A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

Du, Yuheng; Chowdhury, Mashrur; Rahman, Mizanur; Dey, Kakan; Apon, Amy; Luckow, André; Ngo, Linh

doi:10.1109/tits.2017.2701799

Cited by 44 publications

(39 citation statements)

References 24 publications

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“…The proposed system could be used to monitor the status of wind turbines and is expected to help reduce operation and management costs. Du et al proposed a framework for handling huge amounts of incoming unstructured connected vehicle (CV) data [ 24 ]. The proposed framework utilized Apache Kafka as a distributed message broker.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Big data analysis has led to significant improvements in the manufacturing industry, such as reducing energy consumption [ 17 ], improving production scheduling and logistics planning [ 18 ], mitigating social risks [ 19 ], and facilitating better decision making [ 20 ]. Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [ 21 , 22 , 23 , 24 , 25 , 26 ], Apache Storm [ 27 , 28 , 29 , 30 , 31 ], and NoSQL MongoDB [ 32 , 33 , 34 , 35 , 36 , 37 ]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [ 38 ], providing effective and efficient solutions for processing IoT-generated data for smart cities [ 39 ], and handling large amounts of smart environmental data in real-time [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

278

103

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With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Syafrudin

Alfian

Fitriyani

et al. 2018

Sensors

278

103

View full text Add to dashboard Cite

show abstract

“…Our study utilizes real‐time object detection on clouds for visual inspection in automobiles. Other real‐time applications on clouds utilize only memory‐intensive models, low computing devices, or unstructured text data but as our experiments deal with high‐definition image data we utilize a wide range of models, parameters, and hardware platforms, enabling the researchers to make an effective choice of variables while constructing an end‐to‐end visual inspection pipeline.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Deployment of a cloud pipeline for real‐time visual inspection using fast streaming high‐definition images

et al. 2020

Self Cite

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We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system. K E Y W O R D Scloud, end-to-end pipeline, latency, real-time system, visual inspection INTRODUCTIONMachine learning has proven to be highly effective in providing solutions to wide variety of challenging problems in several domains. In the automotive domain, machine learning and deep learning have several potential applications both inside and outside the vehicle. 1 Advanced driving assistance systems (ADAS) and system for self-driving cars operate within the vehicle while systems outside the vehicle indulge in the manufacturing assembly plant systems. 2 Building an end-to-end system pipeline that facilitates continuous delivery requires careful consideration of various tradeoffs. The pipeline should be responsive, scalable for different workloads, fault tolerant, replicable, and cost-effective. Much research lately focuses on optimization of machine learning algorithms, developing frameworks for these algorithms, analyzing the tradeoffs of various parameters associated with these models, or developing a pipeline or system architecture for automatically deploying the infrastructure. The last two topics are main focus of this research. Our work focuses on the end-to-end system architecture for visual inspection using an Amazon Web Services (AWS) implementation. We study various tradeoffs associated with the object detection models using high-definition images. We also discuss interesting use cases for implementation tradeoffs when considering the latency and cost for the end-to-end visual inspection pipeline. 868Visual inspection in the automotive manufacturing is a highly labor intensive and slow process. The quality checks in automotive manufacturing plant involve high-quality visual inspection to ensure that the parts are in the correct locations, have the right shape, that the product does not have any missing parts, and that the automobile is free from scratches, dents, or blemishes. Visual inspection can be facilitated through the use of deep learning models that are deployed on the cloud. Models that are connected to a set of high-definition cameras located at the assembly stat...

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“…Vishwarupe et al, () proposed an embedded system to collect real‐time weather data and deploy them under cooperation with telecom infrastructure. To relieve the bottleneck of transmitting large volumes of unstructured data by using data centres, Du et al, () proposed a novel strategy for creating an efficient and low‐latency‐distributed message delivery system with operational connected vehicles applications. In addition to data, crowdsourcing can focus on computational resources.…”

Section: Review Of Previous Work and Analysismentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

Cited by 44 publications

References 24 publications

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

Deployment of a cloud pipeline for real‐time visual inspection using fast streaming high‐definition images

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

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