Name
Abstract | ||
|---|---|---|
Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limitsWSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rumtime. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference 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 interference model with assured accuracy at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TMC.2015.2416182 | Mobile Computing, IEEE Transactions |
Keywords | Field | DocType |
Interference,Signal to noise ratio,Accuracy,Wireless sensor networks,Time measurement,Analytical models,Computational modeling | Radio resource management,Key distribution in wireless sensor networks,Wireless,Wireless site survey,Computer science,Signal-to-noise ratio,Testbed,Computer network,Interference (wave propagation),Wireless sensor network,Distributed computing | Journal |
Volume | Issue | ISSN |
PP | 99 | 1536-1233 |
Citations | PageRank | References |
9 | 0.45 | 21 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangmao Chang | 1 | 66 | 11.74 |
| Jun Huang | 2 | 284 | 22.18 |
| Shucheng Liu | 3 | 187 | 13.32 |
| Guoliang Xing | 4 | 3416 | 209.19 |
| Hongwei Zhang | 5 | 935 | 67.71 |
| Jianping Wang | 6 | 1422 | 103.90 |
| Liusheng Huang | 7 | 1082 | 123.52 |
| yi zhuang | 8 | 9 | 0.45 |