Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/1460412.1460428acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Distributed image search in camera sensor networks

Published: 05 November 2008 Publication History

Abstract

Recent advances in sensor networks permit the use of a large number of relatively inexpensive distributed computational nodes with camera sensors linked in a network and possibly linked to one or more central servers. We argue that the full potential of such a distributed system can be realized if it is designed as a distributed search engine where images from different sensors can be captured, stored, searched and queried. However, unlike traditional image search engines that are focused on resource-rich situations, the resource limitations of camera sensor networks in terms of energy, bandwidth, computational power, and memory capacity present significant challenges. In this paper, we describe the design and implementation of a distributed search system over a camera sensor network where each node is a search engine that senses, stores and searches information. Our work involves innovation at many levels including local storage, local search, and distributed search, all of which are designed to be efficient under the resource constraints of sensor networks. We present an implementation of the search engine on a network of iMote2 sensor nodes equipped with low-power cameras and extended flash storage. We evaluate our system for a dataset comprising book images, and demonstrate more than two orders of magnitude reduction in the amount of data communicated and up to 5x reduction in overall energy consumption over alternate techniques.

References

[1]
Enalab imote2 camera. http://enaweb.eng.yale.edu/drupal/. 2007.
[2]
H. Aghajan, J. Augusto, C. Wu, P. McCullagh, and J. Walkden. Distributed vision-based accident management for assisted living. In ICOST, pages 196--205, 2007.
[3]
R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
[4]
H. Dai, M. Neufeld, and R. Han. ELF: an efficient log-structured ash file system for micro sensor nodes. In ACM SenSys '04, pages 176--187, 2004.
[5]
A. Deshpande, C. Guestrin, S. Madden, J. M. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, pages 588--599, 2004.
[6]
S. Feng, R. Manmatha, and V. Lavrenko. Multiple Bernoulli relevance models for image and video annotation. In IEEE CVPR, pages 1002--1009, 2004.
[7]
S. Gaonkar, J. Li, R. R. Choudhury, L. Cox, and A. Schmidt. Micro-blog: Sharing and querying content through mobile phones and social participation. In ACM MobiSys, pages 174--186, 2008.
[8]
R. Goshorn, J. Goshorn, D. Goshorn, and H. Aghajan. Architecture for cluster-based automated surveillance network for detecting and tracking multiple persons. In ICDSC, 2007.
[9]
J. Hellerstein, W. Hong, S. Madden, and K. Stanek. Beyond average: Towards sophisticated sensing with queries. In IPSN'03, pages 63--79, 2003.
[10]
S. Hengstler, D. Prashanth, S. Fong, and H. Aghajan. Mesheye: A hybrid-resolution smart camera mote for applications in distributed intelligent surveillance. In IPSN-SPOTS, pages 360--369, 2007.
[11]
J. W. Hui and D. Culler. The dynamic behavior of a data dissemination protocol for network programming at scale. In ACM SenSys, pages 81--94, 2004.
[12]
http://www.intel.com/research/exploratory/motes.htm. Intel imote2.
[13]
A. Kansal, M. Goraczko, and F. Zhao. Building a sensor network of mobile phones. In IPSN, pages 547--548, 2007.
[14]
T. Ko, Z. M. Charbiwala, S. Ahmadian, M. Rahimi, M. B. Srivastava, S. Soatto, and D. Estrin. Exploring tradeoffs in accuracy, energy and latency of scale invariant feature transform in wireless camera networks. In ICDSC, 2007.
[15]
http://people.csail.mit.edu/kkl/libpmk/. LIBPMK: A Pyramid Match Toolkit.
[16]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. In in International Journal of Computer Vision, 60, 2004, pages 91--110, 2004.
[17]
D. Lymberopoulos, A. S. Ogale, A. Savvides, and Y. Aloimonos. A sensory grammar for inferring behaviors in sensor networks. In IPSN, pages 251--259, 2006.
[18]
M. Rahimi and R. Baer and O. I. Iroezi and J. C. Garcia and J. Warrior and D. Estrin and M. Srivastava. Cyclops: In situ Image Sensing and Interpretation in Wireless Sensor Networks. In ACM Sensys, pages 192--204, 2005.
[19]
S. Madden, M. Franklin, J. Hellerstein, and W. Hong. TAG: a tiny aggregation service for ad-hoc sensor networks. In OSDI, Boston, MA, 2002.
[20]
G. Mathur, P. Desnoyers, D. Ganesan, and P. Shenoy. Capsule: An energy-optimized object storage system for memory-constrained sensor devices. In SenSys, pages 195--208, 2006.
[21]
G. Mathur, P. Desnoyers, D. Ganesan, and P. Shenoy. Ultra-low power data storage for sensor networks. In IPSN-SPOTS, pages 374--381, 2006.
[22]
K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE PAMI, 27(10):1615--1630, October 2005.
[23]
S. Nath and A. Kansal. Flashdb: dynamic self-tuning database for nand ash. In IPSN, pages 410--419, 2007.
[24]
D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, pages 2161--2168, 2006.
[25]
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007.
[26]
S. Reddy, A. Parker, J. Hyman, J. Burke, D. Estrin, and M. Hansen. Image browsing, processing, and clustering for participatory sensing: Lessons from a dietsense prototype. In EmNets, 2007.
[27]
http://vision.ucla.edu/~vedaldi/code/siftpp/siftpp.html. SIFT++: A lightweight C++ implementation of SIFT.
[28]
A. Silberstein, R. Braynard, C. Ellis, K. Munagala, and J. Yang. A sampling-based approach to optimizing top-k queries in sensor networks. In ICDE, page 68, 2006.
[29]
A. Silberstein, K. Munagala, and J. Yang. Energy-efficient monitoring of extreme values in sensor networks. In ACM SIGMOD, pages 157--168, 2006.
[30]
J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, pages 1470--1477, 2003.
[31]
C. C. Tan, B. Sheng, H. Wang, and Q. Li. Microsearch: When search engines meet small devices. In Pervasive, pages 93--110, 2008.
[32]
H. Wang, C. C. Tan, and Q. Li. Snoogle: A search engine for physical world. In IEEE Infocom, 2008.
[33]
D. Zeinalipour-Yazti, S. Lin, V. Kalogeraki, D. Gunopulos, and W. Najjar. MicroHash: An efficient index structure for ash-based sensor devices. In USENIX FAST, 2005.

