A Distributed Edge Intelligence Model for Privacy-Aware Smart Home Infrastructure: Secure Framework Design and Threat Mitigation Analysis

Abstract

The rapid expansion of smart home ecosystems has introduced significant challenges related to privacy preservation, secure data handling, and real-time intelligent decision-making. Conventional cloud-centric architectures often suffer from latency overhead, bandwidth dependency, and increased exposure to cyber-physical threats. This paper proposes a distributed edge intelligence model for privacy-aware smart home infrastructure that integrates decentralized computation, local inference, and adaptive security mechanisms. The framework emphasizes reducing data exposure by processing sensitive information at the edge while enabling collaborative intelligence across heterogeneous IoT devices. A structured threat mitigation strategy is incorporated to address common vulnerabilities such as unauthorized access, data interception, and device-level exploitation. The proposed model is critically evaluated through comparative analysis with existing smart surveillance and IoT-based home security systems. Findings indicate that edge-enabled distributed intelligence significantly enhances system responsiveness, reduces attack surfaces, and improves privacy compliance without compromising operational efficiency. The study builds upon established IoT surveillance and embedded intelligence frameworks while extending them into a privacy-centric distributed paradigm.