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Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 17390

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Prof. Dr. Ansar Yasar

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Guest Editor

Transportation Research Institute (IMOB), Hasselt University, 3500 Hasselt, Belgium
Interests: ITS; vehicular networks; agent-based modeling; modeling and simulation; transportation behavior
Special Issues, Collections and Topics in MDPI journals

Dr. Fatma Outay

E-Mail Website
Guest Editor

Zayed University Dubai, United Arab Emirates
Interests: Vehicular Networks, Car2X, ITS

Special Issue Information

Dear Colleagues,

The 4th International Workshop on Connected & Intelligent Mobility (CIM 2020) (https://sites.google.com/view/cim-20/) will be held in Madeira, Portugal, November 2, 2020–November 5, 2020. Authors of papers related to sensors presented at the workshop are invited to submit extended versions of their work to this Special Issue for publication.

Since the first edition, the purpose of the 4th International Workshop on Connected & Intelligent Mobility is to discuss the recent advances in connected vehicles and bring together engineers, researchers, and practitioners interested in the advances and applications in the field of Vehicle Technology, Intelligent Transport Systems, Wireless Sensor Communications, and IoT.

Vehicle crashes on the roads and highways cause loss of lives and damage to property. Connected vehicles have been identified as a key technology for increasing road safety and transport efficiency. In recent years, vehicle infrastructure integration technology attracts a great amount of attention and it can also bring inestimable economic value, and will play an important role in the next generation of intelligent transportation systems and communication network development.

Areas of interest include, but are not limited to the following:

Vehicular Wireless Medium Access Control Routings & Protocols for Connected Vehicles.
V2V, V2I and I2V Road Safety applications
Weather related Safety solutions
Driver behavior countermeasures in connected vehicles
Data Trustworthiness in Connected Vehicles
Security & Privacy Issues in Vehicle Communication Environment
Safety & non-safety applications of Connected Vehicles
Automotive Electronics and Automatic Control in Vehicular Networks
Traffic & Transportation Systems in Vehicular Networks
Telematics and Mobile Internet
Vehicular Cloud Computing
Mobility and the Internet of Vehicles
IoT in connected Vehicles
Big Data and Vehicle Analytics
Data mining and Data analytic in next Generation of Vehicle Telematics Products
Data worthiness in connected vehicles
Sustainable transport
Driver Behavior Analysis
Vision and Image Processing
Vehicle Environment Perception
Cognitive and Context-aware Intelligence

Prof. Dr. Ansar-Ul-Haque Yasar
Dr. Fatma Outay
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

27 pages, 1750 KiB

Open AccessArticle

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

by Mohammad A. R. Abdeen, Ibrahim A. Nemer and Tarek R. Sheltami

Sensors 2021, 21(9), 3148; https://doi.org/10.3390/s21093148 - 1 May 2021

Cited by 9 | Viewed by 3343

Abstract

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods. Full article

(This article belongs to the Special Issue Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020))

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15 pages, 3753 KiB

Open AccessArticle

Automated Keratoconus Detection by 3D Corneal Images Reconstruction

by Hanan A. Hosni Mahmoud and Hanan Abdullah Mengash

Sensors 2021, 21(7), 2326; https://doi.org/10.3390/s21072326 - 26 Mar 2021

Cited by 13 | Viewed by 2391

Abstract

This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts. Full article

(This article belongs to the Special Issue Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020))

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23 pages, 1488 KiB

Open AccessArticle

RF-Based UAV Detection and Identification Using Hierarchical Learning Approach

by Ibrahim Nemer, Tarek Sheltami, Irfan Ahmad, Ansar Ul-Haque Yasar and Mohammad A. R. Abdeen

Sensors 2021, 21(6), 1947; https://doi.org/10.3390/s21061947 - 10 Mar 2021

Cited by 51 | Viewed by 8436

Abstract

Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%. Full article

(This article belongs to the Special Issue Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020))

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22 pages, 4167 KiB

Open AccessArticle

Machine Translation Utilizing the Frequent-Item Set Concept

by Hanan A. Hosni Mahmoud and Hanan Abdullah Mengash

Sensors 2021, 21(4), 1493; https://doi.org/10.3390/s21041493 - 21 Feb 2021

Cited by 3 | Viewed by 2161

Abstract

In this paper, we introduce new concepts in the machine translation paradigm. We treat the corpus as a database of frequent word sets. A translation request triggers association rules joining phrases present in the source language, and phrases present in the target language. It has to be noted that a sequential scan of the corpus for such phrases will increase the response time in an unexpected manner. We introduce the pre-processing of the bilingual corpus through proposing a data structure called Corpus-Trie (CT) that renders a bilingual parallel corpus in a compact data structure representing frequent data items sets. We also present algorithms which utilize the CT to respond to translation requests and explore novel techniques in exhaustive experiments. Experiments were performed on specific language pairs, although the proposed method is not restricted to any specific language. Moreover, the proposed Corpus-Trie can be extended from bilingual corpora to accommodate multi-language corpora. Experiments indicated that the response time of a translation request is logarithmic to the count of unrepeated phrases in the original bilingual corpus (and thus, the Corpus-Trie size). In practical situations, 5–20% of the log of the number of the nodes have to be visited. The experimental results indicate that the BLEU score for the proposed CT system increases with the size of the number of phrases in the CT, for both English-Arabic and English-French translations. The proposed CT system was demonstrated to be better than both Omega-T and Apertium in quality of translation from a corpus size exceeding 1,600,000 phrases for English-Arabic translation, and 300,000 phrases for English-French translation. Full article

(This article belongs to the Special Issue Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020))

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Selected Papers from the 4th International Workshop on Connected & Intelligent Mobility (CIM 2020)

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