Credence Aware Data Aggregation for Wireless Sensor Networks

Abstract

To ensure data's reliability and credibility in Wireless Sensor Networks (WSNs), we provide an effective Credence-aware in-network aggregation design in persistent wireless sensor networks. This approach was motivated by a well-studied reputation and Credence relationships within social sciences. The proposed method uses an efficient CSDA algorithm to get more accurate results in terms of response time, penalty weights, the number of nodes, detection accuracy, etc. During the aggregating process, the Credence evaluation technique obtains benefits by identifying sensor node reliability, distinguishing illegal nodes, and filtering out erroneous data. The main objective of the work is to offer the most accurate answer possible to the user also while ensuring network health by identifying possibly compromised nodes. Experimental results show strategy is effective.