Name
Abstract | ||
|---|---|---|
Packet loss and energy dissipation are two major challenges of designing large scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transinf.E97.D.331 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
large scale wireless sensor networks, compressed sensing, packet loss, energy balancing | Computer science,Packet loss,Computer network,Real-time computing,Artificial intelligence,Compressed sensing,Computer vision,Load balancing (computing),Network packet,Network topology,Error detection and correction,Energy consumption,Wireless sensor network | Journal |
Volume | Issue | ISSN |
E97D | 2 | 1745-1361 |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangming Cao | 1 | 46 | 9.22 |
| Peter Jung | 2 | 154 | 23.80 |
| Slawomir Stanczak | 3 | 521 | 89.71 |
| F. Yu | 4 | 209 | 28.58 |