Name
Abstract | ||
|---|---|---|
Wireless Sensor Networks (WSNs) are increasingly available for mission-critical applications such as emergency management and health care. To meet the stringent requirements on communication performance, it is crucial to understand the complex wireless interference among sensor nodes. Recent empirical studies suggest that the packet-level interference model, also referred to as the packet reception ratio (PRR) versus SINR model or PRR-SINR model, offers significantly improved realism than other simplistic models such as the disc model. However, as shown in our experimental results, the PRR-SINR model yields considerable spatial and temporal variations in reality, which poses a major challenge for accurate measurement at run time. This paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based PRR-SINR model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement (AIM) for measuring the proposed PRR-SINR model with assured accuracy at run time. AIM also adopts new clock calibration and in-network aggregation techniques to reduce the overhead of interference measurement. Our extensive experiments on a 17-node testbed of TelosB motes show that AIM achieves high accuracy of PRR-SINR modeling with significantly lower overhead than state of the art approaches. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDCS.2011.47 | ICDCS |
Keywords | Field | DocType |
wsn,aim,disc model,prr-sinr model,prr-sinr modeling,radiofrequency interference,packet-level interference model,accuracy-aware interference modeling,packet reception ratio,sinr model,run time,proposed prr-sinr model,simplistic model,accuracy-aware approach,complex wireless interference,prr-sinr model yield,wireless sensor networks,new regression-based prr-sinr model,health care,empirical study,interference,signal to noise ratio,accuracy,time measurement,wireless sensor network | Packet reception,Computer science,Signal-to-noise ratio,Computer network,Testbed,Interference (wave propagation),Statistical theory,Wireless sensor network,Calibration,Empirical research | Conference |
ISSN | ISBN | Citations |
1063-6927 E-ISBN : 978-0-7695-4364-2 | 978-0-7695-4364-2 | 13 |
PageRank | References | Authors |
0.56 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Huang | 1 | 284 | 22.18 |
| Shucheng Liu | 2 | 187 | 13.32 |
| Guoliang Xing | 3 | 3416 | 209.19 |
| Hongwei Zhang | 4 | 935 | 67.71 |
| Jianping Wang | 5 | 1422 | 103.90 |
| Liusheng Huang | 6 | 1082 | 123.52 |