A Lightweight Forwarding Strategy for Named Data Networking in Low-end IoT

被引:18
作者
Abane, Amar [1 ,2 ]
Daoui, Mehammed [1 ]
Bouzefrane, Samia [2 ]
Muhlethaler, Paul [3 ]
机构
[1] Univ Mouloud Mammeri Tizi Ouzou, LARI Lab, Tizi Ouzou, Algeria
[2] Conservatoire Natl Arts & Metiers, CEDRIC Lab, Paris, France
[3] EVA Inria, Paris, France
关键词
Named Data Networking; Reinforcement Learning; Wireless Networks; Internet of Things; IEEE; 802.15.4;
D O I
10.1016/j.jnca.2019.102445
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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 rethink 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.
引用
收藏
页数:12
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