1,073 research outputs found

    Image Filtering Techniques for Object Recognition in Autonomous Vehicles

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    The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance and sustainability of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, reduce detection time by up to 43.98%, and significantly reduce energy consumption by up to 50.69% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study

    Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

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    The way we travel is changing rapidly and Cooperative Intelligent Transportation Systems (C-ITSs) are at the forefront of this evolution. However, the adoption of C-ITSs introduces new risks and challenges, making cybersecurity a top priority for ensuring safety and reliability. Building on this premise, this paper introduces an envisaged Cybersecurity Centre of Excellence (CSCE) designed to bolster researching, testing, and evaluating the cybersecurity of C-ITSs. We explore the design, functionality, and challenges of CSCE's testing facilities, outlining the technological, security, and societal requirements. Through a thorough survey and analysis, we assess the effectiveness of these systems in detecting and mitigating potential threats, highlighting their flexibility to adapt to future C-ITSs. Finally, we identify current unresolved challenges in various C-ITS domains, with the aim of motivating further research into the cybersecurity of C-ITSs

    An Efficient Authentication Protocol Based on Chebyshev Chaotic Map for Intelligent Transportation

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    For meeting the demands of safety, traffic management, and high mobility, vehicular adhoc network (VANET) has become a promising component for smart transportation systems. However, the wireless environment of vehicular network leads to various challenges in the communication security. Hence, several authentication schemes have previously been proposed to address VANET security issues but their procedures disregard the balance between effectiveness and security. Thus, this paper presents a new decentralized authentication protocol that relies on lightweight functions such as the Chebyshev chaotic map and logical shift operator to achieve the high mobility requirement. In order to reduce the number of messages transferred over the network, this protocol attempts to eliminate any redundant authentication steps during its authentication stage. Additionally, the new protocol solves key management problems by applying a little modification to the public key infrastructure to ignore certificates transmission over the network. The proposed design incorporates the self-authentication concept to safeguard the vehicle trip route on the road. Moreover, the performance evaluation is conducted to verify that the proposed protocol outperforms the most related scheme in terms of security and efficiency aspects. Finally, the Scyther simulation validates the security robustness of the new protocol

    Spatial-temporal domain charging optimization and charging scenario iteration for EV

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    Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies

    Integrating Edge Computing and Software Defined Networking in Internet of Things: A Systematic Review

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    The Internet of Things (IoT) has transformed our interaction with the world by connecting devices, sensors, and systems to the Internet, enabling real-time monitoring, control, and automation in various applications such as smart cities, healthcare, transportation, homes, and grids. However, challenges related to latency, privacy, and bandwidth have arisen due to the massive influx of data generated by IoT devices and the limitations of traditional cloud-based architectures. Moreover, network management, interoperability, security, and scalability issues have emerged due to the rapid growth and heterogeneous nature of IoT devices. To overcome such problems, researchers proposed a new architecture called Software Defined Networking for Edge Computing in the Internet of Things (SDN-EC-IoT), which combines Edge Computing for the Internet of Things (EC-IoT) and Software Defined Internet of Things (SDIoT). Although researchers have studied EC-IoT and SDIoT as individual architectures, they have not yet addressed the combination of both, creating a significant gap in our understanding of SDN-EC-IoT. This paper aims to fill this gap by presenting a comprehensive review of how the SDN-EC-IoT paradigm can solve IoT challenges. To achieve this goal, this study conducted a literature review covering 74 articles published between 2019 and 2023. Finally, this paper identifies future research directions for SDN-EC-IoT, including the development of interoperability platforms, scalable architectures, low latency and Quality of Service (QoS) guarantees, efficient handling of big data, enhanced security and privacy, optimized energy consumption, resource-aware task offloading, and incorporation of machine learnin

    Misbehavior aware on-demand intrusion detection system to enhance security in VANETs with efficient rogue nodes detection and prevention techniques

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    Vehicular ad-hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The goal behind sharing the information through beacon messages is to disseminate network state or emergency information. The exchange of information is susceptible to security attacks of different kinds. Amongst various problems to be solved in VANETs is the issue of rogue nodes and their impact on the network. Rogue nodes are malicious vehicles that are vicious to cause severe damage to the network by modifying or altering false data in beacon messages that could lead to catastrophic consequences like trapping a group of vehicles, road accidents, vehicle collisions, etc. This thesis discusses the problems associated with the security VANETs in the presence of rogue nodes. We proposed three novel intrusion detection frameworks to detect the rogue nodes responsible for false information, Sybil, and platoon control maneuver attacks only by analyzing and comparing the beacon messages broadcast over the network. The novelty of our frameworks lies in containing network damage and securing VANETs from the harmful impact of rogue nodes. The proposed frameworks are simulated using SUMO, OMNET++, and VENTOS, and the results obtained have been presented, discussed, and compared to existing frameworks. Results show that the developed methods improve the systems’ performance compared to existing methods even when the number of rogue nodes increases in the region

    Securing IoT Applications through Decentralised and Distributed IoT-Blockchain Architectures

