Wireless Communication Optimization in Optical Imaging and Sensing for Connected and Autonomous Vehicles Chain Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 6 June 2025 | Viewed by 2662

Special Issue Editor

Department of Electrical Engineering, University of Mississippi, University, MS 38677, USA
Interests: error statistics; Rayleigh channels; amplify and forward communication; cooperative communication; fading channels; orthogonal codes
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Special Issue Information

Dear Colleagues,

With advances in automated and connected driving, secure communication is increasingly becoming a safety-critical function. The injection of manipulated radio messages into traffic could cause severe accidents in the foreseeable future, and can currently be achieved without having to manipulate on-board vehicle systems directly, for example by hijacking cell phones instead and using these as senders. Thereby, large-scale attacks on vehicles can be executed remotely, and relatively vulnerable devices can be targeted.

Advances in communication, controls and embedded systems now pave the way for connected and autonomous vehicles (CAVs), which use sensors and software to control, navigate and drive vehicles to a predetermined destination, without any human intervention.

Autonomous vehicles could increase car utilization by 5%–75%, thus reducing CO2 emissions becomes critical, as does saving fuel and time, reducing congestion, parking utilization and even accidents.

Autonomous vehicles with optical imaging and sensing technologies incorporate safety systems such as lane departure warning systems, sign detection systems, parking assistance, collision avoidance and accident recorders. Radars, lidars, sensors and cameras are the key components in the development of self-driving cars. Radars lag in the detection of the exact size and shape of an object; meanwhile, for lidars, although they are more accurate than radars, the detection distance is critical. Lidars help in generating 3D point maps around the vehicle. These components provide the necessary information about the environment around the vehicle in real time.

This Special Issue aims to present recent advances in wireless communications in optical imaging and sensing for autonomous vehicles. This is an opportunity to gather researchers to develop fundamental principles and discuss and share original research works and practical experiences. The scope includes, but is not limited to:

  • Optimization of optical imaging and sensing in V2X communication;
  • Wireless communication in vehicle optics and chain management;
  • Optimization intelligent CAVs;
  • Millimeter-wave communication for internet management of vehicles;
  • Optical camera communication systems for intelligent internet of vehicles;
  • Wireless sensor fusion technology in CAVs;
  • CAV environment management;
  • Optical communication in underwater connected and autonomous vehicles;
  • AI and machine intelligence for optical imaging and sensing-enabled CAVs.

Prof. Dr. Mustafa M. Matalgah
Guest Editor

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Published Papers (3 papers)

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Research

15 pages, 2023 KiB  
Article
Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm
Appl. Sci. 2024, 14(4), 1670; https://doi.org/10.3390/app14041670 - 19 Feb 2024
Viewed by 339
Abstract
Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process [...] Read more.
Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm. Full article
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13 pages, 859 KiB  
Article
Enhancing Self-Care Prediction in Children with Impairments: A Novel Framework for Addressing Imbalance and High Dimensionality
Appl. Sci. 2024, 14(1), 356; https://doi.org/10.3390/app14010356 - 30 Dec 2023
Viewed by 585
Abstract
Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children’s healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, [...] Read more.
Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children’s healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry. Full article
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13 pages, 2268 KiB  
Article
Human Activity Detection Using Smart Wearable Sensing Devices with Feed Forward Neural Networks and PSO
Appl. Sci. 2023, 13(6), 3716; https://doi.org/10.3390/app13063716 - 14 Mar 2023
Cited by 1 | Viewed by 1078
Abstract
Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating [...] Read more.
Hospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate. Full article
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