Flexray versus Ethernet for vehicular networks

Amel, Ben Ncira; Bouhouch, Rim; Jaouani, Houda; Hasnaoui, Salem; Jelassi, Khaled

doi:10.1109/ievc.2014.7056123

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…In STL, with the tremendous rise in the number of sensors deployed in the road, the number of connected vehicles and their ever-increasing mobility, the support for low latency and uninterrupted communication between the sensors and the fog is crucial to assure the correct operation of the applications [23]. DDS has been used as basis for improving the network semantics and the QoS in these environments [78].…”

Section: Network Semanticsmentioning

confidence: 99%

“…Hence, it is necessary to decide which protocol to use in relation to the type of application, adopting MQTT for reliable communications or for big-size packet communication, and CoAP otherwise, in order to decrease latency and increase system performance. DDS is widely used for real-time M2M communications among constrained devices [78]. [78] addresses DDS as one of the best solutions for realtime distributed industrial systems.…”

Section: Low-latencymentioning

confidence: 99%

“…DDS is widely used for real-time M2M communications among constrained devices [78]. [78] addresses DDS as one of the best solutions for realtime distributed industrial systems.…”

Section: Low-latencymentioning

confidence: 99%

See 2 more Smart Citations

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

233

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Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.

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Section: Network Semanticsmentioning

confidence: 99%

Section: Low-latencymentioning

confidence: 99%

See 1 more Smart Citation

A survey on fog computing for the Internet of Things

Bellavista

Berrocal

Corradi

et al. 2019

Pervasive and Mobile Computing

233

View full text Add to dashboard Cite

show abstract

“…Amel B.N. et al [29] showed how to use DDS over an Ethernet network and proposed a SAE benchmark as a test application. The main goal is to calculate the worst-case response time (WCRT) based on the scheduling model and the use it as a metric to evaluate the DDS real-time QoS (Quality of Service).…”

Section: Vehicle Protocolmentioning

confidence: 99%

A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle

et al. 2018

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This paper proposes a Lightweight In-Vehicle Edge Gateway (LI-VEG) for the self-diagnosis of an autonomous vehicle, which supports a rapid and accurate communication between in-vehicle sensors and a self-diagnosis module and between in-vehicle protocols. A paper on the self-diagnosis module has been published previously, thus this paper only covers the LI-VEG, not the self-diagnosis. The LI-VEG consists of an In-Vehicle Sending and Receiving Layer (InV-SRL), an InV-Management Layer (InV-ML) and an InV-Data Translator Layer (InV-DTL). First, the InV-SRL receives the messages from FlexRay, Control Area Network (CAN), Media Oriented Systems Transport (MOST), and Ethernet and transfers the received messages to the InV-ML. Second, the InV-ML manages the message transmission and reception of FlexRay, CAN, MOST, and Ethernet and an Address Mapping Table. Third, the InV-DTL decomposes the message of FlexRay, CAN, MOST, and Ethernet and recomposes the decomposed messages to the frame suitable for a destination protocol. The performance analysis of the LI-VEG shows that the transmission delay time about message translation and transmission is reduced by an average of 10.83% and the transmission delay time caused by traffic overhead is improved by an average of 0.95%. Therefore, the LI-VEG has higher compatibility and is more cost effective because it applies a software gateway to the OBD, compared to a hardware gateway. In addition, it can reduce the transmission error and overhead caused by message decomposition because of a lightweight message header.

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