FRAMEWORK FOR AN INTELLIGENT VOICE ENABLED ACCESS CONTROL FOR SMART CITIES USING 5G TECHNOLOGY

Smart cities present unprecedented security challenges, necessitating authentication systems that are simultaneously secure, scalable, real-time, and privacy-preserving across millions of distributed access points. This paper illustrate and analyses a comprehensive architectural framework for integrating deep learning-based voice biometric authentication with fifth-generation (5G) wireless network infrastructure to address these demands. The framework strategically leverages four defining 5G capabilities: ultra-low latency (sub-10ms) via Ultra-Reliable Low-Latency Communication (URLLC) for instantaneous authentication response; city-wide IoT device connectivity through Massive Machine-Type Communications (mMTC) supporting simultaneous management of millions of access nodes; Mobile Edge Computing (MEC) for proximity-based, privacy-preserving biometric processing; and dedicated, isolated security infrastructure through Network Slicing. The architecture positions the ECAPA-TDNN voice authentication engine achieving 94.2% accuracy and 2.3% Equal Error Rate (EER) as a MEC-hosted service interfaced with 5G network slices dedicated to smart city security applications. A Belief-Desire-Intention extended (BDIx) intelligent agent framework governs autonomous, resilient node operation across distributed access infrastructure. The paper analyses how each 5G capability addresses specific limitations of conventional biometric systems in distributed urban environments. The new framework uses a four-layer integration architecture spanning IoT device, 5G access, MEC processing, and smart city platform layers, and discusses deployment considerations including privacy regulation compliance (GDPR, CCPA, NDPR), resilience design, and evolutionary migration toward 6G architectures. The framework provides a replicable blueprint for smart city planners and security architects seeking to deploy next-generation voice-enabled access control at urban scale.