Information-Centric Internet of Underwater Robotic Things-Based Content Caching and Transmission

Abstract

The Internet of Underwater robotic Things (IoUT) makes intelligent marine environmental exploration possible. Compared to the traditional IoT, IoUT is a three-dimension marine space characterized by water depth, so distances between consumers on the water surface and underwater contents in deep water grow substantially, resulting in considerable content access delays and costs. To achieve rapid and cost-efficient access to marine contents, we propose an information-centric Internet of underwater robotic things based content caching and transmission approach, and aim to leverage in-network caching and aggregation in the information-centric networking to access marine contents in deep water. The main ideas of this approach are threefold: (1) Exploit content attributes and node attributes to perform in-network caching, shortening distances between consumers on water surface and contents in deep water; and (2) Learning automata is leveraged to learn real-time forwarding information on caching robots in shallow water; and (3) Aggregation is utilized to share contents in deep water from optimal caching robots in shallow water. The experiment results demonstrate the feasibility and superiority of the proposal.