Name
Abstract | ||
|---|---|---|
Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23954-0_44 | AI*IA |
Keywords | Field | DocType |
work environment,environmental monitoring,deployment environment,actual trend,probabilistic anomaly detection,sensor node,wireless sensor network,probabilistic algorithm,faulty sensor,faulty node,environmental condition,wireless sensor networks,probabilistic reasoning,autonomic computing | Sensor node,Key distribution in wireless sensor networks,Anomaly detection,Autonomic computing,Computer science,Brooks–Iyengar algorithm,Real-time computing,Probabilistic logic,Deployment environment,Wireless sensor network | Conference |
Volume | ISSN | Citations |
6934 | 0302-9743 | 4 |
PageRank | References | Authors |
0.47 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfonso Farruggia | 1 | 30 | 3.66 |
| Giuseppe Lo Re | 2 | 338 | 41.26 |
| Marco Ortolani | 3 | 209 | 21.31 |