Vehicle Ad-hoc Network (VANET) is a type of wireless network that enables communication between vehicles and Road Side Units (RSUs) to improve road safety, traffic efficiency, and service delivery. However, the widespread use of vehicular networks raises serious concerns about users’ privacy and security. Privacy in VANET refers to the protection of personal information and data exchanged between vehicles, RSUs, and other entities. Privacy issues in VANET include unauthorized access to location and speed information, driver and passenger identification, and vehicle tracking. To ensure privacy in VANET, various technologies such as pseudonymization, message authentication, and encryption are employed. When vehicles frequently change their identity to avoid tracking, message authentication ensures messages are received from trusted sources, and encryption is used to prevent unauthorized access to messages. Therefore, researchers have presented various schemes to improve and enhance the privacy efficiency of vehicle networks. This survey article provides an overview of privacy issues as well as an in-depth review of the current state-of-the-art pseudonym-changing tactics and methodologies proposed.
Path planning is an essential concern in robotic systems, and it refers to the process of determining a safe and optimal path starting from the source state to the goal one within dynamic environments. We proposed an improved path planning method in this article, which merges the Dijkstra algorithm for path planning with Potential Field (PF) collision avoidance. In real-time, the method attempts to produce multiple paths as well as determine the suitable path that’s both short and reliable (safe). The Dijkstra method is employed to produce multiple paths, whereas the Potential Field is utilized to assess the safety of each route and choose the best one. The proposed method creates links between the routes, enabling the robot to swap between them if it discovers a dynamic obstacle on its current route. Relating to path length and safety, the simulation results illustrate that Dynamic Dijkstra-Potential Field (Dynamic D-PF) achieves better performance than the Dijkstra and Potential Field each separately, and going to make it a promising solution for the application of robotic automation within dynamic environments.
The phenomenal rise of the Internet in recent years, as well as the expansion of capacity in today’s networks, have provided both inspiration and incentive for the development of new services that combine phone, video, and text ”over IP.” Although unicast communications have been prevalent in the past, there is an increasing demand for multicast communications from both Internet Service Providers (ISPs) and content or media providers and distributors. Indeed, multicasting is increasingly being used as a green verbal exchange mechanism for institution-oriented programmers on the Internet, such as video conferencing, interactive college games, video on demand (VoD), TV over the Internet, e-learning, software programme updates, database replication, and broadcasting inventory charges. However, the lack of security within the multicast verbal exchange model prevents the effective and large-scale adoption of such important company multi-celebration activities. This situation prompted a slew of research projects that addressed a variety of issues related to multicast security, including confidentiality, authentication, watermarking, and access control. These issues should be viewed within the context of the safety regulations that work in the specific conditions. For example, in a public inventory charge broadcast, while identification is a vital necessity, secrecy is not. In contrast, video-convention programme requires both identification and confidentiality. This study gives a complete examination and comparison of the issues of group key management. Both network-dependent and network-independent approaches are used. The study also addresses the advantages, disadvantages, and security problems of various protocols.
in recent years popularity of smart Home has been increasing due to low price and simplicity through tablet and Smartphone connectivity. It is an automation of house or home activity. Raspberry Pi3 is a small computer with digital input output capability and it was introduced in 2016; input/output ability besides the availability of all computer features make this system very suitable to be central unit can for smart home. Smart Home may contain centralize controller which control heating, lightning, ventilation in the home, HAVC( Heating, Ventilation and air conditioning),Safety locks of gates, doors and other system to provide improve comfort, better energy efficiency and security. The aim of this Paper is to develop a smart home application using RPi3, wemose-d1 and GSM. Programming has been developed in C++ in wemose-d1 and Python environment for RPi3 operation. The MQTT (Message Queuing Telemetry Transport protocol) technologic used to connect between raspberry pi3 and nodes.
The smart classroom is a fully automated classroom where repetitive tasks, including attendance registration, are automatically performed. Due to recent advances in artificial intelligence, traditional attendance registration methods have become challenging. These methods require significant time and effort to complete the process. Therefore, researchers have sought alternative ways to accomplish attendance registration. These methods include identification cards, radio frequency, or biometric systems. However, all of these methods have faced challenges in safety, accuracy, effort, time, and cost. The development of digital image processing techniques, specifically face recognition technology, has enabled automated attendance registration. Face recognition technology is considered the most suitable for this process due to its ability to recognize multiple faces simultaneously. This study developed an integrated attendance registration system based on the YOLOv7 algorithm, which extracts features and recognizes students’ faces using a specially collected database of 31 students from Mustansiriyah University. A comparative study was conducted by applying the YOLOv7 algorithm, a machine learning algorithm, and a combined machine learning and deep learning algorithm. The proposed method achieved an accuracy of up to 100%. A comparison with previous studies demonstrated that the proposed method is promising and reliable for automating attendance registration.
A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high e ffi ciency of transportation, the convenience of manoeuvring ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.
