Our team in CS 424 Real-time and Cyber-physical & Systems (Abdulrahman AlRabah, Imaad Zaffar Khan, Amaan Aijaz Sheikh) — conducted a survey of 21 papers on authentication and access control for smart home devices. We synthesize trends across authentication mechanisms (passwords, biometrics, multi-factor, behavioral, voice/facial), access control models (RBAC, ABAC, and context-aware hybrids), and privacy practices (transparency, consent, data protection) while analyzing trade-offs between security and usability. The study highlights key challenges—interoperability across heterogeneous devices, scalability/manageability of policies, and observability for debugging—and identifies promising directions such as AI-driven adaptive authentication and anomaly detection, context-aware policies, hybrid RBAC+ABAC designs, and user-empowering interfaces for managing multi-user households.

In MP1, we utilized the Waymo Open Dataset to simulate autonomous vehicle camera perception and designed scheduling strategies using bounding-box prioritization (based on area and depth) to optimize response times.

In MP2, we extended this work to real-world hardware using the NVIDIA Jetson Nano, deploying YOLOv8 for real-time object detection under resource constraints. Through iterative implementations — including inversion-based task scheduling, de-duplication across frames, and spatial hashing for optimization — we achieved substantial improvements in average response time and runtime efficiency (over 30 % reduction compared to baseline).