A., Chang, E., Wang, Y.: Adaptive stream resource mangement using kalman filters. In: SIGMOD (2004)
Chu, D., Deshpande, A., et al.: Approxmiate data collection in sensor networks using probabilistic models. In: ICDE (2006)
Silberstein, A., Gelfand, A., Munagala, K., Puggioni, G., Yang, J.: Making sense of suppressions and failures in sensor data: A bayesian approach. In: VLDB, pp. 842–853 (2007)
Yang, X., Lim, H., Özsu, M.T., Tan, K.-L.: In-network execution of monitoring queries in sensor networks. In: SIGMOD Conference, pp. 521–532 (2007)
Ahmad, Y., Nath, S.: Colr-tree: Communication-efficient spatio-temporal indexing for a sensor data web portal. In: ICDE, pp. 784–793 (2008)
Li, J., Deshpande, A., Khuller, S.: On computing compression trees for data collection in wireless sensor networks. In: INFOCOM, pp. 2115–2123 (2010)
Gedik, B., Liu, L.: Energy-aware data collection in sensor networks: a localized selective sampling approach. In: IEEE TPDS (2006)
Meka, A., Singh, A.K.: Distributed spatial clustering in sensor networks. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 980–1000. Springer, Heidelberg (2006)
Bhattacharya, A., Meka, A., Singh, A.K.: Mist: Distributed indexing and querying in sensor networks using statistical models. In: VLDB, pp. 854–865 (2007)
Lin, S., Arai, B., Gunopulos, D., Das, G.: Region sampling: Continuous adaptive sampling on sensor networks. In: ICDE, pp. 794–803 (2008)
Li, Z., Liu, Y., Li, M., Wang, J., Cao, Z.: Exploiting ubiquitous data collection for mobile users in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 24(2), 312–326 (2013)
Wang, C., Ma, H.: Data collection in wireless sensor networks by utilizing multiple mobile nodes. Ad Hoc & Sensor Wireless Networks 18(1), 65–85 (2013)
Wang, C., Ma, H., He, Y., Xiong, S.: Adaptive approximate data collection for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(6), 1004–1016 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, B., Yang, X., Zang, W., Yu, M. (2014). Approximate Self-Adaptive Data Collection in Wireless Sensor Networks. In: Cai, Z., Wang, C., Cheng, S., Wang, H., Gao, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2014. Lecture Notes in Computer Science, vol 8491. Springer, Cham. https://doi.org/10.1007/978-3-319-07782-6_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-07782-6_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07781-9
Online ISBN: 978-3-319-07782-6
eBook Packages: Computer ScienceComputer Science (R0)