• Sangeetha J
• Mrs Pushpalatha S

Wireless sensor network is almost everywhere, these networks has huge application in habitat monitoring, disaster management, security and military, etc. Data aggregation is gaining much attention from researchers as best way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we suggest an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further in a local aggregation at sensor node level, our technique allows cluster head to eliminate redundant data sets generated by neighboring nodes by applying two data aggregation methods. These suggested methods are based on the sets similarity functions and the distance functions, respectively. Based on real sensor data, we have analyzed their performances according to the energy consumption, delay, accuracy and overhead, and we show how these methods can significantly improve the performance of sensor networks.

Periodic Sensor Network (PSN), data aggregation, clustering topology, similarity and distance functions

"Performance Analysis of Periodic Sensor Networks Using Different Data Aggregation Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.395-401, June-2019, Available :http://www.jetir.org/papers/JETIR1906E07.pdf

"Performance Analysis of Periodic Sensor Networks Using Different Data Aggregation Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp395-401, June-2019, Available at : http://www.jetir.org/papers/JETIR1906E07.pdf