Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks
Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when appli...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-02-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/7256396 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of its l 1 -based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. The l 0 -based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF_CSOR). Furthermore, we study three different greedy algorithms under DAF_CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF_CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF_CSOR exhibits a certain advantage over conventional methods. |
|---|---|
| ISSN: | 1550-1477 |