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A survey of visual sensor network platforms

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Abstract

Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks. Various visual sensor platforms have been developed with the aim of integrating visual data to wireless sensor applications. The objective of this article is to survey current visual sensor platforms according to in-network processing and compression/coding techniques together with their targeted applications. Characteristics of these platforms such as level of integration, data processing hardware, energy dissipation, radios and operating systems are also explored and discussed.

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Notes

  1. The energy cost of transmitting 1 Kb of data to a distance of 100 m, assuming Raleigh fading channel, BPSK modulation, 10−6 BER and fourth power distance loss is 3 Joules, which is approximately the same energy cost as of executing 3 million instructions by a 100 million instructions per second (MIPS)/W processor.

  2. Because of their simplicity, PSNR (Peak Signal-to-Noise-Ratio) and MSE (Mean Square Error) are most widely used models for assessing image/video quality. We note that there are better models available in the literature [50].

  3. There are many other smart camera platforms, which are not specifically designed as standalone VSN platforms (a comprehensive survey of smart camera platforms is presented in [41]).

  4. There are some real-time embedded operating systems that provide a full-compliance POSIX interface (e.g., LynxOS, QNX), the same functionalities as Linux, but with less overhead. However, they were not used in VSN platforms presented in this paper.

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Acknowledgements

This work was partially supported by Spanish projects TIN2010-21378-C02-01 and 2009-SGR-1167. We would like to thank Ugur Cil for his help on figure drawings. We would like to thank the anonymous reviewers for their helpful comments, feedbacks, and suggestions.

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Authors and Affiliations

  1. TOBB University of Economics and Technology, Ankara, Turkey

    Bulent Tavli & Kemal Bicakci

  2. Technical University of Catalonia, Barcelona, Spain

    Ruken Zilan & Jose M. Barcelo-Ordinas

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  1. Bulent Tavli
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  2. Kemal Bicakci
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  3. Ruken Zilan
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  4. Jose M. Barcelo-Ordinas
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Correspondence to Bulent Tavli.

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Tavli, B., Bicakci, K., Zilan, R. et al. A survey of visual sensor network platforms. Multimed Tools Appl 60, 689–726 (2012). https://doi.org/10.1007/s11042-011-0840-z

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