Challenges and opportunities in immersive vehicular sensing: Lessons from urban deployments

Pau, Giovanni; Tse, Rita

doi:10.1016/j.image.2012.01.015

Cited by 14 publications

(13 citation statements)

References 15 publications

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“…The solutions in [19] and [20] meet the objective of reducing the load of the uplink of the cellular network, but, these rely on IEEE 802.11p-enabled on-board devices, whose penetration rate is expected to increase at a slow pace and whose cost may be very high at the initial deployment stage. 2 Our proposal, instead, appears as a promising cheap and short-time-to-market solution that, by leveraging widely 1 The minimum data footprint per packet is around 25 bytes, for timestamp, position, speed and direction information [30] and may increase according to the number of additional conveyed elements concerning vehicle status and its surroundings. 2 The cost of commercial OBUs compliant with IEEE 802.11p specifications, like the ones by Cohda Wireless [31] is around 3000 €, whereas for a 802.11p PMCIA [32] mounted into an Alix board it may be up to 500 €.…”

Section: Fcd Load and Proposed Solutionsmentioning

confidence: 99%

“…2 Our proposal, instead, appears as a promising cheap and short-time-to-market solution that, by leveraging widely 1 The minimum data footprint per packet is around 25 bytes, for timestamp, position, speed and direction information [30] and may increase according to the number of additional conveyed elements concerning vehicle status and its surroundings. 2 The cost of commercial OBUs compliant with IEEE 802.11p specifications, like the ones by Cohda Wireless [31] is around 3000 €, whereas for a 802.11p PMCIA [32] mounted into an Alix board it may be up to 500 €. Unlike the proposals in [19] and [21], our solution extends the scope of FCD by effectively exploiting the car as a powerful multisensor platform, also able to gather city-wide environmental measurements while moving.…”

Section: Fcd Load and Proposed Solutionsmentioning

confidence: 99%

“…Furthermore, thanks to the advancements in invehicle telematics, FCD can also include reports about the status of microprocessor-based electronic control units networked within the vehicle through the Controller Area Network (CAN) bus. Environmental sensors (like pollution detectors) can be easily brought into the picture to augment the scope of FCD-enabled applications towards urban sensing operations, so that vehicles can be integrated in the future smart-city infrastructure [2]. Henceforth, we will refer to the resulting set of collected data as "augmented" FCD.…”

Section: Introductionmentioning

confidence: 99%

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Supporting augmented floating car data through smartphone-based crowd-sensing

Briante

Campolo

Molinaro

et al. 2014

Vehicular Communications

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Section: Fcd Load and Proposed Solutionsmentioning

confidence: 99%

Section: Fcd Load and Proposed Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supporting augmented floating car data through smartphone-based crowd-sensing

Briante

Campolo

Molinaro

et al. 2014

Vehicular Communications

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“…It has become important to monitor the air quality of different places. The MPI urban sensing research project [1][2] provides a cost-effective method to monitor air quality in Macao. It uses the campus vehicular testbed based on MPI facility vehicles to collect pollution data from many mobile air quality sensors.…”

Section: Introductionmentioning

confidence: 99%

A Visualized Exploration in Urban Sensing of Air Quality with Vehicular Sensors

Tse¹,

Lei²,

Lam³

et al. 2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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Worldwide deterioration of air quality has raised people's concerns on what they inhale on a daily basis. This work exploits inexpensive pollution sensors deployed in the Macao Polytechnic Institute service fleet as a mean to monitor air quality at city scale. We propose web-based geo-spatial exploration tool to provide air-quality information for users and insights on the causes to the city hall. We are able to identify strong connections between the territory orography and the air quality level. Specifically, it is possible by analyzing various areas to discover that places with traffic congestion and confined space, or poor natural ventilation which exhibits a sudden change in air quality readings.

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“…Section 6 presents our conclusions. [26] present a solution where vehicles are used to measure air parameters as well as urban traffic. The data generated is then used to better understand the pollution levels in cities such as Macao.…”

Section: Introductionmentioning

confidence: 99%

Enabling Vehicular Data with Distributed Machine Learning

Chilipirea

Petre

Dobre

et al. 2015

Lecture Notes in Computer Science

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Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Transactions on computational collective intelligence.La publicació final està disponible a Springer a través de http://dx.doi. org/10.1007/978-3-662-49017-4_6 This is a copy of the author 's final draft version of an article published in the journal Transactions on computational collective intelligence. Because all roads are connected in a vast network, events in other parts of town can and will directly affect us. The more we know about the traffic inside a city, the better decisions we can make. Vehicular measurements may contain a vast amount of information about the way our cities function. Information that can be used for more than improving our commute, it is indicative of other features of the city like the amount of pollution in different regions. All the information and knowledge we can extract, can be used to directly improve our life. We live in a world where data is constantly generated and we store it and process it at an ever growing rate. Vehicular Data does not stray from this fact and is rapidly growing in size and complexity, with more and more ways to monitoring traffic, either from inside cars or from sensors placed on the road. Smartphones and in-car-computers are now common and they can produce a vast amount of data: it can identify a cars location, destination, current speed and even driving habits. Machine learning is the perfect complement for Big Data, as large data sets can be rendered useless without methods to extract knowledge and information from them. Machine learning, currently a popular research topic, has a large number of algorithms design to achieve this task, of knowledge extraction. Most of these techniques and algorithms can be directly applied to Vehicular Data.In this article we demonstrate how the use of a simple algorithm, kNearest Neighbors, can be used to extract valuable information from even a relatively small vehicular data set. Because of the vast size of most of our cities and the number of cars that are on their roads at any time of the day, standard machine learning systems do not manage to process data in a manner that would permit real time use of the extracted information. A solution to this problem is brought by distributed systems and cloud processing. By parallelizing and distributing machine learning algorithms we can use data at its highest potential and with little delay. Here, we * Corresponding author. Email: ciprian.dobre@cs.pub.ro show how this can be achieved by distributing the k-Nearest Neighbors machine learning algorithm over MPI. We hope this would motivate the research into other combinations of merging machine learning algorithms with Vehicular Data sets.

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Challenges and opportunities in immersive vehicular sensing: Lessons from urban deployments

Cited by 14 publications

References 15 publications

Supporting augmented floating car data through smartphone-based crowd-sensing

Supporting augmented floating car data through smartphone-based crowd-sensing

A Visualized Exploration in Urban Sensing of Air Quality with Vehicular Sensors

Enabling Vehicular Data with Distributed Machine Learning

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