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"Internet of Vehicles"
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Internet of things : robotic and drone technology
\"This reference text discusses intelligent robotic and drone technology with embedded Internet of Things (IoT) for smart applications. The text discusses future directions of optimization methods with various engineering and science fundamentals used in robotics and drone-based applications. Its emphasis is on covering deep learning and similar models of neural network-based learning techniques employed in solving optimization problems of different engineering and science applications. It covers important topics including sensors and actuators in the internet of things (IoT), internet-of-robotics-things (IoRT), IoT in agriculture and food processing, routing challenges in flying Ad-hoc networks, and smart cities. The book will serve as a useful text for graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and mechanical engineering\"-- Provided by publisher.
Book
A Blockchain Framework for Securing Connected and Autonomous Vehicles
Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.
Journal Article
Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain
The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
Journal Article
Lightweight-BIoV: Blockchain Distributed Ledger Technology (BDLT) for Internet of Vehicles (IoVs)
The vast enhancement in the development of the Internet of Vehicles (IoV) is due to the impact of the distributed emerging technology and topology of the industrial IoV. It has created a new paradigm, such as the security-related resource constraints of Industry 5.0. A new revolution and dimension in the IoV popup raise various critical challenges in the existing information preservation, especially in node transactions and communication, transmission, trust and privacy, and security-protection-related problems, which have been analyzed. These aspects pose serious problems for the industry to provide vehicular-related data integrity, availability, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects against the increasing number of multiple transactions. In addition, there has been a lot of research interest that intersects with blockchain and Internet of Vehicles association. In this regard, the inadequate performance of the Internet of Vehicles and connected nodes and the high resource requirements of the consortium blockchain ledger have not yet been tackled with a complete solution. The introduction of the NuCypher Re-encryption infrastructure, hashing tree and allocation, and blockchain proof-of-work require more computational power as well. This paper contributes in two different folds. First, it proposes a blockchain sawtooth-enabled modular architecture for protected, secure, and trusted execution, service delivery, and acknowledgment with immutable ledger storage and security and peer-to-peer (P2P) network on-chain and off-chain inter-communication for vehicular activities. Secondly, we design and create a smart contract-enabled data structure in order to provide smooth industrial node streamlined transactions and broadcast content. Substantially, we develop and deploy a hyperledger sawtooth-aware customized consensus for multiple proof-of-work investigations. For validation purposes, we simulate the exchange of information and related details between connected devices on the IoV. The simulation results show that the proposed architecture of BIoV reduces the cost of computational power down to 37.21% and the robust node generation and exchange up to 56.33%. Therefore, only 41.93% and 47.31% of the Internet of Vehicles-related resources and network constraints are kept and used, respectively.
Journal Article
SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach
Proposing an optimal routing protocol for internet of vehicles with reduced overhead has endured to be a challenge owing to the incompetence of the current architecture to manage flexibility and scalability. The proposed architecture, therefore, consolidates an evolving network standard named as software defined networking in internet of vehicles. Which enables it to handle highly dynamic networks in an abstract way by dividing the data plane from the control plane. Firstly, road-aware routing strategy is introduced: a performance-enhanced routing protocol designed specifically for infrastructure-assisted vehicular networks. In which roads are divided into road segments, with road side units for multi-hop communication. A unique property of the proposed protocol is that it explores the cellular network to relay control messages to and from the controller with low latency. The concept of edge controller is introduced as an operational backbone of the vehicle grid in internet of vehicles, to have a real-time vehicle topology. Last but not least, a novel mathematical model is estimated which assists primary controller in a way to find not only a shortest but a durable path. The results illustrate the significant performance of the proposed protocol in terms of availability with limited routing overhead. In addition, we also found that edge controller contributes mainly to minimizes the path failure in the network.
Journal Article
Quantum secured 6G technology-based applications in Internet of Everything
Scientific and technological breakthroughs carry with themselves a pertinent question—“what’s next”. The evolution of mobile networks and spectrum technology over the last two decades has only confirmed this well-established fact. The ever-increasing need for broadening frequency bands, connectivity of devices which now run in billions of numbers, lower latency (so that we can enjoy interruption-free services 24×7), increasing intervention of artificial intelligence in our day-to-day life, remote connectivity, and virtual presence to increase our output and reach and many more aspects have only intensified the demand for a more reliable, robust, intelligent, secure and friendly infrastructure through which the data, touted as the new oil for 21st century can be produced, stored and transmitted efficiently. The existing 4G and 5G systems cannot handle the ever-growing latency and connectivity needs of the Internet of Everything. Therefore, in this article, we present the vision of 6G technology with its requirements and discuss the associated challenges and enabler technologies in detail. Yet, with the advent of the quantum computer, all public-key-based techniques employed to provide communication security in existing smart networks, irrespective of underlying communication technology, are prone to quantum attacks. Hence, case studies related to Internet-of-Vehicles and Internet-of-Things utilizing a newly proposed architecture based on quantum secured 6G enabled communication is discussed to realize the secure and efficient Internet of Everything.