Cited By

View all
  • (2022)A Relational Retrieval Model for Industrial Production Systems2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT)10.1109/ICEEMT56362.2022.9862651(431-437)Online publication date: 1-Jul-2022
  • (2021)Event-B HybridationACM Transactions on Embedded Computing Systems10.1145/344827020:4(1-37)Online publication date: 13-May-2021
  • (2021)Toward a Lingua Franca for Deterministic Concurrent SystemsACM Transactions on Embedded Computing Systems10.1145/344812820:4(1-27)Online publication date: 18-May-2021
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems
November 2008
468 pages
ISBN:9781595939906
DOI:10.1145/1460412
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. camera sensor networks
  2. distributed search
  3. visterms
  4. vocabulary tree

Qualifiers

  • Research-article

Conference

Acceptance Rates

Overall Acceptance Rate 174 of 867 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)11
  • Downloads (Last 6 weeks)1
Reflects downloads up to 13 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2022)A Relational Retrieval Model for Industrial Production Systems2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT)10.1109/ICEEMT56362.2022.9862651(431-437)Online publication date: 1-Jul-2022
  • (2021)Event-B HybridationACM Transactions on Embedded Computing Systems10.1145/344827020:4(1-37)Online publication date: 13-May-2021
  • (2021)Toward a Lingua Franca for Deterministic Concurrent SystemsACM Transactions on Embedded Computing Systems10.1145/344812820:4(1-27)Online publication date: 18-May-2021
  • (2021)Efficient External Sorting for Memory-Constrained Embedded Devices with Flash MemoryACM Transactions on Embedded Computing Systems10.1145/344697620:4(1-21)Online publication date: 26-Mar-2021
  • (2019)Discovery of Multimodal Sensor Data Through Webpage ExplorationIEEE Internet of Things Journal10.1109/JIOT.2019.28996126:3(5232-5245)Online publication date: Jun-2019
  • (2019)Capsule network‐based data pruning in wireless sensor networksInternational Journal of Communication Systems10.1002/dac.414533:5Online publication date: 18-Aug-2019
  • (2017)Searching the Web of ThingsACM Computing Surveys10.1145/309269550:4(1-34)Online publication date: 25-Aug-2017
  • (2017)Efficient Decentralized Visual Place Recognition Using a Distributed Inverted IndexIEEE Robotics and Automation Letters10.1109/LRA.2017.26501532:2(640-647)Online publication date: Apr-2017
  • (2017)Efficient object-based surveillance image search using spatial pooling of convolutional featuresJournal of Visual Communication and Image Representation10.1016/j.jvcir.2017.02.01045:C(62-76)Online publication date: 1-May-2017
  • (2017)Supporting secure keyword search in the personal cloudInformation Systems10.1016/j.is.2017.09.00372:C(1-26)Online publication date: 1-Dec-2017
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media