Analysing Vehicular Congestion Scenario in Kuala Lumpur Using Open Traffic

Ali, Muhammad Faizan; Manogaran, Saargunawathy; Yusof, Kamaludin Mohamad; Suhaili, Muhammad Ramdhan Muhammad

doi:10.11591/ijeecs.v10.i3.pp875-882

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Intelligent Transportation System (ITS) provides the solutions for these issues by congestion charging, smart parking system, environment friendly vehicles, internet of things based sensors support system in vehicles [22]- [24]. The studied articles encompass wide variety of ITS technologies that provide the solutions for smart mobility.…”

Section: Resultsmentioning

confidence: 99%

Reviewing the Components of Technology Acceptance Behavior in Transportation Sector

Ahmed¹,

Hizam²,

Sentosa³

2022

Communications

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ResumeTechnology integration in transportation enhances the efficiency towards urban mobility management. The effective implementation of such technologies requires exploration of vital factors of acceptance and use. This study synthesizes a review work on the road-transport technologies' literature from 2011 to 2021 by contemplating the adoption mechanism keywords such as "acceptance", "usability", "adoption" of technology in transportation, mobility and numerous transport technologies through Boolean operators. Total of 42 articles from 20 developed and developing countries are enlisted for analysis. Numerous Information System (IS) theories and behavioral dimensions are emanated from surveyed literature. Avoid-Shift-Improve (ASI) framework is also materialized to understand the inferences of reviewed articles. Study conclusions are debated for implications of transport technologies from citizens' perspective.

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Section: Resultsmentioning

confidence: 99%

Reviewing the Components of Technology Acceptance Behavior in Transportation Sector

Ahmed¹,

Hizam²,

Sentosa³

2022

Communications

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“…If job i is finished after the expected time, its tardiness is found by Equation 10. These are taken from [31].…”

Section: Ge(tc) = Number Of Jobs T That Controller C Executes (7)mentioning

confidence: 99%

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Lanka,

Kandasamy

2023

IJRITCC

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Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study.

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“…The traffic evolution during a day implies variations on the number of vehicles and the vehicle density distribution. For example, the roads and places solicited during the business days are different from the week-end ones, as shown in the traffic analysis in Reference [34]. These variations mainly affect two factors, namely: (i) the controller load (in number of controlled nodes (vehicles)), and (ii) the number of nodes being far from their controller, as shown in right graph.…”

Section: Main Limitations Of Static Placement In Sdvnmentioning

confidence: 99%

Towards Dynamic Controller Placement in Software Defined Vehicular Networks

Toufga

Abdellatif

Assouane

et al. 2020

Sensors

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The emerging SDVN (Software Defined Vehicular Network) paradigm promises to bring flexibility and efficient resource utilization to vehicular networks, enabling the emergence of novel Intelligent Transportation Services. However, as it was initially designed with wired network in mind, applying the SDN paradigm to a vehicular context faces new challenges related to the peculiar characteristics of this network (high node mobility and node density, and the presence of wireless links). In this paper, we focus on one of the critical architectural elements of SDVN, namely, the SDN Controller Placement, and promote the use of dynamic placement methods that take into account the dynamicity of vehicular networks' topology. We also describe the different approaches towards a dynamic controller placement and also propose an ILP (Integer Linear Programming) based dynamic placement method that adaptively readjusts the number and placement of controllers according to road traffic fluctuations. The proposed method is evaluated using a realistic traffic trace from Luxembourg City. Simulation results show that our approach outperforms the static approach as proposed in the literature.Most existing research work have been elaborated on the opportunities that the SDVN concept could bring when applied to vehicular networks [10]. However, some challenges need to be addressed to reach these expected opportunities. Among these challenges, we mention firstly, the network topology discovery, which is a crucial service in the Software Defined Vehicular Netowrk (SDVN) system [11]. Its main goal is to build and maintain an up-to-date view of the underlying network. The density and high mobility of vehicles make the design of this service a challenging task. Secondly, the connectivity between the vehicles and their controller is partly wireless [12]. In fact, vehicles can go through areas without network coverage, this could make the controller temporarily unreachable, which may impact network performance. Thirdly, the mobility of vehicles can result in frequent changes of their attached SDN controller. This impacts the established sessions with the initial controller, as well as an increase of network overhead due to inter-controller vehicles information exchange. The SDN controller placement is a key architectural element that can help mitigating the above mentioned challenges. It consists of computing the number of SDN controllers to deploy and derive their placement in the network. This choice must be done appropriately given the critical role of an SDN controller in this architecture. First, wireless link connectivity and quality must be considered in order to minimize the connectivity losses between vehicles and their controllers. Second, the SDN Controller coverage should be defined carefully taking into account the density and mobility of vehicles. This helps avoiding SDN controllers' overload as well as improving the performance of vehicle-to-controller communications, and also, minimizing the number of changes in the set of deploy...

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Analysing Vehicular Congestion Scenario in Kuala Lumpur Using Open Traffic

Cited by 5 publications

References 2 publications

Reviewing the Components of Technology Acceptance Behavior in Transportation Sector

Reviewing the Components of Technology Acceptance Behavior in Transportation Sector

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Towards Dynamic Controller Placement in Software Defined Vehicular Networks

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