A Lightweight Forwarding Strategy for Named Data Networking in Low-end IoT. (15th December 2019)

• Record Type: Journal Article
• Authors: Abane, Amar; Daoui, Mehammed; Bouzefrane, Samia; Muhlethaler, Paul
• Is Part Of: Journal of network and computer applications. Volume 148(2019)
• Publication Date: 2019-12-15
• Subjects: Named Data Networking -- Reinforcement Learning -- Wireless Networks -- Internet of Things -- IEEE 802.15.4; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2019.102445
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: The proliferation of Internet of Things (IoT) devices has given a new dimension to networking operations. In fact, IoT issues in terms of complex communication patterns, dynamic topologies and security force to re-think basic networking operations in a more adapted way. Moreover, the IoT needs contribute to show the limitations of the IP model to support content-based applications, and the emergence of the Information Centric Networking (ICN) paradigm seems to be more compliant with the IoT vision. In this context, to enable the ICN model in IoT devices, we present a lightweight forwarding strategy for Named Data Networking (NDN) over IEEE 802.15.4. NDN is an ICN architecture with a great potential for the IoT and future Internet. This paper presents a forwarding strategy that reduces network overhead to the bare minimum, while keeping satisfactory performance in different IoT application scenarios. To forward named contents without node addresses, the strategy is based on a reinforcement learning technique that provides an accurate broadcast-based forwarding with a reduced overhead.