The Future of India Creeping up in Building a Smart City: Intelligent Traffic Analysis Platform

Balamurugan, S.; Lilian, J. Felicia; Sasikala, S

doi:10.1109/icicct.2018.8473194

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The Internet of Things (IoT) has been recognized as an efficient mechanism to achieve city urbanization. Emerging technologies provide reliable technology for real-time data collection and sharing using controlled architecture following standard processes for highimpact applications (Khan, Pervez, & Ghafoor 2014;Li, Cao, & Yao, 2015;Maheswaran & Misra, 2015;Popescul & Radu, 2016;Pacheco & Hariri 2016;Shahat, Elragal, & Bergvall-Kåreborn, 2017;Ati & Basmaji, 2018;Alias Balamurugan, Lilian, & Sasikala 2018;Manjunatha & Annappa, 2018;Alsafery, Alturki, Reiff-Marganiec, & Jambi, 2018;Puschmann, Barnaghi, & Tafazolli, 2018;Santos et. al, 2017;Lim, Kim, & Maglio, 2018;Harris 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

A Hyperconnected Smart City Framework

Naqvi

Rehman

Islam

2020

AJIS

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Recent technological advancements have given rise to the concept of hyper-connected smart cities being adopted around the world. These cities aspire to achieve better outcomes for citizens by improving the quality of service delivery, information sharing, and creating a sustainable environment. A smart city comprises of a network of interconnected devices also known as IoT (Internet of Things), which captures data and transmits it to a platform for analysis. This data covers a variety of information produced in large volumes also known as Big Data. From data capture to processing and storage, there are several stages where a breach in security and privacy could result in catastrophic impacts. Presently there is a gap in the centralization of knowledge to implement smart city services with a secure architecture. To bridge this gap, we present a framework that highlights challenges within the smart city applications and synthesizes the techniques feasible to solve them. Additionally, we analyze the impact of a potential breach on smart city applications and state-of-the-art architectures available. Furthermore, we identify the stakeholders who may have an interest in learning about the relationships between the significant aspects of a smart city. We demonstrate these relationships through force-directed network diagrams. They will help raise the awareness amongst the stakeholders for planning the development of a smart city. To complement our framework, we designed web-based interactive resources that are available from http://ausdigitech.com/smartcity/.

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Section: Literature Reviewmentioning

confidence: 99%

A Hyperconnected Smart City Framework

Naqvi

Rehman

Islam

2020

AJIS

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“…On one hand, people's lives are improved in a variety of ways due to wide range of applications, but on the other hand, the required quality of services (QoS) also need to be ensured. In this regard, optimization of resources like computing power, sum-rate maximization, and delay minimization has been the focus of optimization problem formulations [2,3]. Meanwhile, simple convex optimization also suffices as one of the basic scenarios to fulfil these aims.…”

Section: Introductionmentioning

confidence: 99%

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

Alsulami

Serbaya

Abualsauod

et al. 2022

Wireless Communications and Mobile Computing

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The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new federated deep reinforcement learning- (FDRL-) based vehicle communication method is presented as a new insight. Finally, a UAV-aided vehicular communication system based on FDRL-based UAVs is proposed as a novel resource management technique to optimize 5G and 6G quality of services.

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“…It allows numerous different applications to make lives easier, smoother, and more comfortable. In the intelligent transportation system (ITS), autonomous driving, and smart city areas, 5G is expected to improve safety, increase experience comport, reduce traffic congestion, and lower air pollution [2]. Vehicles will be highly connected with the aid of the ubiquitous wireless network at anytime and anywhere [3].…”

Section: Introductionmentioning

confidence: 99%

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Nguyen

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety-critical real-time system. To guarantee low-latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.

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The Future of India Creeping up in Building a Smart City: Intelligent Traffic Analysis Platform

Cited by 4 publications

References 8 publications

A Hyperconnected Smart City Framework

A Hyperconnected Smart City Framework

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

DRL‐Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

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