VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring

Kargupta, Hillol; Bhargava, Ruchita; Liu, Kun; Powers, M.; Blair, Patrick J.; Bushra, S.; Dull, J. D.; Sarkar, Kakali; Klein, Martin; Vasa, Mitesh; Handy, David

doi:10.1137/1.9781611972740.28

Cited by 120 publications

(69 citation statements)

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“…For example, [16] proposed an onc 2017 Information Processing Society of Japan board stream processing method for engineering testing and diagnosis in vehicle systems. The Vehicle Data Stream Mining System (VEDAS) [17] and Minefleet [18] are distributed data stream mining platforms for use in mobile computing devices or automotive systems. StreamCars is a stream processing platform for driver assistance systems, which shares a similar objective as our research [19].…”

Section: Data Stream Management Systemsmentioning

confidence: 99%

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.

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Section: Data Stream Management Systemsmentioning

confidence: 99%

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Yamaguchi

Watanabe

Sato

et al. 2017

Journal of Information Processing

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“…VEDAS, a real-time on-board stream mining system for vehicle-health-monitoring and driver status characterization, is developed in [6]. The PDAs perform most of the data management and mining tasks and send the analysis results to the central server site.…”

Section: Distributed Stream Miningmentioning

confidence: 99%

A Scalable Distributed Stream Mining System for Highway Traffic Data

Liu

Choudhary

Zhou

et al. 2006

Lecture Notes in Computer Science

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Abstract. To achieve the concept of smart roads, intelligent sensors are being placed on the roadways to collect real-time traffic streams. Traditional method is not a real-time response, and incurs high communication and storage costs. Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed traffic stream mining system. The central server performs various data mining tasks only in the training and updating stage and sends the interesting patterns to the sensors. The sensors monitor and predict the coming traffic or raise alarms independently by comparing with the patterns observed in the historical streams. The sensors provide real-time response with less wireless communication and small resource requirement, and the computation burden on the central server is reduced. We evaluate our system on the real highway traffic streams in the GCM Transportation Corridor in Chicagoland.

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“…Learning from data streams in ubiquitous environment is enabled by Ubiquitous Data Mining (UDM), which is the analysis of data streams to discover useful knowledge on mobile, embedded, and ubiquitous devices [9]. UDM have been used to monitor vehicle's health and driver's characteristics in moving vehicles [13] and to identify drunk-driving behavior [12].…”

Section: Intelligent Software Systemsmentioning

confidence: 99%

Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety

Salim

Krishnaswamy

Loke

et al. 2005

Embedded and Ubiquitous Computing – EUC 2005 Workshops

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Abstract. In USA, 2002, approximately 3.2 million intersection-related crashes occurred, corresponding to 50 percent of all reported crashes. In Japan, more than 58 percent of all traffic crashes occur at intersections. With the advances in Intelligent Transportation Systems, such as off-the-shelf and in-vehicle sensor technology, wireless communication and ubiquitous computing research, safety of intersection environments can be improved. This research aims to investigate an integration of intelligent software agents and ubiquitous data stream mining, for a novel context-aware framework that is able to: (1) monitor an intersection to learn for patterns of collisions and factors leading to a collision; (2) learn to recognize potential hazards in intersections from information communicated by road infrastructures, approaching and passing vehicles, and external entities; (3) warn particular threatened vehicles that are approaching the intersection by communicating directly to the in-vehicle system.

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VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring

Cited by 120 publications

References 0 publications

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

A Scalable Distributed Stream Mining System for Highway Traffic Data

Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety

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