Name
Abstract | ||
|---|---|---|
Wireless Sensor Networks (WSNs) are widely used for monitoring physical happenings of the environment. However, the data gathered by the WSNs may be inaccurate and unreliable due to power exhaustion, noise and other reasons. Unnecessary data such as erroneous data and redundant data transmission causes a lot of extra energy consumption. To improve the data reliability and reduce the energy consumption, we proposed an in-network processing architecture for data cleaning, which divides the task into four stages implemented in different nodes respectively. This strategy guaranteed the cleaning algorithms were computationally lightweight in local nodes and energy-efficient due to almost no communication overhead. In addition, we presented the detection algorithms for data faults and event outliers, which were conducted by utilizing the related attributes from the local sensor node and the cooperation with its relaying neighbor. Experiment results show that our proposed approach is accurate and energy-efficient. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/10798587.2016.1152769 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
Wireless sensor networks, data cleaning, in-network processing, outlier detection, fault detection | Sensor node,Key distribution in wireless sensor networks,Anomaly detection,Data transmission,Computer science,Fault detection and isolation,Outlier,Real-time computing,Wireless sensor network,Energy consumption | Journal |
Volume | Issue | ISSN |
22 | 4 | 1079-8587 |
Citations | PageRank | References |
4 | 0.39 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Lei | 1 | 713 | 52.69 |
| Haiyang Bi | 2 | 4 | 0.39 |
| Ying Xia | 3 | 10 | 2.85 |
| Jun Huang | 4 | 394 | 45.19 |
| Hae-Young Bae | 5 | 78 | 31.47 |