Aircraft detection is a vital and significant field within object detection that has garnered considerable attention from academics, particularly following the advancement of deep learning methods. Aircraft detection has recently become widely utilized in several civil and military fields. This comprehensive survey meticulously categorizes and evaluates diverse deep learning methodologies in airplane detection research. Encompassing radar-based, image-based, and multimodal approaches, the paper presents a structured framework to enhance understanding of the evolving research landscape within this domain. The survey critically identifies gaps and discerns emerging trends, offering valuable insights into standard datasets of aircraft images, performance metrics, real-world applications, and challenges and limitations encountered by aircraft detection systems. Its potential contributions are underscored as pivotal for advancing the safety and security of air travel. This research paper is the inaugural publication of its kind in the domain of aircraft detection review papers, establishing itself as an all-encompassing reference for subsequent scholars.
In recent years, self-driving cars and reducing the number of accident casualties have drawn a lot of attention. Although it is crucial to increase driver awareness on the road, autonomous vehicles can emulate human driving and guarantee improved levels of road safety. Artificial intelligence (AI) technologies are often employed for this purpose. However, deep learning, a subset of AI, is prone to numerous errors, a wide range of threats, and needs to handle vast amounts of data, which imposes high-performance hardware requirements. This study suggests a deep learning model for object recognition that employs characteristics to describe data rather than images. Our model employs the COCO dataset as the training foundation, and it was suggested that the features be retrieved using the principal component analysis PCA extraction method. The current results demonstrate the efficacy and precision of our model, with an accuracy of 99.96 %. Furthermore, the performance indices, i.e., recall, precision, and F1-score, achieved about 1 for most of the COCO classes in training phase and promising results in testing phase.
Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people's health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.
In recent years, Vehicular Ad-Hoc Networks (VANETs) innovation has been regarded as a significant research area. This is owing to the increasing popularity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications in the area of Intelligent Transportation System (ITS) to improve traffic management, safety, CO2 emission mitigation, and other applications. A variety of routing protocols for VANETs are being recently developed. More specifically, geographic-based routing algorithms such as Greedy Perimeter Stateless Routing (GPSR) have provoked the most interest in VANETs due to their compatibility with a regularly changing network structure and the highly unsteady nature of automobile nodes. This paper proposes an efficient weight based mobility method in VANET to improve the mechanism of the GPSR protocol through optimizing the greedy forwarding strategy; which is so called O-Greedy Mode. Therefore, the key goal is to achieve the optimal data forwarding paths. The next hop is determined by estimating the neighbors’ mobility based on each neighbor’s Greedy Link Weight Factor (GLWF). The Weighted GPSR (W-GPSR) based on Mobility Prediction is then evaluated using OMNeT++ simulator with Inet, Veins and SUMO traffic simulator. The results demonstrate the efficiency of W-GPSR in contrast with the traditional existing protocols for essential metrics of Packet Delivery Ratio (PDR), throughput, End-to-End Delay (E2ED), Normalized Routing Load (NRL) and Packet Loss Ratio (PLR).
Over the previous decade, significant research has been conducted in the field of healthcare services and their technological advancement. To be more precise, the Internet of Things (IoT) has demonstrated potential for connecting numerous medical devices, sensors, and healthcare professionals in order to deliver high-quality medical services in remote locations. This has resulted in an increase in patient safety, a decrease in healthcare expenses, an increase in the healthcare services' accessibility, and an increase in the industry's healthcare operational efficiency. This paper provides an overview of the possible healthcare uses of Internet of Things (IoT)-based technologies. The evolution of the HIoT application has been discussed in this article in terms of enabling technology, services of healthcare, and applications for resolving different healthcare challenges. Additionally, effort difficulties and drawbacks with the HIoT system are explored. In summary, this study provides a complete source of information on the many applications of HIoT together the purpose is to help future academics who are interested in working in the field and making advances gain knowledge into the issue.
License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications, including parking management, vehicle access control, traffic control, and law enforcement. This project aims to provide a robust and dependable method for detecting license plates that will outperform existing approaches in accuracy and dependability. This observation method uses contemporary technology to address challenging troubles related to license plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established model for picture item detection. The novel thing, even though, is how Gray Wolf Optimization—which draws notion from the searching conduct of gray wolves—is mixed with the Faster R-CNN network. The accuracy is greatly improved by this synergistic combination, which also improves detection abilities. Moreover, an improved ResNet-50 model is blanketed to improve the classification system similarly, ensuring accurate license plate detection in several situations. The extensively utilized ”car license plate detection” dataset is used to assess the recommended technology very well, confirming its efficacy in practical settings. The empirical outcomes show exceptional performance, with a median precision of 98.21%, demonstrating how nicely the hybrid method works to attain the very best stage of license plate detecting accuracy. This painting establishes a new benchmark in license plate identity using cutting-edge technology and innovative techniques, starting the door for enhanced safety and surveillance.
This paper presents a simple and fast design and implementation for a soft robot arm. The proposed continuum arm has been built by a single self-bending contraction actuator (SBCA) with two-fingers soft gripper. Because of the valuable advantages of the pneumatic artificial muscle (PAM), this continuum arm provides a high degree of safety to individuals. The proposed soft robot arm has a bending behaviour of more 180° at 3.5 kg, while, its weight is 0.7 kg. Moreover, it is designed to assist the people by reducing the number of backbends and that leads to a decrease in the possibility of lower back pain.