Enhanced Connectivity for Non-Critical In-Vehicle Systems Using EnRF24L01

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2025-02-26 DOI:10.1109/JSEN.2025.3542869
J. N. V. R. Swarup Kumar;Kuna Venkateswararao;Umashankar Ghugar;Sourav Kumar Bhoi;Kshira Sagar Sahoo
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Abstract

There has been a significant paradigm shift from wired to wireless technology in in-vehicular networks. This shift is driven by the need for greater scalability, cost-effectiveness, and flexibility. In the automotive industry, traditional wired protocols such as the local interconnect network (LIN) and media-oriented systems transport (MOST) for non-critical systems (NCSs) add complexity to installation and maintenance, incur higher material costs, and offer limited scalability and mobility. NCSs, such as infotainment and weather forecast systems, do not require low latency and do not impair vehicle function when unavailable. This article presents an advanced methodology for enhancing connectivity in noncritical in-vehicular networks using Nordic Semiconductor’s (nRFs) enhanced-nRF24L01 (EnRF24L01) module. The EnRF24L01 module is the nRF24L01 module that incorporated the Sensor-Medium Access Control (S-MAC) algorithm for energy-efficient communication. The proposed method enables seamless communication between NCSs using a tree-based primary and secondary architecture, where the primary is the actuator and the secondary is the sensor node. To optimize energy efficiency using synchronized sleep/wake schedules, reduce power consumption, and enhance scalability, the S-MAC protocol was incorporated. Comprehensive experiments were conducted in simulated environments using optimized network engineering tool (OPNET) and Proteus Circuit Simulators, analyzing critical performance metrics: latency, jitter, throughput, packet delivery ratio (PDR), and energy efficiency. The results indicate that the proposed method supports a greater number of nodes with enhanced data transmission rates and operates at lower voltages, thereby extending the communication range and reducing overall power consumption. Additionally, hardware simulation results demonstrate the successful integration of EnRF24L01 modules with Arduino for wireless data transmission, showing significant improvements in scalability, energy efficiency, and adaptability, as well as architectural and operational costs and maintenance efficiency.
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使用EnRF24L01增强非关键车载系统的连接性
在车载网络中,从有线技术到无线技术已经发生了重大的范式转变。这种转变是由对更大的可伸缩性、成本效益和灵活性的需求驱动的。在汽车行业,传统的有线协议,如用于非关键系统(NCSs)的本地互连网络(LIN)和面向媒体的系统传输(MOST),增加了安装和维护的复杂性,产生更高的材料成本,并且提供有限的可扩展性和移动性。ncs,如信息娱乐和天气预报系统,不需要低延迟,并且在不可用时不会损害车辆功能。本文介绍了一种使用北欧半导体(nRFs)增强型nrf24l01 (EnRF24L01)模块增强非关键车载网络连通性的先进方法。EnRF24L01模块是nRF24L01模块,它结合了传感器介质访问控制(S-MAC)算法,用于节能通信。所提出的方法使用基于树的主辅架构实现ncs之间的无缝通信,其中主辅架构是执行器,次要是传感器节点。为了使用同步睡眠/唤醒时间表优化能源效率,降低功耗并增强可扩展性,S-MAC协议被纳入其中。利用优化的网络工程工具(OPNET)和Proteus电路模拟器在模拟环境中进行了综合实验,分析了关键性能指标:延迟、抖动、吞吐量、分组传输比(PDR)和能效。结果表明,该方法支持更多节点,数据传输速率更高,工作电压更低,从而扩大了通信范围,降低了总体功耗。此外,硬件仿真结果表明,将EnRF24L01模块与Arduino成功集成在一起进行无线数据传输,在可扩展性、能效和适应性以及架构和运营成本和维护效率方面都有显着提高。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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