Confrence

📘 Literature Review Summary (for GitHub Release Tag)
The evolution of IoT and cloud computing technologies has enabled transformative changes in real-time rescue and surveillance systems. In the context of public safety and emergency response, numerous studies highlight the efficiency and intelligence brought by these technologies when integrated with AI and decision-making algorithms. Sinha et al. (2019) present a foundational framework by applying Task-Technology Fit theory to IoT-based disaster management, proving that IoT devices can effectively collect, transmit, and contextualize data in real time to support critical rescue missions during natural disasters. Their work emphasizes the role of cloud-enabled infrastructure in handling vast sensor data, which is directly aligned with the architecture of IntelliGuard Ver. 2.

Complementing this, Roy et al. (2022) explore a multi-robot system integrated with IoT and secured via cloud computation for coordinated rescue tasks. Their model particularly focuses on ensuring communication and data security, which is essential for remote operations like pipeline inspections and borewell rescues, as envisioned in IntelliGuard. Similarly, Sood et al. (2018) propose a Fog-IoT architecture for smart flood monitoring, where latency-sensitive data is processed locally while non-critical data is offloaded to the cloud. This hybrid architecture is closely aligned with IntelliGuard’s goal of radar-based obstacle detection and real-time decision-making at the edge.

Further, Padmaavathy et al. (2022) stress the importance of AI integration in IoT-cloud systems, proposing models where intelligent anomaly detection and predictive analytics can secure and optimize real-time data communication in critical operations. Their security-focused perspective is instrumental in preventing system breaches in surveillance applications. Finally, the IntelliGuard concept by Pandey and Bhatt (2023) lays a direct foundation for your project by integrating AI-based cyber-defense strategies with IoT surveillance bots, offering real-world applications for safeguarding sensitive zones.

In summary, these scholarly works collectively support the development of IntelliGuard Ver. 2 by validating the use of IoT, cloud infrastructure, AI-based analytics, and autonomous systems in public safety scenarios. They reinforce the viability of building a robust, real-time, and intelligent rescue monitoring bot equipped with radar, camera sensors, and private IP streaming for inaccessible or hazardous environments like borewells, underground pipelines, and mining tunnels.