Journal Article
Trust on wheels: Towards secure and resource efficient IoV networks
The emerging yet promising paradigm of Internet-of-Vehicles (IoV) has received considerable attention in the recent years as an integral and indispensable constituent of the modern intelligent transportation systems, wherein safety-critical information is exchanged amongst the connected vehicles via a high-bandwidth, low-latent vehicle-to-everything communication system so as to ensure road safety and highly efficient traffic flows. Therefore, it is of utmost importance to ensure the authenticity and reliability of such safety messages together with the legitimacy of the vehicles disseminating the same. This necessitates instituting a state-of-the-art trust-based mechanism in order to not only identify but to further evict the malicious vehicles from an IoV network. The risk augments if a malicious vehicle (via its malign attempts) assume the role of a cluster head in a cluster, thereby jeopardizing the safety of both the occupants of the vehicles and vulnerable pedestrians. Hence, intelligent algorithms with a potential to opt for both a trusted and a resource efficient cluster head needs to be put into place to guarantee the overall security and resource efficacy of the corresponding cluster. Accordingly, in this paper, we have envisaged a scalable hybrid trust-based model which introduces a composite metric encompassing the weighted amalgamation of a vehicle’s computed trust score and its corresponding available resources in order to ensure that the stringent performance requirements of the safety-critical vehicular applications are fully met. A Hungarian algorithm-based role assignment mechanism has been subsequently envisaged for the selection of an optimal cluster head, proxy cluster head, and followers amongst the members of a vehicular cluster for maximizing its overall efficacy. Furthermore, the notion of an adaptive threshold has been proposed in order to identify and subsequently eliminate the smart malicious vehicles from an IoV network in a timely manner, i.e., as soon as they start exhibiting an adverse behavior, to guarantee that the network is not manipulated for any malicious gains. Extensive simulations have been carried out and performance analysis demonstrates the efficaciousness of our proposed scheme.
Journal Article
Performance Analysis of Non-Orthogonal Multiple Access-Enhanced Autonomous Aerial Vehicle-Assisted Internet of Vehicles over Rician Fading Channels
The increasing number of intelligent connected vehicles (ICVs) is leading to a growing scarcity of spectrum resources for the Internet of Vehicles (IoV), which has created an urgent need for the use of full-duplex non-orthogonal multiple access (FD-NOMA) techniques in vehicle-to-everything (V2X) communications. Meanwhile, for the flexibility of autonomous aerial vehicles (AAVs), V2X communications assisted by AAVs are regarded as a potential solution to achieve reliable communication between ICVs. However, if the integration of FD-NOMA and AAVs can satisfy the requirements of V2X communications, then quickly and accurately analyzing the total achievable rate becomes a challenge. Motivated by the above, an accurate analytical expression for the total achievable rate over Rician fading channels is proposed to evaluate the transmission performance of NOMA-enhanced AAV-assisted IoV with imperfect channel state information (CSI). Then, we derive an approximate expression with the truncated error, based on which the closed-form expression for the approximate error is theoretically provided. Finally, the simulation results demonstrate the accuracy of the obtained approximate results, where the maximum approximate error does not exceed 0.5%. Moreover, the use of the FD-NOMA technique in AAV-assisted IoV can significantly improve the total achievable rate compared to existing work. Furthermore, the influence of key network parameters (e.g., the speed and Rician factor) on achievable rate is thoroughly discussed.
Journal Article
Federated AI-Enabled In-Vehicle Network Intrusion Detection for Internet of Vehicles
The integration of artificial intelligence (AI) technology into the Internet of Vehicles (IoV) has provided smart services for intelligent connected vehicles (ICVs). However, due to gradually upgrading to ICVs, an increasing number of external communications interfaces exposes the in-vehicle networks (IVNs) to malicious network intrusion. The malicious intruders can take over the compromised ICVs and mediately intrude into the ICVs connected through IoV. Therefore, it is urgent to develop IVN intrusion detection methods for IoV security protection. In this paper, a ConvLSTM-based IVN intrusion detection method is developed by leveraging the periodicity of the network message ID. For training the ConvLSTM model, a federated learning (FL) framework with client selection is proposed. The fundamental FL framework works in the client-server mode. ICVs are the local clients, and mobile edge computing (MEC) servers connected to base stations (BSs) function as the parameter servers. Based on the framework, a proximal policy optimization (PPO)-based federated client selection (FCS) scheme is further developed to optimize the model accuracy and system overhead of federated ConvLSTM model training. Simulations are conducted by the exploitation of real-world IoV scenario settings and IVN datasets. The results indicate that by exploiting the ConvLSTM, the model size and convergence time are dramatically reduced, and the 95%-beyond detection accuracy is maintained. The results also unveil that the PPO-based FCS scheme outperforms the benchmarks on the convergence rate, model accuracy, and system overhead.
Journal Article
CCP-federated deep learning based on user trust chain in social IoV
Federated learning is widely used in the context of wireless networks to protect sensitive user data. However, centralized federated learning encounters some issues when applied to the Social Internet of Vehicles, specifically low communication efficiency and high computational cost. In order to alleviate communication bottlenecks and protect vehicle user data privacy, we herein propose the Conditional Choice Probability-Federated Deep Learning algorithm based on user trust chain. This algorithm introduces inter-user trust elements to characterize the vehicle connection network from a vehicle-user relationship perspective. It computes the node conditional choice trust probability based on the single-way trust atomic chain and circular chain of user nodes. Local model interactions are then performed to complete the decentralized federated deep learning framework. Experiments are conducted to verify the robustness of the proposed algorithm's conditional choice probability estimation and confirm that decentralized federated deep learning is effective.
Journal Article