Learning Automata-Based Content Forwarding for Information-Centric IoT
Xiaonan Wang, Ranran Zhang
Abstract
The Internet of Things (IoT) employs end-to-end communication to deliver content between a pair of source and destination. To overcome the limitations of end-to-end communication, the Named Data Networking (NDN) is introduced to IoT, called INDN, to improve the efficiency of content forwarding. In INDN, IoT nodes work as providers and content routers, but node mobility and limited resources make it difficult for INDN to fully exploit the advantages of NDN. NDN relies on Forwarding Information Base (FIB) to make forwarding decisions, but frequent FIB updates caused by in-network caching and provider mobility are costly and time-consuming. Moreover, frequent interruptions of reverse paths caused by node mobility cannot enable aggregation and also lead to frequent content forwarding failures. Taking these issues into account, in this paper, we propose a learning automata based content forwarding solution for INDN, and aim to leverage learning automata to support in-network caching and provider mobility and establish continuous reverse paths to enable aggregation, ultimately achieving the goals of alleviating content forwarding latency, costs and failures. The experiment results have demonstrated the feasibility and strengths of the proposal.