Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Toward cluster-based weighted compressive data aggregation in wireless sensor networks
Abbasi-Daresari S., Abouei J. Ad Hoc Networks36, Part 1 368-385,2016.Type:Article
Date Reviewed: Jun 27 2016

It seems surprising that so many users of remote sensing (including wireless networks) have yet to hear the 10-year-old message of compressive sensing: data from nature is “sparse,” so a small number of properly selected data points often allow one to reconstruct the original data set with high probability and/or low error rate. Put briefly, one can compress at the source, rather than collecting the whole set.

(Confusingly, the standard abbreviation for compressive sensing is “CS.” This review follows that convention, but also refers to “traditional CS,” that is, computer science.)

While the first CS papers were rather abstract, researchers have continued to expand the ideas of CS into more areas of applications involving more types of sensors. Here, the authors are interested in wireless sensor networks, that is, a geographically spread array of low-power sensors that communicate with each other by radio. CS has been used for such problems for a while; the innovation in this paper is more a result about “traditional CS” graph techniques. The sensors are the nodes and the radio links are the edges, as expected, but they choose to optimize power consumption under the reasonable assumption that batteries are hard to change. Thus, sensors should prefer to reduce transmitted power by only communicating with nearby sensors. Their intricate algorithms select collector nodes and active radio links to acquire the data using hybrid CS techniques. But this is also “traditional CS”: they even use Dijkstra’s all points shortest path algorithm in their new algorithm.

They present convincing simulations with random network configurations, which as usual raise questions about the applicability to deployed networks whose data may be intrinsically sparse due to spatial correlations.

Reviewer:  J. Wolper Review #: CR144530 (1609-0659)
Bookmark and Share
 
Sensor Networks (C.2.1 ... )
 
 
Sparse, Structured, And Very Large Systems (Direct And Iterative Methods) (G.1.3 ... )
 
 
Wireless Communication (C.2.1 ... )
 
 
Clustering (I.5.3 )
 
Would you recommend this review?
yes
no
Other reviews under "Sensor Networks": Date
Performance analysis of opportunistic broadcast for delay-tolerant wireless sensor networks
Nayebi A., Sarbazi-Azad H., Karlsson G. Journal of Systems and Software 83(8): 1310-1317, 2010. Type: Article
Nov 8 2010
Heartbeat of a nest: using imagers as biological sensors
Ko T., Ahmadian S., Hicks J., Rahimi M., Estrin D., Soatto S., Coe S., Hamilton M. ACM Transactions on Sensor Networks 6(3): 1-31, 2010. Type: Article
Jan 10 2011
Efficient clustering-based data aggregation techniques for wireless sensor networks
Jung W., Lim K., Ko Y., Park S. Wireless Networks 17(5): 1387-1400, 2011. Type: Article
May 8 2012
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy