Author(s): Isento, João Nuno Gabriel
Date: 2014
Persistent ID: http://hdl.handle.net/10400.6/6132
Origin: uBibliorum
Subject(s): Delay-Tolerant Networks; Fragmentation; Performance Assessment; Routing; Testbed; Vehicular Ad Hoc Networks; Vehicular Delay-Tolerant Networks; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular communications where vehicles cooperate with each other, acting as the communication infrastructure, to provide low-cost asynchronous opportunistic communications. These communication technologies assume variable delays and bandwidth constraints characterized by a non-transmission control protocol/ internet protocol architecture but interacting with it at the edge of the network. VDTNs are based on the principle of asynchronous communications, bundleoriented communication from the DTN architecture, employing a store-carryand- forward routing paradigm. In this sense, VDTNs should use the tight network resources optimizing each opportunistic contact among nodes. At the ingress edge nodes, incoming IP Packets (datagrams) are assembled into large data packets, called bundles. The bundle aggregation process plays an important role on the performance of VDTN applications. Then, this paper presents three aggregation algorithms based on time, bundle size, and a hybrid solution with combination of both. Furthermore, the following four aggregation schemes with quality of service (QoS) support are proposed: 1) single-class bundle with N = M, 2) composite-class bundle with N = M, 3) single-class bundle with N > M, and 4) composite-class bundle with N > M, where N is the number of classes of incoming packets and M is the number of priorities supported by the VDTN core network. The proposed mechanisms were evaluated through a laboratory testbed, called VDTN@Lab. The adaptive hybrid approach and the composite-class schemes present the best performance for different types of traffic load and best priorities distribution, respectively.
