Vehicular Intelligence: Towards Vehicular Network Digital-Twin

Al-Hamid, Duaa Zuhair; Al-Anbuky, Adnan

doi:10.1109/apcc55198.2022.9943695

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…In addition, the RSSI data are collected at different sampling rates with various number of node scenarios to test the delivery of packets and determine if there is any packet loss. This is mainly to evaluate the capacity of the coordinator (cluster head) node, tested virtually [8,9]. The modified values of the RF transmission power of the sensor nodes based on designed indoor and outdoor scenarios can reflect the mobility aspect in the network.…”

Section: Physical Network Setup For Wsn-based Clusteringmentioning

confidence: 99%

Section: Physical Network Setup For Wsn-based Clusteringmentioning

confidence: 99%

“…Mainly, the testing relates to the network connectivity (router or gateway) capacity tested in the virtual platform using the Contiki Cooja simulator. Herein, the physical network test scenario objective is aligned with our road traffic analysis capacity and virtualization capacity testing scenarios [8,9]. The physical network test was performed, and data were collected when a set of (10 and 20) TI CC2538s connected to a coordinator node reported RSSI values based on a set of sampling rates (1, 2, 3, 4, and 5 samples/s).…”

Section: Network-based Clustering Use Case: Router/coordinator Node C...mentioning

confidence: 99%

“…Herein, cloud-based virtualization is used to develop and provide specified functional configuration parameters to physical nodes. As a result, the cloud can assist with performance analysis via the virtual unit, where possible reorchestration of network behavior can be deployed and tested prior to real-world implementation, paving the way for the cyber-physical system [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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A Novel SDWSN-Based Testbed for IoT Smart Applications

Al-Hamid,

Karegar,

Chong

2023

Future Internet

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Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from “prior-to-implementation” analysis via virtualization, as the performance of both virtual and physical networks is comparable.

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Section: Physical Network Setup For Wsn-based Clusteringmentioning

confidence: 99%

Section: Physical Network Setup For Wsn-based Clusteringmentioning

confidence: 99%

Section: Network-based Clustering Use Case: Router/coordinator Node C...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel SDWSN-Based Testbed for IoT Smart Applications

Al-Hamid,

Karegar,

Chong

2023

Future Internet

Self Cite

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Energy-efficient fuzzy management system using tri-parametric methodology in vanet

Blessy,

Brindha

2023

Wireless Netw

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UAV-enabled software defined data collection from an adaptive WSN

Karegar,

Al-Hamid,

Chong

2024

Wireless Netw

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Unmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

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Vehicular Intelligence: Towards Vehicular Network Digital-Twin

Cited by 6 publications

References 7 publications

A Novel SDWSN-Based Testbed for IoT Smart Applications

A Novel SDWSN-Based Testbed for IoT Smart Applications

Energy-efficient fuzzy management system using tri-parametric methodology in vanet

UAV-enabled software defined data collection from an adaptive WSN

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