Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles

Rakhmanov, Amnon; Wiseman, Yair

doi:10.3390/rs15082165

Cited by 16 publications

(10 citation statements)

References 28 publications

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“…Compression algorithms in the IoV. Compression and decompression algorithms [49,50] are usually a set of deterministic algorithms and are publicly available. Data with low-security requirements can be transmitted directly or after compression, which greatly reduces the communication bandwidth requirements.…”

Section: Methodsmentioning

confidence: 99%

Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

Xu,

Yin,

2023

Electronics

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The Internet of Vehicles (IoV) improves traffic efficiency and enhances driving safety through the real-time collection and analysis of traffic-related data. Numerous secure and privacy-preserving communication protocols have been proposed for the IoV. However, various security threats, privacy leakage, and inefficient communications remain unaddressed. Therefore, a lightweight and secure multi-message multi-receiver certificateless signcryption (LS-MRCLSC) scheme based on elliptic curve cryptography (ECC) is proposed. The proposed scheme guarantees secure communication and promotes messaging efficiency with multi-cast mode. Multiple key generation centers (KGCs) collaborate to generate and update the system master key (SMK) using Feldman’s verifiable secret-sharing (FVSS) algorithm, avoiding the single point of failure (SPoF) problem. Formal security proofs under the random oracle model (ROM) demonstrate that the proposed scheme meets requirements such as data confidentiality, message unforgeability, anonymity, and unlinkability. Performance evaluations confirm that the LS-MRCLSC scheme is better than similar schemes in terms of efficiency, feasibility, and scalability.

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Section: Methodsmentioning

confidence: 99%

Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

Xu,

Yin,

2023

Electronics

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“…Our proposed work is a generic solution for carrying out a variety of data spatial transformations in digital logic. The proposed accelerator enables real-time computing, omitting the need for a communication bandwidth saving mechanisms as in [12] and enabling the fully digital implementation of such use cases as real-time image rectification and registration, digital zoom, lens distortion correction and compression [14]. Additionally, in this work, we formalise the logic behind parallel data access, which enables 16-point bicubic interpolation and improves over other related work: [11,12].…”

Section: Related Workmentioning

confidence: 99%

Spatial Transformation Accelerator with Parallel Data Access Scheme for Sample Reconstruction

Novickis,

Lielāmurs,

Justs

et al. 2024

Electronics

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Spatial image transformation is a commonly used component in many image processing pipelines. It enables the correction of optical distortions, image registration onto a common reference plane, electronic image stabilisation, digital zoom, video mosaicking, etc. With the growing tendency to embed image processing in low-power devices, attaining an efficient transformation solution becomes increasingly decisive. Furthermore, interpolation is the key operation in achieving the high quality of the transformed data from the original data. Fortunately, different implementations have already seen several efficiency improvements in recent years. However, interpolation relies on sampling a set of neighbouring points from memory, which has yet to be addressed efficiently for smaller computational platforms with limited memory resources. In this work, we derive a generic mathematical model and circuit design principles for the spatial transformation accelerator design for N-dimensional data. Furthermore, we present an efficient simultaneous access scheme for high-quality signal reconstruction. Finally, the introduced ideas are verified in field programmable gate arrays using one-dimensional and two-dimensional data transformation use cases. The presented solution is able to transform images with sizes ranging from 256 × 256 to 8192 × 8192 and achieves a transfer rate of 275 frames per second with 512 × 512 images.

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“…A model is created from the legitimate traffic (i.e., network traffic without attacks) and it is used in a subsequent step to detect anomalies, which may correspond to a potential attack. A similar pre-processing step is used in the automotive domain for similar problems as in [14] to improve the reliability of the data flow. A sliding window approach can be more time-efficient than approaches where each single CAN-bus message is analyzed but a potential weakness is found in the lower discriminating power as discussed in [15][16][17], where entropy measures are used as features.…”

Section: Related Workmentioning

confidence: 99%

In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms

Baldini

2023

Information

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Cybersecurity in modern vehicles has received increased attention from the research community in recent years. Intrusion Detection Systems (IDSs) are one of the techniques used to detect and mitigate cybersecurity risks. This paper proposes a novel implementation of an IDS for in-vehicle security networks based on the concept of multi-scale histograms, which capture the frequencies of message identifiers in CAN-bus in-vehicle networks. In comparison to existing approaches in the literature based on a single histogram, the proposed approach widens the informative context used by the IDS for traffic analysis by taking into consideration sequences of two and three CAN-bus messages to create multi-scale dictionaries. The histograms are created from windows of in-vehicle network traffic. A preliminary multi-scale histogram model is created using only legitimate traffic. Against this model, the IDS performs traffic analysis to create a feature space based on the correlation of the histograms. Then, the created feature space is given in input to a Convolutional Neural Network (CNN) for the identification of the windows of traffic where the attack is present. The proposed approach has been evaluated on two different public data sets achieving a very competitive performance in comparison to the literature.

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Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles

Cited by 16 publications

References 28 publications

Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

Lightweight and Secure Multi-Message Multi-Receiver Certificateless Signcryption Scheme for the Internet of Vehicles

Spatial Transformation Accelerator with Parallel Data Access Scheme for Sample Reconstruction

In-Vehicle Network Intrusion Detection System Using Convolutional Neural Network and Multi-Scale Histograms

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