Automatic Configuration of OpenFlow in Wireless Mobile Ad hoc Networks

Sharma, Sachin; Nag, Avishek; Stynes, Paul; Nekovee, Maziar

doi:10.1109/hpcs48598.2019.9188200

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…As part of our EU NGIAtlantic project [3,4], wireless Internet of Things (IoT) devices send data to an IoT application in the cloud via a gateway that runs network functions to ensure security and latency. In this project, software-defined networking ( [5,6]) and machine learning are used to make intelligent IoT decisions. In addition, several EU and US testbeds-W-iLab1.t, W-iLab2.t, CityLab, COSMOS, and POWDER-are considered for experimentation.…”

Section: Introductionmentioning

confidence: 99%

Future Wireless Networking Experiments Escaping Simulations

et al. 2022

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In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported.

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Section: Introductionmentioning

confidence: 99%

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et al. 2022

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Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles

Sharma

2021

Internet of Things

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On the optimal integration of legacy and SDWN communication nodes in emergency scenarios

Sousa,

Sargento,

Luís

2024

Wireless Netw

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Spontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.

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Automatic Configuration of OpenFlow in Wireless Mobile Ad hoc Networks

Cited by 9 publications

References 15 publications

Future Wireless Networking Experiments Escaping Simulations

Future Wireless Networking Experiments Escaping Simulations

Towards Artificial Intelligence Assisted Software Defined Networking for Internet of Vehicles

On the optimal integration of legacy and SDWN communication nodes in emergency scenarios

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