Research

16 pages, 9986 KiB

Open AccessArticle

An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

by Ahmed Dirir, Henry Ignatious, Hesham Elsayed, Manzoor Khan, Mohammed Adib, Anas Mahmoud and Moatasem Al-Gunaid

Future Internet 2021, 13(12), 306; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13120306 - 30 Nov 2021

Cited by 4 | Viewed by 3272

Abstract

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the [...] Read more.

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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19 pages, 1086 KiB

Open AccessArticle

Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles

by Eiman ElGhanam, Ibtihal Ahmed, Mohamed Hassan and Ahmed Osman

Future Internet 2021, 13(10), 257; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13100257 - 08 Oct 2021

Cited by 13 | Viewed by 2913

Abstract

Dynamic wireless charging (DWC) is a promising technology to charge Electric Vehicles (EV) using on-road charging segments (CS), also known as DWC pads. In order to ensure effective utilization of this on-the-road charging service, communication and coordination need to be established between the [...] Read more.

Dynamic wireless charging (DWC) is a promising technology to charge Electric Vehicles (EV) using on-road charging segments (CS), also known as DWC pads. In order to ensure effective utilization of this on-the-road charging service, communication and coordination need to be established between the EVs and the different network entities, thereby forming an Internet of Electric Vehicles (IoEV). In an IoEV, EVs can utilize different V2X communication modes to enable charging scheduling, load management, and reliable authentication and billing services. Yet, designing an authentication scheme for dynamic EV charging presents significant challenges given the mobility of the EVs and the short contact time between the EVs and the charging segments. Accordingly, this work proposes a fast, secure and lightweight authentication scheme that allows only authentic EVs with valid credentials to charge their batteries while ensuring secure and fair payments. The presented scheme starts with a key pre-distribution phase between the charging service company (CSC) and the charging pad owner (PO), followed by a hash chain and digital signature-based registration and authentication phase between the EV and the CSC, before the EV reaches the beginning of the charging lane. These preliminary authentication phases allow the authentication between the EVs and the charging segments to be performed using simple hash key verification operations prior to charging activation, which reduces the computational cost of the EVs and the CS. Symmetric and asymmetric key cryptography are utilized to secure the communication between the different network entities. Analysis of the computational and transmission time requirements of the proposed authentication scheme shows that, for an EV traveling at 60 km/h to start charging at the beginning of the charging lane, the authentication process must be initiated at least

1.35

m ahead of the starting point of the lane as it requires ≃81 ms to be completed. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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13 pages, 3120 KiB

Open AccessArticle

Reader–Tag Commands via Modulation Cutoff Intervals in RFID Systems

by Abdallah Y. Alma’aitah and Mohammad A. Massad

Future Internet 2021, 13(9), 235; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13090235 - 16 Sep 2021

Viewed by 1577

Abstract

Radio frequency identification (RFID) technology facilitates a myriad of applications. In such applications, an efficient reader–tag interrogation process is crucial. Nevertheless, throughout reader–tag communication, significant amounts of time and power are consumed on inescapable simultaneous tag replies (i.e., collisions) due to the lack [...] Read more.

Radio frequency identification (RFID) technology facilitates a myriad of applications. In such applications, an efficient reader–tag interrogation process is crucial. Nevertheless, throughout reader–tag communication, significant amounts of time and power are consumed on inescapable simultaneous tag replies (i.e., collisions) due to the lack of carrier sensing at the tags. This paper proposes the modulation cutoff intervals (MCI) process as a novel reader–tag interaction given the lack of carrier sensing constraints in passive RFID tags. MCI is facilitated through a simple digital baseband modulation termination (DBMT) circuit at the tag. DBMT detects the continuous-wave cutoff by the reader. In addition, DBMT provides different flags based on the duration of the continuous-wave cutoff. Given this capability at the tag, the reader cuts off its continuous-wave transmission for predefined intervals to indicate different commands to the interrogated tag(s). The MCI process is applied to tag interrogation (or anti-collision) and tag-counting protocols. The MCI process effect was evaluated by the two protocols under high and low tag populations. The performance of such protocols was significantly enhanced with precise synchronization within time slots with more than 50% and more than 55.6% enhancement on time and power performance of anti-collision and counting protocols, respectively. Through the MCI process, fast and power-efficient tag identification is achieved in inventory systems with low and high tag mobility; alternatively, in addition to the rapid and power efficient interaction with tags, anonymous tag counting is conducted by the proposed process. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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18 pages, 1611 KiB

Open AccessArticle

Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

by Taghreed Alghamdi, Khalid Elgazzar and Taysseer Sharaf

Future Internet 2021, 13(9), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13090225 - 30 Aug 2021

Cited by 1 | Viewed by 1891

Abstract

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the [...] Read more.

