.png){kind=icon}
Return
An Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework
.png){kind=icon}
OA
AI
DOI：10.3390/s140508330.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency.
Keywords:
Bayesian compressed sensing
routing structure
wireless sensor networks
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
Organization
Cited Papers
ShareSaveShareSaveAnalysis of Energy Efficiency of Compressive Sensing in Wireless Sensor Networks.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
ShareSaveOne-Dimensional Spin Channel in Two-Dimensional Transition Metal Dichalcogenide Heterostructures
PREAI
ShareSaveShareSaveShareSave
View more