A vehicular network–based intelligent transport system for smart cities

Zaheer, Tayyaba; Malik, Asad Waqar; Rahman, Anis Ur; Zahir, Ayesha; Fraz, Muhammad Moazam

doi:10.1177/1550147719888845

Cited by 19 publications

(10 citation statements)

References 28 publications

(37 reference statements)

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“…Zaheer et al [20] presented a VANET architecture for smart cities which allows route selection based on real world information, established from adjacent vehicle, in ad-hoc method. The study utilized VANET architecture to implement a smart transportation system.…”

Section: Prior Work On Routing Algorithms For Iovmentioning

confidence: 99%

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

Hamza¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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With recent advances made in Internet of Vehicles (IoV) and Cloud Computing (CC), the Intelligent Transportation Systems (ITS) find it advantageous in terms of improvement in quality and interactivity of urban transportation service, mitigation of costs incurred, reduction in resource utilization, and improvement in traffic management capabilities. Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation. IoV communication enables the collection and distribution of real-time essential data regarding road network condition. In this scenario, energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing. With this motivation, the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing (AI-EECR) Protocol for IoV in urban computing. The proposed AI-EECR protocol operates under three stages namely, network initialization, Cluster Head (CH) selection, and routing protocol. The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization (QCRO) algorithm. QCRO algorithm derives a fitness function with the help of vehicle speed, trust level, and energy level of the vehicle. In order to make appropriate routing decisions, a set of relay nodes was selected using Group Teaching Optimization Algorithm (GTOA). The performance of the presented AI-EECR model, in terms of energy efficiency, was validated against different aspects and a brief comparative analysis was conducted. The experimental outcomes established

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Section: Prior Work On Routing Algorithms For Iovmentioning

confidence: 99%

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

Hamza¹,

Alshahrani²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The proposed model reduces the problem of medical assistance for patients. The authors proposed an intelligent vehicle network system in [15] for smart cities, which requires ad hoc path choice based on real-time data collected from nearby vehicles. They use Android-based Wi-Fi direct-enabled smartphones as integrated devices in cars.…”

Section: Iot and Vanetsmentioning

confidence: 99%

Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL

et al. 2022

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VANETs have gained much attention from both industry and academia because of their characteristics, such as dynamic topology. There are various applications of VANETs that are classified on the basis of safety, efficiency, commercial usage, and productive areas. This paper presents an IoT-based formal model for vehicle-life integration enabling RSUs with the help of different approaches. We have developed a model that uses vehicle scenarios in smart transportation systems so that quick data transmission is provided between the source and destination vehicles. Further, fog-based RSUs provide a wide range to communicate with hospitals and emergency vehicles to deal with emergency situations. All the appropriate entities are connected to ensure a consistent traffic flow for the arrival of an emergency vehicle in emergency places. The UML, graph theory, and VDM-SL formal technique are used to represent this system. To model the network and discover appropriate paths for V2V communication, graph theory is applied. The system requirements are designed using a UML diagram. The VDM-SL, an object-oriented model-based formal technique, was utilized for this modeling procedure. This approach assures the safety and accuracy of systems by detecting flaws early in the design process. It also gives an exceptionally important answer to an issue and increases trust in the software’s quality.

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“…Intervention in the "urban transport" area focused on reducing traffic congestion [27,31], planning urban transformation [30], or increasing cities' accessibility [29]. The management of traffic flows was possible by a smarter control of traffic lights [27] or by suggesting different individual routes exploiting real-time data [31].…”

Section: Urban Health Areasmentioning

confidence: 99%

The Adoption of Digital Technologies and Artificial Intelligence in Urban Health: A Scoping Review

et al. 2022

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As more people live in cities, the impact of urban settings on population health has been increasing. One of the main strategies to cope with urbanization is adopting artificial intelligence (AI) and new digital technologies to develop new urban services that improve citizens’ health and well-being. The aim of this study is to review urban interventions and adopting digital technologies and AI-based algorithms to improve population health. A scoping review of the literature was conducted by querying MEDLINE, Web of Science, and Scopus databases. The included studies were categorized into one urban health area, suggested by the WHO, according to the type of intervention investigated. Out of 3733 records screened, 12 papers met all inclusion criteria. Four studies investigated the “outdoor and indoor pollution” area, one “climate change”, one “housing”, two “health and social services” and four “urban transport” areas. Only one article used a comprehensive approach to public health, investigating the use of AI and digital technologies both to characterize exposure conditions to health determinants and to monitor population health effects, while the others were limited to characterizing exposure conditions to health determinants, thus employing a preliminary public health perspective. From this point of view, countries should foster synergy for the development of research on digital technologies to address the determinants of health in the urban context. From a global health perspective, sharing results with the scientific community would also allow other countries to use those technologies that have been shown to be effective, paving the way for more sustainable living conditions worldwide.

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A vehicular network–based intelligent transport system for smart cities

Cited by 19 publications

References 28 publications

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL

The Adoption of Digital Technologies and Artificial Intelligence in Urban Health: A Scoping Review

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