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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16 pages, 791 KiB

Open AccessArticle

Cascaded κ-μ Fading Channels with Colluding and Non-Colluding Eavesdroppers: Physical-Layer Security Analysis

by Deemah Tashman and Walaa Hamouda

Future Internet 2021, 13(8), 205; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13080205 - 04 Aug 2021

Cited by 7 | Viewed by 2148

Abstract

In this paper, the physical-layer security for a three-node wiretap system model is studied. Under the threat of multiple eavesdroppers, it is presumed that a transmitter is communicating with a legitimate receiver. The channels are assumed to be following cascaded

κ

-

μ

[...] Read more.

In this paper, the physical-layer security for a three-node wiretap system model is studied. Under the threat of multiple eavesdroppers, it is presumed that a transmitter is communicating with a legitimate receiver. The channels are assumed to be following cascaded

κ

-

μ

fading distributions. In addition, two scenarios for eavesdroppers’ interception and information-processing capabilities are investigated: colluding and non-colluding eavesdroppers. The positions of these eavesdroppers are assumed to be random in the non-colluding eavesdropping scenario, based on a homogeneous Poisson point process (HPPP). The security is examined in terms of the secrecy outage probability, the probability of non-zero secrecy capacity, and the intercept probability. The exact and asymptotic expressions for the secrecy outage probability and the probability of non-zero secrecy capacity are derived. The results demonstrate the effect of the cascade level on security. Additionally, the results indicate that as the number of eavesdroppers rises, the privacy of signals exchanged between legitimate ends deteriorates. Furthermore, in this paper, regarding the capabilities of tapping and processing the information, we provide a comparison between colluding and non-colluding eavesdropping. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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17 pages, 3804 KiB

Open AccessArticle

Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

by Umar Iqbal, Ashraf Abosekeen, Jacques Georgy, Areejah Umar, Aboelmagd Noureldin and Michael J. Korenberg

Future Internet 2021, 13(8), 191; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13080191 - 26 Jul 2021

Cited by 8 | Viewed by 2902

Abstract

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with [...] Read more.

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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12 pages, 1039 KiB

Open AccessArticle

Multi-Angle Lipreading with Angle Classification-Based Feature Extraction and Its Application to Audio-Visual Speech Recognition

by Shinnosuke Isobe, Satoshi Tamura, Satoru Hayamizu, Yuuto Gotoh and Masaki Nose

Future Internet 2021, 13(7), 182; https://0-doi-org.brum.beds.ac.uk/10.3390/fi13070182 - 15 Jul 2021

Cited by 8 | Viewed by 2128

Abstract

Recently, automatic speech recognition (ASR) and visual speech recognition (VSR) have been widely researched owing to the development in deep learning. Most VSR research works focus only on frontal face images. However, assuming real scenes, it is obvious that a VSR system should [...] Read more.

Recently, automatic speech recognition (ASR) and visual speech recognition (VSR) have been widely researched owing to the development in deep learning. Most VSR research works focus only on frontal face images. However, assuming real scenes, it is obvious that a VSR system should correctly recognize spoken contents from not only frontal but also diagonal or profile faces. In this paper, we propose a novel VSR method that is applicable to faces taken at any angle. Firstly, view classification is carried out to estimate face angles. Based on the results, feature extraction is then conducted using the best combination of pre-trained feature extraction models. Next, lipreading is carried out using the features. We also developed audio-visual speech recognition (AVSR) using the VSR in addition to conventional ASR. Audio results were obtained from ASR, followed by incorporating audio and visual results in a decision fusion manner. We evaluated our methods using OuluVS2, a multi-angle audio-visual database. We then confirmed that our approach achieved the best performance among conventional VSR schemes in a phrase classification task. In addition, we found that our AVSR results are better than ASR and VSR results. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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15 pages, 4823 KiB

Open AccessArticle

An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers

by Yanjun Shi, Yijia Guo, Lingling Lv and Keshuai Zhang

Future Internet 2020, 12(10), 172; https://0-doi-org.brum.beds.ac.uk/10.3390/fi12100172 - 15 Oct 2020

Cited by 1 | Viewed by 2225

Abstract

The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. [...] Read more.

The fast development of connected vehicles with support for various V2X (vehicle-to-everything) applications carries high demand for quality of edge services, which concerns microservice deployment and edge computing. We herein propose an efficient resource scheduling strategy to containerize microservice deployment for better performance. Firstly, we quantify three crucial factors (resource utilization, resource utilization balancing, and microservice dependencies) in resource scheduling. Then, we propose a multi-objective model to achieve equilibrium in these factors and a multiple fitness genetic algorithm (MFGA) for the balance between resource utilization, resource utilization balancing, and calling distance, where a container dynamic migration strategy in the crossover and mutation process of the algorithm is provided. The simulated results from Container-CloudSim showed the effectiveness of our MFGA. Full article

(This article belongs to the Special Issue Selected Papers from the International Conference on Communications, Signal Processing and Their Applications (ICCSPA ’20))

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