Secure Data Aggregation Framework for Wireless Sensor Networks with Privacy Preservation

Abstract

Wireless Sensor Networks (WSNs) have become an integral component of modern intelligent systems by enabling continuous monitoring, environmental sensing, industrial automation, healthcare applications, military surveillance, and smart city infrastructures. Since sensor nodes continuously generate and forward large volumes of sensitive information through resource-constrained wireless environments, secure data aggregation has emerged as a critical challenge due to vulnerabilities associated with data tampering, node compromise, privacy leakage, communication overhead, and limited energy resources. This experimental study proposes a Secure Data Aggregation Framework for Wireless Sensor Networks with Privacy Preservation that integrates secure data collection, encryption, authentication, privacy-preserving aggregation, integrity verification, energy-efficient communication, and performance evaluation into a unified computational architecture. The proposed framework combines lightweight cryptographic techniques, secure aggregation mechanisms, privacy preservation strategies, and intelligent communication management to protect sensitive sensor information while maintaining network efficiency. A mathematical framework and algorithmic strategy are developed to evaluate data confidentiality, aggregation accuracy, packet delivery ratio, communication overhead, energy consumption, and Quality of Service (QoS). Experimental evaluation demonstrates that the proposed framework significantly improves secure data transmission, aggregation reliability, privacy preservation, communication efficiency, and network lifetime while reducing redundant transmissions and security vulnerabilities. The proposed framework provides valuable guidance for researchers, network engineers, and cybersecurity professionals seeking to design secure, scalable, energy-efficient, and privacy-preserving Wireless Sensor Network management systems.