SDAFPS: Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution

Abstract

Secure data aggregation intends to reduce redundant data transmission and malicious node interference in the network. Therefore, designing secure data aggregation protocol is a crucial task in WSNs. In this paper, we have proposed a Secure Data Aggregation using Fuzzy Judgement, Pattern Category and SHAP Contribution (SDAFPS) protocol. The SDAFPS protocol involves three main phases. In the first phase, the protocol controls the topology with the selection of efficient aggregator node in every interval. The second phase uses category pattern code generation and utilization concept to reduce data size and to aggregate data. Finally, in third phase, the aggregated data are encrypted using partial equation of SHAP contribution and decrypted with SHAP contribution equation. The decrypted data are verified with dataset preserved at the sink node. The SDAFPS protocol is implemented using NS2 Simulator