Attributes-Driven Post-Disaster Data Collection and Routing based on Internet of Robotic Things

The Internet of Robotic Things (IoRT) plays a crucial role in facilitating rescue missions for survivors in disaster-stricken areas. However, key challenges remain in rapidly collecting comprehensive disaster data and efficiently routing it to rescue personnel. To address these challenges, we propose an attribute-driven IoRT framework for post-disaster data collection and routing, enabling rapid and energy-efficient access to comprehensive disaster data. The proposed framework has three key innovations: (1) Robot attributes are defined to elect collection robots, leveraging Information-Centric Networking (ICN) to gather comprehensive disaster data; (2) Learning automata is exploited to elect relay robots, constructing optimized routing paths; and (3) Routing paths are reused for continuous, push-based disaster data dissemination. Experimental results demonstrate the framework’s superiority in reducing dissemination delays, improving success rates, and minimizing energy consumption compared to existing approaches.