The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
A Survey of Visual Sensor Networks
Open accessAbstract
Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, with unique performance, complexity, and quality of service challenges. Consisting of a large number of low-power camera nodes, visual sensor networks support a great number of novel vision-based applications. The camera nodes provide information from a monitored site, performing distributed and collaborative processing of their collected data. Using multiple cameras in the network provides different views of the scene, which enhances the reliability of the captured events. However, the large amount of image data produced by the cameras combined with the network's resource constraints require exploring new means for data processing, communication, and sensor management. Meeting these challenges of visual sensor networks requires interdisciplinary approaches, utilizing vision processing, communications and networking, and embedded processing. In this paper, we provide an overview of the current state-of-the-art in the field of visual sensor networks, by exploring several relevant research directions. Our goal is to provide a better understanding of current research problems in the different research fields of visual sensor networks, and to show how these different research fields should interact to solve the many challenges of visual sensor networks.
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Cites
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Maintaining Sensing Coverage and Connectivity in Large Sensor Networks
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Compression via channel coding - Distributed source coding for sensor networks
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Cited by
Bridging e-Health and the Internet of Things: The SPHERE Project
Zhu, N., Diethe, T., Camplani, M., Tao, L., Burrows, A., Twomey, N., Kaleshi, D., Mirmehdi, M., Flach, P., & Craddock, I. (2015) · IEEE Intelligent Systems · doi:10.1109/mis.2015.57 · Cited by 242
Metadata
- Authors
- Soro, S., & Heinzelman, W.
- Venue
- Advances in Multimedia
- Publisher
- Wiley
- Published
- 2009
- Volume
- 2009
- Pages
- 1-21
- ISSN
- 1687-5680 (ISSN-L)
- 1687-5699
- OpenAlex
- W2171117352
- Cited by
- 445
- References
- 25
- FWCI
- 53.14
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
