Vehicular Grouping Protocol: Towards Cyber Physical Network Intelligence

被引:3
作者
Al-Hamid, Duaa Zuhair [1 ]
Al-Anbuky, Adnan [1 ]
机构
[1] Auckland Univ Technol, Elect & Elect Engn, Auckland, New Zealand
来源
IEEE CONGRESS ON CYBERMATICS / 2021 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS (ITHINGS) / IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) / IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) / IEEE SMART DATA (SMARTDATA) | 2021年
关键词
Vehicular network; Self-formation; Wireless sensor network; Virtualization; IoT; IoV; MODEL;
D O I
10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00017
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Vehicular network structures present a range of challenges and opportunities for efficiently managing awareness of road dynamics and network connectivity. An enhanced manageable organization can offer a better reaction to safety-related road events, facilitate dynamic topological flexibility, relate to road layout, and interact with unpredictable distribution of the vehicles. Vehicular grouping is one of the suggested structural techniques that offers a great benefit in grouping vehicles and modelling data routing, giving importance to road structure and the occurrence of a dynamic event within the associated group of vehicles. The approach discussed in this paper is based on a dynamic grouping through phases of self-formation, self-joining, self-leaving and self-healing as key components of the protocol operational cycle. Both vehicular physical connected resources and the remote computational cloud could be used for data processing and monitoring of road dynamics. This, in effect, encourages an Internet of Things (IoT) environment that enhances the dynamic performance through direct interaction between the virtualized network of vehicles and the physical network on the road leading to Internet of Vehicles (IoV). The objective of this paper is to develop a concept of network self-formation algorithm based on vehicle grouping strategy wherein the node can flexibly switch its function, be it an IoT gateway or a router node, based on the proposed fitness election model to be elected as group head. Testing using Contiki-Cooja simulator has been implemented on various road condition scenarios reflects the operational ability of the algorithm taking into consideration the network performance based on the ultimate capacity of the road.
引用
收藏
页码:9 / 16
页数:8
相关论文
共 50 条
[41]   VelogCPS: A safe blockchain network for cyber-physical systems leveraging block verifiers [J].
Garcia-Valls, Marisol ;
Chirivella-Ciruelos, Alejandro M. .
JOURNAL OF SYSTEMS ARCHITECTURE, 2024, 153
[42]   Research on a Fusion Scheme of Cellular Network and Wireless Sensor for Cyber Physical Social Systems [J].
Yang, Ai-Min ;
Yang, Xiao-Lei ;
Chang, Jin-Cai ;
Bai, Bin ;
Kong, Fan-Bei ;
Ran, Qing-Bo .
IEEE ACCESS, 2018, 6 :18786-18794
[43]   Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks [J].
Hussain, Md Muzakkir ;
Beg, M. M. S. .
INTERNATIONAL JOURNAL OF SOFTWARE SCIENCE AND COMPUTATIONAL INTELLIGENCE-IJSSCI, 2019, 11 (01) :47-69
[44]   Distributed state estimation for cyber-physical systems under Round-Robin communication protocol [J].
Zhu F.-Z. ;
Peng L. .
Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2022, 39 (10) :1925-1936
[45]   Towards a real-time IoT: Approaches for incoming packet processing in cyber-physical systems [J].
Behnke, Ilja ;
Blumschein, Christoph ;
Danicki, Robert ;
Wiesner, Philipp ;
Thamsen, Lauritz ;
Kao, Odej .
JOURNAL OF SYSTEMS ARCHITECTURE, 2023, 140
[46]   A Cyber-Physical System Framework Towards Smart City and Urban Computing to Aid People with Disabilities [J].
Goldberg, Martin ;
Zhang, Zhanyang .
2018 27TH WIRELESS AND OPTICAL COMMUNICATION CONFERENCE (WOCC), 2018, :25-29
[47]   Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems [J].
Mohammed, Abdul-Wahid ;
Xu, Yang ;
Hu, Haixiao ;
Agyemang, Brighter .
SENSORS, 2016, 16 (09)
[48]   Intelligent Network Intrusion Detection and Situational Awareness for Cyber-Physical Systems in Smart Cities [J].
Song, Shouliang ;
Dong, Anming ;
Zhu, Honglei ;
Wang, Shuai ;
Yu, Jiguo .
PRICAI 2023: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I, 2024, 14325 :29-35
[49]   Deep Q-Network with Reinforcement Learning for Fault Detection in Cyber-Physical Systems [J].
Jayaprakash, J. Stanly ;
Priyadarsini, M. Jasmine Pemeena ;
Parameshachari, B. D. ;
Karimi, Hamid Reza ;
Gurumoorthy, Sasikumar .
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2022, 31 (09)
[50]   Generating a distributed network attack dataset based on weather station IoT Cyber Physical Systems [J].
Johnson, David ;
Roy, Kaushik .
2024 7TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, BIG DATA, COMPUTING AND DATA COMMUNICATION SYSTEMS, ICABCD 2024, 2024,