Smart Surveillance Aided by Fog, Edge and Cloud Computing Architectures: A Critical Review - Moepi, Glen (The Indonesian Journal of Computer Science, 2026)
The rapid growth of the Internet of Things (IoT) has transformed intelligent surveillance systems, creating demands for real-time monitoring, efficient data processing, and reliable decision-making. This review examines the integration of cloud, fog, and edge computing as complementary paradigms for enhancing surveillance. Cloud computing provides large-scale storage, advanced analytics, and centralized management. Fog computing brings computation and networking closer to end devices, reducing latency and optimizing bandwidth. Edge computing enables immediate processing at the source, ensuring rapid responses to security-critical events. Together, these paradigms form a hierarchical, collaborative framework that balances scalability, efficiency, and reliability while overcoming the limitations of standalone approaches. Challenges such as device heterogeneity, interoperability issues, and security risks persist. Future directions include optimized resource allocation, lightweight Artificial Intelligence (AI) for fog and edge nodes, and stronger privacy-preserving mechanisms. The synergy of cloud, fog, and edge computing is expected to drive adaptive, secure, and resilient surveillance in IoT-based smart environments.