Vehicular Network Spectrum Allocation Using Hybrid NOMA and Multi-agent Reinforcement Learning
Springer
May 09, 2023
The recent years have seen a proven impact of the reinforcement learning use in many applications which showed tremendous success in solving many decision-making paradigms in machine learning. Most of the successful applications involves the existence of more than one agent, which makes it fall into the multi-agent category, taking autonomous driving as an example of these applications. We know that today’s Internet of Vehicles (IoVs) consists of multi-communication patterns which work efficiently in keeping all the IoV network components connected. With regards to sharing the frequency spectrum, applying Non-Orthogonal Multiple Access (NOMA) communication built over deep deterministic policies gradients (DDPG) scheme to cope with the rabid erratic channels conditions due to fast mobility nature of vehicles network has proven promising results. In this paper the framework of NOMA communication-based DDPG and multiple agent reinforcement learning approach (MARL) are discussed in brief, and then, the performance evaluation of DDPG scheme compared with MARL and random spectrum allocation approaches for vehicular network spectrum and resources allocation is analysed.KeywordsSpectrum allocationV2V communicationsHybrid NOMAReinforcement LearningMARLDDPGRandom allocation
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Tags: AI
Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends
Hindawe
April 15, 2022
Recently, artificial intelligence (AI) technology has great attention in transportation systems, which led to the emergence of a new concept known as Internet of Vehicles (IoV). The IoV has been associated with the IoT revolution and has become an active field of research due to the great need, in addition to the increase in the various applications of vehicle communication. AI provides unique solutions to enhance the quality of services (QoS) and performance of IoV systems as well. In this paper, some concepts related to deep learning networks will be discussed as one of the uses of machine learning in IoV systems, in addition to studying the effect of neural networks (NNs) and their types, as well as deep learning mechanisms that help in processing large amounts of unclassified data. Moreover, this paper briefly discusses the classification and clustering approaches in predicative analysis and reviews their abilities to enhance the performance of IoV application systems.
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Tags: AI, Autonomous Vehicles
Robotics architectures based machine learning and deep learning approaches
IEEE
January 05, 2022
8th International Conference on Mechatronics Engineering (ICOM 2022)
Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications.
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Tags: AI, Autonomous Vehicles, Robotics
XAI applications in autonomous vehicles
CRC Press
June 06, 2024
Artificial Intelligence (AI) has been improving and widely spreading to be an integral part of many applications, including IoT, IoV, and smart cities, among other known applications. Traditional AI has a complicated nature when attempting to create models to perform a certain task. Explainable AI (XAI) has been introduced to overcome the model complexity by adding more clarity in understanding the complexity of the model under consideration, which leads to performance improvement. The integration of the Internet of Things (IoT) and Intelligent Transport Systems (ITS) has come about because of the growth of AI technologies, and IoT is a promising technology that is leading to improved services in autonomous vehicles. XAI implementation with autonomous vehicles will improve user acceptance and trust by employing XAI models in intrusion detection and improvement of the overall security of the autonomous vehicles system. XAI will add value to the overall performance of the system to improve road safety and V2X service to develop machine learning (ML) models, which generate more explainable techniques, such as Layer-Wise Relevance Propagation (LRP), which is an advancement of the traditional Convolutional Neural Networks (CNNs). As an emerging ML technology, XAI has shown promising results in enhancing the overall performance of autonomous vehicles by elevating safety and reliability in addition to user trust and acceptance.
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Tags: AI, Autonomous Vehicles
Machine Learning and Deep Learning Approaches for Robotics Applications
Springer
May 01, 2023
Robotics plays a significant part in raising the standard of living. With a variety of useful applications in several service sectors, such as transportation, manufacturing, and healthcare. In order to make these services useable with efficacy and efficiency in having robotics obey the directions supplied to them by the program, continuous improvement is required. Intensive research has been focusing on the way to improve these services which has led to the use of sub-sections of artificial intelligence represented by ML and DL with their state-of-the-art algorithms and architecture adding positive improvements to the field of robotics. Recent studies prove various ML/DL algorithms for robotic system architectures to offer solutions for different issues related to, robotics autonomy, and decision making. This chapter provides a thorough review about autonomous and automatic robotics along with their uses. Additionally, the chapter discusses extensive machine learning techniques such as machine learning for robotics. And finally, a discussion about the issues and future of artificial intelligence applications in robotics.KeywordsRobotics applicationsMachine learningDeep learningVisioning applicationsAssistive technologyImitation learningSoft robotics
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Tags: AI, Robotics
Data-Driven Techniques for Intrusion Detection in Wireless Networks
CRC Press
January 05, 2023
In a wireless network, there is typically a large amount of data being exchanged over the network. Having sensitive information about users and network analytics leads to the idea of using this data for analysis and anomaly detection to detect the behavior of attackers and prevent future attacks on the network. Using modern AI approaches such as machine learning (ML) in robust data-driven intrusion detection systems (IDS) helps to prevent attacks, such as denial of service (DoS), eavesdropping, spoofing, which compromise the availability and security of the network, and pursues the achievement of quality-of-service (QoS) factors that improve user experience in the network. This chapter provides brief concepts about wireless network attacks and related data-driven techniques used in IDS. Moreover, comprehensive information about data-driven IDS models and AI algorithms will be discussed. This chapter will help readers grasp different concepts about the applications of AI algorithms used in data-driven IDS.
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Tags: AI
Deep Learning Approaches for IoV Applications and Services
Springer
June 10, 2021
Internet of vehicles (IoV) has become an important revolution of intelligent transportation system (ITS). It became an emerging research area as the need for it has increased tremendously. With a great number of applications available, in addition to the intention to improve the quality of life and quality of services, the application of artificial intelligence (AI) techniques would dramatically enhance the performance of the IoV overall system. This chapter will discuss deep learning networks as a type of machine learning use in IoV with influence of Neural Networks (NN), where great amounts of unlabeled data are processed, classified and clustered. Deep learning network approaches i.e., Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Reinforcement Learning (DRL), classification, clustering, and predictive analysis (regression) will briefly discussed in this chapter, in addition to review its ability to obtain better performing IoV applications.
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Tags: AI, Autonomous Vehicles
XAI applications in autonomous vehicles
