—Image-based embedded wireless sensor networks (WSNs) can be a useful tool in various environmental monitoring applications to unobtrusively observe biological phenomena. Our prior deployments of an embedded wireless imaging system at the James Reserve have already shown its feasibility and usefulness. However, we argue that data compression schemes employed in prior systems can be improved to provide higher image transfer rates per node, or lower the energy costs of wireless communication. In this paper, we develop an image compression scheme using K-means clustering on low-power embedded devices for image-based WSNs. Specifically, we use the similarity of pixel colors to group pixels and compress the original image. Using 100 000 images collected from our pilot deployments at the James Reserve, we study the applicability and impact of the proposed K-means clustering-based compression algorithm. Our results suggest that the cost of running K-means learning on a wireless sensor node may outweigh the benefit of data compression, but offloading the learning step and only performing the compression can provide significant energy gains. Specifically, our evaluations with real-world data sets show that our proposed scheme reduces power usage by ∼49%, when sending image updates from a bird nest periodically every 15 min.
Bibliographical noteFunding Information:
Manuscript received January 10, 2015; revised June 23, 2015 and August 30, 2015; accepted October 4, 2015. Date of publication October 30, 2015; date of current version November 22, 2017. This work was supported in part by the Chung-Ang University Research Grants in 2015 and also in part by the New Faculty Fund of Ajou University. (Corresponding author: JeongGil Ko.) J. Paek is with the School of Computer Science and Engineering, Chung-Ang University, Seoul 156756, Korea (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering