Local Aggregation Scheme for Data Collection in Periodic Sensor Network
Neetu Verma1, Dinesh Singh2
1Neetu Verma, Assistant Professor, Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana India.
2Dinesh Singh, Assistant Professor, Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3583-3588 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2602129219/2019©BEIESP | DOI: 10.35940/ijeat.B2602.129219
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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: Data aggregation is an important technique for data collection & aggregation in WSN where sensor nodes sense the raw data and sends the aggregated data to the sink node. In a cluster based periodic network, sensor node senses the data on a specific time interval, performs local aggregation and send aggregated data to Cluster Head (CH). Various Local aggregation algorithms are used to remove redundant data at sensor nodes but local outlier detection problem is still unsolved. Therefore, a local aggregation algorithm has been proposed which uses the temporal correlation property of WSN to eliminate redundant and local outlier data which improves the data sent ratio and data quality. Sensor measurement is collected at different time interval of a sensor, exhibits temporal correlation because measurements varies with small or same difference (δ) and measurements are treated as similar measurements. In proposed local aggregation approach, each sensor node finds similar measurements of sensors with their frequency (number of occurrence) in a specific time interval (Temporal correlation). Set having higher frequency is selected and transmitted the average values of measurements that lie in the selected set to the cluster head. If sensors don’t detect any reading between intervals it simple send a message ‘data not found’ instead of sending empty set. In this way we delete redundant and local outliers. The experimental result shows that algorithm improves the data quality and data sent ratio by eliminating redundant data and local outliers.
Keywords: Data aggregation, Data fusion, WSN, Temporal correlation.