Multiresolution storage and search in sensor networks
Deepak Ganesan, Ben Greenstein, Deborah Estrin, John Heidemann and Ramesh Govindan
USC/Information Sciences Institute
Citation
Deepak Ganesan, Ben Greenstein, Deborah Estrin, John Heidemann and Ramesh Govindan. Multiresolution storage and search in sensor networks. ACM Transactions on Storage. 1, 3 (Aug. 2005), 277–315. [DOI] [PDF] [alt PDF]
Abstract
Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. This article addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multihop transmission of sensor data to Internet gateways which can quickly drain battery-operated nodes.Constructing a storage and search system that satisfies the requirements of data-rich scientific applications is a daunting task for many reasons: (a) the data requirements may be large compared to available storage and communication capacity of resource-constrained nodes, (b) user requirements are diverse and range from identification and collection of interesting event signatures to obtaining a deeper understanding of long-term trends and anomalies in the sensor events, and (c) many applications are in new domains where a priori information may not be available to reduce these requirements.This article describes a lossy, gracefully degrading storage model. We believe that such a model is necessary and sufficient for many scientific applications since it supports both progressive data collection for interesting events as well as long-term in-network storage for in-network querying and processing. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multiresolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.Bibtex Citation
@article{Ganesan05a,
author = {Ganesan, Deepak and Greenstein, Ben and Estrin, Deborah and Heidemann, John and Govindan, Ramesh},
title = {Multiresolution storage and search in sensor networks},
journal = {ACM Transactions on Storage},
year = {2005},
sortdate = {2005-08-01},
project = {ilense, cens, scadds, nocredit},
jsubject = {sensornet_general},
volume = {1},
number = {3},
month = aug,
pages = {277--315},
jlocation = {johnh: pafile xxx},
keywords = {dimensions, multiresolution storage, sensor
network storage},
url = {https://ant.isi.edu/%7ejohnh/PAPERS/Ganesan05a.html},
pdfurl = {https://ant.isi.edu/%7ejohnh/PAPERS/Ganesan05a.pdf},
doi = {http://doi.acm.org/10.1145/1084779.1084780},
myorganization = {USC/Information Sciences Institute},
copyrightholder = {ACM},
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