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    The integration of blockchain into IoT can provide reliable control of the IoT network's ability to distribute computation over a large number of devices. It also allows the AI system to use trusted data for analysis and forecasts while utilising the available IoT hardware to coordinate the execution of tasks in parallel, using a fully distributed approach. This thesis's  rst contribution is a practical implementation of a real world IoT- blockchain application, ood detection use case, is demonstrated using Ethereum proof of authority (PoA). This includes performance measurements of the transaction con-  rmation time, the system end-to-end latency, and the average power consumption. The study showed that blockchain can be integrated into IoT applications, and that Ethereum PoA can be used within IoT for permissioned implementation. This can be achieved while the average energy consumption of running the ood detection system including the Ethereum Geth client is small (around 0.3J). The second contribution is a novel IoT-centric consensus protocol called honesty- based distributed proof of authority (HDPoA) via scalable work. HDPoA was analysed and then deployed and tested. Performance measurements and evaluation along with the security analyses of HDPoA were conducted using a total of 30 di erent IoT de- vices comprising Raspberry Pis, ESP32, and ESP8266 devices. These measurements included energy consumption, the devices' hash power, and the transaction con rma- tion time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J, 54Kh/J, and 22.4Kh/J when using the Raspberry Pi, the ESP32 devices, and the ESP8266 devices, respectively, this achieved while there is limited impact on each de- vice's power. In HDPoA the transaction con rmation time was reduced to only one block compared to up to six blocks in bitcoin. The third contribution is a novel, secure, distributed and decentralised architecture for supporting the implementation of distributed arti cial intelligence (DAI) using hardware platforms provided by IoT. A trained DAI system was implemented over the IoT, where each IoT device hosts one or more neurons within the DAI layers. This is accomplished through the utilisation of blockchain technology that allows trusted interaction and information exchange between distributed neurons. Three di erent datasets were tested and the system achieved a similar accuracy as when testing on a standalone system; both achieved accuracies of 92%-98%. The system accomplished that while ensuring an overall latency of as low as two minutes. This showed the secure architecture capabilities of facilitating the implementation of DAI within IoT while ensuring the accuracy of the system is preserved. The fourth contribution is a novel and secure architecture that integrates the ad- vantages o ered by edge computing, arti cial intelligence (AI), IoT end-devices, and blockchain. This new architecture has the ability to monitor the environment, collect data, analyse it, process it using an AI-expert engine, provide predictions and action- able outcomes, and  nally share it on a public blockchain platform. The pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used as a use-case implementation to test and evaluate the proposed system. While providing the AI-engine trusted data, the system achieved an accuracy of 95%,. This is achieved while the AI-engine only requires a 7% increase in power consumption. This demon- strate the system's ability to protect the data and support the AI system, and improves the IoT overall security with limited impact on the IoT devices. The  fth and  nal contribution is enhancing the security of the HDPoA through the integration of a hardware secure module (HSM) and a hardware wallet (HW). A performance evaluation regarding the energy consumption of nodes that are equipped with HSM and HW and a security analysis were conducted. In addition to enhancing the nodes' security, the HSM can be used to sign more than 120 bytes/joule and encrypt up to 100 bytes/joule, while the HW can be used to sign up to 90 bytes/joule and encrypt up to 80 bytes/joule. The result and analyses demonstrated that the HSM and HW enhance the security of HDPoA, and also can be utilised within IoT-blockchain applications while providing much needed security in terms of con dentiality, trust in devices, and attack deterrence. The above contributions showed that blockchain can be integrated into IoT systems. It showed that blockchain can successfully support the integration of other technolo- gies such as AI, IoT end devices, and edge computing into one system thus allowing organisations and users to bene t greatly from a resilient, distributed, decentralised, self-managed, robust, and secure systems

    Una revisión a los sistemas de transporte inteligente

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    Intelligent Transportation systems are essential because of the rapid expansion in new machine learning methods and the emergence of new data sources, ITS has made it possible to check and forecast traffic conditions faster and more accurately. Although this type of transport has been widely studied, a literature review that covers the main areas has not yet been built to date. The objective is to carry out this review using the tree of science through a Scopus query. The results showed three approaches: deep learning of transport systems, the importance of data for these transport systems, and traffic flow prediction. In practical terms, this article is important because it improves the effect of limiting the use of private vehicles in the contemporary world, and can help different countries to use fossil fuels more efficiently and maintain a healthy environment for the current generation.Los sistemas de transporte inteligentes son esenciales debido a la rápida expansión de nuevos métodos de aprendizaje automático y la aparición de nuevas fuentes de datos. Los ITS han hecho posible verificar y pronosticar las condiciones del tráfico de manera más rápida y precisa. Si bien este tipo de transporte ha sido ampliamente estudiado, hasta la fecha aún no se ha construido una revisión de la literatura que cubra las principales áreas. El objetivo es realizar esta revisión utilizando el árbol de la ciencia mediante una consulta Scopus. Los resultados mostraron tres enfoques: aprendizaje profundo de los sistemas de transporte, la importancia de los datos para estos sistemas de transporte y predicción del flujo de tráfico. En términos prácticos, este artículo es importante porque mejora el efecto de limitar el uso de vehículos privados en el mundo contemporáneo y puede ayudar a diferentes países a utilizar los combustibles fósiles de manera más eficiente y mantener un medio ambiente saludable para la generación actual
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