Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks
Abstract
:1. Introduction
- (i)
- In temporal correlation of sensor network, we propose the PCM and interval methods;
- (ii)
- In spatial correlation we divide the sensor network into fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood, and other regions for anomaly detection, respectively, to achieve fault tolerance.
- (iii)
- We conduct extensive simulations to evaluate the performance of the proposed algorithms. The results demonstrate the effectiveness of the proposed algorithms.
2. Related Work
3. Symbols and Network Model
4. Fault Tolerance Detection Method
4.1. Temporal Correlation of Fault Tolerance Anomaly Detection Methods
Pauta Criterion Method, PCM, and Interval Method
Algorithm 1. Temporal correlation. |
1://Calculate interval for each sensor node 2: for each do 3: Data sets collected during the T time period 4: Calculate using R 5: end for 6://Detect if a sensor abnormal occurred. 7: if then 8: status = 9: Recalculate 10: else 11: calculate and 12: if then 13: increase 14: end if 15: if then 16: increase 17: end if 18: end if 19: if then 20: status = 21: else 22: status = 23: end if 24: broadcasting status and to all neighbors… |
4.2. Spatial Correlation of Fault Tolerance Anomaly Detection Methods
Algorithm 2. Spatial correlation. |
1: receiving statuses and from neighbors… 2: if status = then 3: continue 4: else 5: if in then 6: final status equal last status 7: end if 8: if in then 9: if status is same most of neighbors then 10: final status is fault 11: else 12: final status is event 13: end if 14: if in then 15: Compare and 16: end if 17: if in or in then 18: if status is same most of neighbors then 19: final status is event 20: else 21: final status is fault 22: end if 23: if in then 24: if status is most same of neighbors then 25: final status is normal 26: else 27: final status is fault 28: end if 29: end if |
5. Experimental Results and Analysis
5.1. Experimental Design
5.2. Result Analysis of Event and Fault Detection
5.3. Result Analysis of Event Boundary Neighborhood Detection
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Symbol | Definition |
---|---|
The total number of sensor nodes in the sensor network | |
Event characteristic duration | |
Sensor node adjacent sampling interval | |
Times of the sensor node sampled in . | |
Sampled readings of sensor node at time | |
The threshold function of sensor reading, in order to determine whether the event occurred | |
The expected value of the event | |
Sample size | |
Confidence | |
The upper limit (maximum value of normal interval, change with environment) | |
The lower limit (minimum value of normal interval, change with environment) | |
Sensor node is in the normal condition | |
Sensor node is in the event state | |
Sensor node is in the fault state | |
A fault has occurred on the sensor node | |
The sensor node detected the event |
Parameter | Value | |
---|---|---|
1 | Sensing area | |
2 | Measurement value of the sensor in the event area | Normal distribution |
3 | Measurement value of the sensor out of the event area | Uniform distribution |
4 | Faulty measurement value of the sensor | Uniform distribution |
5 | Communication radius |
Result | TPR | FNR | |||||||
---|---|---|---|---|---|---|---|---|---|
Method | 5% | 15% | 25% | 35% | 5% | 15% | 25% | 35% | |
FTAD | 98.1% | 98.0% | 98.0% | 97.9% | 91.2% | 96.0% | 98.3% | 98.3% | |
KPCA | 95.0% | 92.5% | 89.4% | 85.0% | 94.0% | 93.8% | 91.0% | 90.0% | |
FDS | 97.5% | 95.3% | 92.0% | 88.9% | 96.5% | 94.5% | 92.8% | 91.1% |
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Peng, N.; Zhang, W.; Ling, H.; Zhang, Y.; Zheng, L. Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks. Information 2018, 9, 236. https://doi.org/10.3390/info9090236
Peng N, Zhang W, Ling H, Zhang Y, Zheng L. Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks. Information. 2018; 9(9):236. https://doi.org/10.3390/info9090236
Chicago/Turabian StylePeng, Nengsong, Weiwei Zhang, Hongfei Ling, Yuzhao Zhang, and Lixin Zheng. 2018. "Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks" Information 9, no. 9: 236. https://doi.org/10.3390/info9090236