scholarly journals Interest Forwarding in Named Data Networking Using Reinforcement Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3354 ◽  
Author(s):  
Olumide Akinwande
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Routing Protocol ◽  
Named Data Networking ◽  
Delivery Performance ◽  
Efficient Performance ◽  
Random Neural Network ◽  
Key Features ◽  
With Memory ◽  
Data Networking

In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently coordinating the forwarding of requests with the volatile cache states at the routers. In this paper, we address information-centric networks and consider in-network caching specifically for Named Data Networking (NDN) architectures. Our proposal departs from the forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding. We propose a novel adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), which leverages the routing information and actively seeks new delivery paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves better delivery performance than a strategy that uses fixed paths from the routing layer and a more efficient performance than a strategy that retrieves contents from the nearest caches by flooding requests.

TSRN: A Tabu Search-Based Routing Protocol for Named Data Networking

2018 ◽  
Vol 101 (3) ◽  
pp. 1411-1428
Author(s):  
Zeinab Shariat ◽  
Ali Movaghar ◽  
Mehdi Hosseinzadeh
Keyword(s):  
Tabu Search ◽  
Routing Protocol ◽  
Named Data Networking ◽  
Data Networking

scholarly journals Mobility Support in Vehicular Named-Data Networking

2019 ◽  
Author(s):  
João do Monte Duarte ◽  
Torsten Braun ◽  
Leandro Villas
Keyword(s):  
Routing Protocol ◽  
Communication Model ◽  
Network Partitioning ◽  
Named Data Networking ◽  
Application Performance ◽  
Mobility Support ◽  
Hoc Networks ◽  
Reverse Path ◽  
Data Networking

In this thesis, Vehicular Named-Data Networking (VNDN) refers tothe use of the Named-Data Networking communication model over VehicularAd-hoc Networks. With the aim of addressing the problems caused by mobility to efficiently support VNDN communications in highly mobile traffic scenarios, various contributions were proposed in the scope of this thesis. These contributions include a routing protocol, able to address VNDN problems such as broadcast storms and message redundancy, as well as solutions to enable content advertisements and for addressing the problems caused by reverse path partitioning, network partitioning, and source mobility. Finally, all the proposed solutions are integrated into a single framework called MobiVNDN. The evaluation results show that the proposed solutions are efficient and scalable, providing high VNDN application performance even in complex traffic scenarios.

scholarly journals A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

2016 ◽  
Vol 17 (2) ◽  
pp. 59-69
Author(s):  
Parisa Bazmi ◽  
Manijeh Keshtgary
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Shortest Path ◽  
Optimal Path ◽  
Named Data Networking ◽  
Internet Architecture ◽  
Multiple Paths ◽  
Forwarding Strategy ◽  
Packet Drop ◽  
Data Networking

Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding strategy (NNTF) is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

scholarly journals A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4859
Author(s):  
Ju-Ho Choi ◽  
Jung-Hwan Cha ◽  
Youn-Hee Han ◽  
Sung-Gi Min
Keyword(s):  
Routing Protocol ◽  
Named Data Networking ◽  
Data Link ◽  
Ip Address ◽  
Link State ◽  
Dual Connectivity ◽  
Soft Handover ◽  
Link Service ◽  
Access Router ◽  
Data Networking

With the exponential growth of Cyber-Physical Systems (CPSs) technologies, the Internet of Things (IoT) infrastructure has evolved from built-in static infrastructure to a flexible structure applicable to various mobile environments. In this Internet of Mobile Things (IoMT) environment, each IoT device could operate simultaneously as a provider and consumer of information, and could provide new services through the exchange of such information. Named Data Networking (NDN), which could request data by content name rather than location (IP address), is suitable for such mobile IoT environments. However, in the current Named Data Networking (NDN) specification, producer mobility is one of the major problems in need of remedy. Previously proposed schemes for producer mobility use an anchor to hide the producer’s movement from consumers. As a result, they require a special anchor node and a signaling procedure to track the current locations of contents. A few anchorless schemes have also been proposed, but they still require mobility signaling and all NDN routers on the signaling path must understand the meaning of the signaling. We therefore propose an anchorless producer mobility scheme for the NDN. This scheme uses a dual-connectivity strategy that can be expressed as a soft handover. Whenever a producer changes its NDN Access Router (NAR), the new mobility link service located on the mobile producer’s old NDN face repairs the old link so that the connectivity with the pNAR can be maintained for a while. The old NDN face is removed after the new location information on the contents of the producer is disseminated over the NDN network by the Named-data Link State Routing Protocol (NLSR) routing protocol at the nNAR. The new mobility link service decouples connection and transaction to hide the collapse of the link. Therefore, the NDN’s mobility procedure could be simplified as the handover is defined as transaction completion as opposed to a breakdown of links. The proposed scheme prevents the routing information from being abruptly outdated due to producer mobility. Our simulation results show seamless handover when the producer changes its default access router.

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