List of Titles

SPEED: A deep learning assisted privacy-preserved framework for intelligent transportation systems

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza

Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza
Date: 2021
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4376-4384
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Description: Roadside cameras in an Intelligent Transportation System (ITS) are used for various purposes, e.g., monitoring the speed of vehicles, violations of laws, and detection of suspicious activities in parking lots, streets, and side roads. These cameras generate big multimedia data, and as a result, the ITS faces challenges like data management, redundancy, and privacy breaching in end-to-end communication. To solve these challenges, we propose a framework, called SPEED, based on a multi-level edge computing architecture and machine learning algorithms. In this framework, data captured by end-devices, e.g., smart cameras, is distributed among multiple Level-One Edge Devices (LOEDs) to deal with data management issue and minimize packet drop due to buffer overflowing on end-devices and LOEDs. The data is forwarded from LOEDs to Level-Two Edge Devices (LTEDs) in a compressed sensed format. The LTEDs use an online Least-Squares Support-Vector Machines (LS-SVMs) model to determine distribution characteristics and index values of compressed sensed data to preserve its privacy during transmission between LTEDs and High-Level Edge Devices (HLEDs). The HLEDs estimate the redundancy in forwarded data using a deep learning architecture, i.e., a Convolutional Neural Network (CNN). The CNN is used to detect the presence of moving objects in the forwarded data. If a movement is detected, the data is forwarded to cloud servers for further analysis otherwise discarded. Experimental results show that the use of a multi-level edge computing architecture helps in managing the generated data. The machine learning algorithms help in addressing issues like data redundancy and privacy-preserving in end-to-end communication. © 2000-2011 IEEE.

Multi-variate data fusion technique for reducing sensor errors in intelligent transportation systems

- Manogaran, Gunasekaran, Balasubramanian, Venki, Rawal, Bharat, Saravanan, Vijayalakshmi, Montenegro-Marin, Carlos

Authors: Manogaran, Gunasekaran , Balasubramanian, Venki , Rawal, Bharat , Saravanan, Vijayalakshmi , Montenegro-Marin, Carlos
Date: 2021
Type: Text , Journal article
Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15564-15573
Full Text: false
Reviewed:
Description: Connected vehicles in intelligent transportation system (ITS) scenario rely on environmental data for supporting user-centric applications along the driving time. Sensors equipped in the vehicles are responsible for accumulating data from the environment, followed by the fusion process. Such fusion process provides accurate and stable data required for the applications in a recurrent manner. In order to enhance the data fusion of connected vehicles, this article introduces multi-variate data fusion (MVDF) technique. This technique is competent in handling asynchronous and discrete data from the environment and streamlining them into continuous and delay-less inputs for the applications. The process of data fusion is aided through least square regression learning to determine the errors in different time instances. The indefinite and definite data fusion instances are differentiated using this regression model to identify the errors in fore-hand. Besides, the differentiation relies on the application run-time interval to progress data fusion within the same or extended time instance and data slots. In this manner the differentiation along with the error identification is regular until the application required data is met. The performance of this technique is verified using network simulator experiments for the metrics error, data utilization ratio, and computation time. The results show that this technique improves data utilization under controlled time and fewer errors. © 2001-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.

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Smart dynamic traffic monitoring and enforcement system

- El-Hansali, Youssef, Outay, Fatma, Yasar, Ansar, Farrag, Siham, Shoaib, Muhammad, Imran, Muhammad, Awan, Hammad

Authors: El-Hansali, Youssef , Outay, Fatma , Yasar, Ansar , Farrag, Siham , Shoaib, Muhammad , Imran, Muhammad , Awan, Hammad
Date: 2021
Type: Text , Journal article
Relation: Computers, Materials and Continua Vol. 67, no. 3 (2021), p. 2797-2806
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Description: Enforcement of traffic rules and regulations involves a wide range of complex tasks, many of which demand the use of modern technologies. variable speed limits (VSL) control is to change the current speed limit according to the current traffic situation based on the observed traffic conditions. The aim of this study is to provide a simulation-based methodological framework to evaluate (VSL) as an effective Intelligent Transportation System (ITS) enforcement system. The focus of the study is on measuring the effectiveness of the dynamic traffic control strategy on traffic performance and safety considering various performance indicators such as total travel time, average delay, and average number of stops. United Arab Emirates (UAE) was selected as a case study to evaluate the effectiveness of this strategy. A micro simulation software package VISSIM with add-on module VisVAP is used to evaluate the impacts of VSL. It has been observed that VSL control strategy reduced the average delay time per vehicle to around 7%, travel time by 3.2%, and number of stops by 48.5%. Dynamic traffic control strategies also alleviated congestion by increasing the capacity of the bottleneck section and improving safety. Results of this study would act as a guidance for engineers and decision makers to new traffic control system implementation. © 2021 Tech Science Press. All rights reserved.

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