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Data Fault Detection Algorithm for Binary Sensor Networks
Vaishali S. Deshmukh1, Arvind V. Deshpande2, Parikshit N. Mahalle3

1Vaishali S. Deshmukh, Department of Computer Engineering, Smt. Kashibai Navale College of Engg., Savitribai Phule Pune University, Pune (M.H), India.
2Arvind V. Deshpande, Department of Computer Engineering, Smt. Kashibai Navale College of Engg., Savitribai Phule Pune University, Pune (M.H), India.
3Parikshit N. Mahalle, Department of Computer Engineering, Smt. Kashibai Navale College of Engg., Savitribai Phule Pune University, Pune (M.H), India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1348-1354 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6649048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Sensors are deployed in the real world to gather huge amounts of raw data. This unprocessed data contain meaningful information. However the data might not be correct due to faults in the sensors. So in order to draw valid conclusion, sensors producing faulty data need to be identified. This paper proposes an interesting approach to discover anomalous data from binary sensors. Our approach is able to differentiate correctly functioning and faulty sensors with respect to data fault. The correct interpretation of data is carried out by exploiting spatial and temporal correlations among sensor data. Experimentation shows that the proposed algorithm is able to identify faults in binary sensors. In various applications of sensor networks, the accuracy of sensor data directly affects the performance of that application. The proposed algorithm shows significant improvement in detection accuracy over existing technique for higher sensor fault probability.
Keywords: Wireless Sensor Network, Data Fault, Binary Sensor

Scope of the Article: WSN