Composite fault diagnosis methodology for urban vehicular ad hoc network

Senapati, Biswa Ranjan; Khilar, Pabitra Mohan; Swain, Rakesh Ranjan

doi:10.1016/j.vehcom.2021.100337

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…As shown in Figure 10, although the average speed of the proposed method is almost consistent with that of the first two benchmark methods during approximately 30-100s as well as lower than the actuated STA method in approximately 20-30 s, it is significantly higher than the comparison methods in other time periods. It is shown that using the proposed FIGURE 10 Comparison results of average speed under typical cooperative driving method, the travel time for CAVs to leave the conflict zone is approximately 120s, which is significantly lower than that of the other three benchmark methods. Further, the results illustrate that, compared with the three benchmark algorithms, the proposed method can reduce the travel time of CAVs and improve the road traffic efficiency under the conditions of time-varying delays and packet losses at the intersections, which can satisfy our expectations of the algorithm.…”

Section: Figure 6 Comparison Results Of Average Speed Under Different...mentioning

confidence: 97%

“…and take Equation ( 6) into Equation (10) and set 𝜕J ∕𝜕U Nu (k) = 0 to obtain the optimal control variable…”

Section: Moving Horizon Prediction Controlmentioning

confidence: 99%

“…However, the data transmission quality between CAVs and RSU is limited by the fast movement of a multitude of vehicles, buildings, walls, and other different fault states, which can shadow the signal exchange of information between CAVs and RSU. Hence, fatal accidents could occur and the traffic efficiency could decrease on the road [10,11]. Therefore, studying the networked control method that overcomes the negative impact of communication restrictions on multi-vehicle cooperative driving at unsignallized intersections is vital [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Jiang

Luo

et al. 2022

IET Intelligent Trans Sys

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In a vehicle‐to‐infrastructure (V2I) communication network at signal‐free intersections, time‐varying delays and random packet losses may occur because of the unreliable nature of wireless communication, which can degrade the performance of cooperative driving for large‐scale connected and automated vehicles (CAVs). Consequently, to solve this problem, this paper proposed a cooperative driving control method based on improved model‐free adaptive predictive control (IMFAPC). I/O data of the target CAVs during driving are adopted to realize the networked predictive control of the time‐varying transmission delays and packet losses in the dynamic linearization prediction scheme and moving horizon prediction control technology. And then the vehicle can be allocated the expected speed to realize accurate vehicle control. In addition, the convergence of the IMFAPC‐based cooperative driving control algorithm is proven by conducting a stability analysis. Extensive numerical experiments verified the effectiveness of the proposed control strategy, and the results demonstrate that the proposed method can alleviate the adverse effects of time‐varying delays and random packet losses on the control effects, thereby improving traffic efficiency under various traffic volumes on the basis of ensuring the safety of multi‐vehicle driving.

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Section: Figure 6 Comparison Results Of Average Speed Under Different...mentioning

confidence: 97%

“…and take Equation ( 6) into Equation (10) and set 𝜕J ∕𝜕U Nu (k) = 0 to obtain the optimal control variable…”

Section: Moving Horizon Prediction Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Jiang

Luo

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Mathematically, the value of the weights can be represented as follows. 58 0 ≤ w1, w2, w3,w4, w5 ≤ 1: ð11Þ If the value of weight is equal to 1, then the weight completely determines the path value . If the value of weight is equal to 0, then the weight has no role in determining the path path value .…”

Section: Transmission Phasementioning

confidence: 99%

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

Senapati

Khilar

Swain

2021

Int J Communication

Self Cite

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Industrial growth, technological development, and increased human activities for comfort and luxurious life increase different environmental parameters like temperature, humidity, carbon monoxide, methane, and smoke. The value of these parameters are increasing steadily which has adverse effects on human life and human health and is also the cause of various disease like cancers and lung disease. It is now essential to monitor the environmental parameters of a region frequently. For the automatic continuous monitoring of environmental parameters of any location, this paper proposes to use vehicular ad hoc network (VANET), which is one of the promising subclasses of mobile ad hoc network (MANET). Automatic monitoring of environmental parameters of any location includes measurement of environmental parameters of a location, transmission of environmental parameters from the measured location to the observation center, and analysis of the parameters at the observation center. Two aspects are discussed for the transmission of parameters from the location of measurement to the observation center: first by vehicular communication and second by vehicular cloud. The analysis of the environmental parameters at the observation center is done using probabilistic neural network (PNN).Apart from this, different generic network parameters like end-to-end delay, number of hops, and network gaps for the proposed work are computed for the transmission of different environmental parameters and is compared with different VANET routing protocols like geographic source routing (GSR), anchor-based street and traffic-aware routing (A-STAR), and peripheral nodebased geographic distance routing (P-GEDIR). Vehicular motion is created using Simulation of Urban MObility (SUMO), and the network parameter values, like end-to-end delay and number of hops, are obtained using OMNET ++. A testbed experiment is conducted for the classification of sensor data which is adoptable in practice. K E Y W O R D SMANET, probabilistic neural network, VANET, vehicular cloud A productive application of VANET.

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“…(i) In statistics-based detection methods commonly metrics such as the mean, the variance, or the gradient of the sensor data are considered for outlier detection [29], but there are also more sophisticated approaches that, for example, apply the Mann-Whitney U statistical test or the Kolmogorov-Smirnov test [30] to identify permanent, intermittent, and transient irregularities in the sensor data [31] as well as 3σ-based techniques using historical data and the measurements of neighboring nodes [32].…”

Section: Sensor Data Analysismentioning

confidence: 99%

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

Widhalm

Goeschka

Kästner

2021

Sensors

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In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.

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Composite fault diagnosis methodology for urban vehicular ad hoc network

Cited by 19 publications

References 40 publications

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Improved model‐free adaptive predictive control‐based cooperative driving control for connected and automated vehicles subject to time‐varying communication delays and packet losses at signal‐free intersections

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

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