A Controller-Driven Approach for Opportunistic Networking

Opportunistic networks (OppNets) leverage opportunistic contacts to flow data across an infrastructure-free network. As of yet, OppNets’ performance depends on applying the most suitable forwarding strategy based on the OppNet typology. On the other hand, software-defined networking (SDN) is a parad...

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Bibliographic Details
Main Authors: MariaCarmen de Toro, Carlos Borrego, Sergi Robles
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/12/23/12479
Description
Summary:Opportunistic networks (OppNets) leverage opportunistic contacts to flow data across an infrastructure-free network. As of yet, OppNets’ performance depends on applying the most suitable forwarding strategy based on the OppNet typology. On the other hand, software-defined networking (SDN) is a paradigm for wired networks that decouples the control and data planes. The control plane oversees the network to configure the data plane optimally. Our proposal uses SDN-like controllers to build a partial overview of the opportunistic network. The forwarding strategy uses this context information to achieve better network performance. As a use case of our proposal, in the context of an OppNet quota-based forwarding algorithm, we present a controller-driven architecture to tackle the congestion problem. Particularly, the controller-driven architecture uses the context information on the congestion of the network to dynamically determine the message replication limit used by the forwarding algorithm. A simulation based on real and synthetic mobility traces shows that using context information provided by the controller to configure the forwarding protocol increments the delivery ratio and keeps a good latency average and a low overhead compared with the baseline forwarding protocols based on message replication. These results strengthen the benefits of using supervised context information in the forwarding strategy in OppNets.
ISSN:2076-3417