Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method

Liu, Yazhi; Niu, Jianwei; Liu, Xiting

doi:10.1007/s00779-016-0932-x

Cited by 37 publications

(14 citation statements)

References 29 publications

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“…Similarly, in [14], the vehicle selection problem is formulated as a knapsack problem and a greedy optimal selection strategy is proposed to select vehicles with appropriate sensing capabilities in a given area and time under budget restrictions. In another work [15], vehicles are selected as participant for urban crowdsensing where the focus is to assign multiple sensory task to vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…• Only Rank: approach where the best vehicles are selected based on their reputation/rank irrespective of the cost, coverage and quality requirements [9]. • Only Budget: as a cost minimizing approach where the service provider selects the vehicles which cost less irrespecitve of their reputation, quality and coverage requirements [14] [16] [17]. • Greedy Variant: an approach where the maximum coverage providing vehicles are selected in a greedy manner [15].…”

Section: A Simulation Scenariomentioning

confidence: 99%

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ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection

Khan

Ghamri‐Doudane

2019

IEEE Trans. Veh. Technol.

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The growth in mobile devices results in constant generation and consumption of large amount of data by mobile users on the go which is unbearable by the current mobile networks in terms of cost and bandwidth. At the same time, the technological advancements in modern vehicles allow us to harness their computing, caching and communication capabilities to support various smart city applications. It is now possible to recruit set of connected vehicles to collect, store and share heterogeneous information regarding urban streets and facilitate citizens with different location-aware services. However, for a user to find and retrieve relevant content among the fleet of hundreds of vehicles on urban roads is challenging due to high mobility and intermittent connectivity. To address this, we propose ROVERS for a service provider to recruit the best set of vehicles to facilitate users on urban streets with different location-based applications. To identify the best vehicles, we first use a distributed ranking scheme CarRank, where the vehicle autonomously classifies itself as important with respect to urban users' interest. Then, we present a centralized recruitment scheme, exploiting game-theory for the service provider to fairly and optimally select the best vehicles under desired coverage, redundancy and quality requirements. Comparative analysis after in-depth simulations using realistic mobility traces of 2, 986 vehicles shows that the set of vehicles selected using ROVERS yield better results compared to other selection approaches.

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Section: Related Workmentioning

confidence: 99%

Section: A Simulation Scenariomentioning

confidence: 99%

ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection

Khan

Ghamri‐Doudane

2019

IEEE Trans. Veh. Technol.

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“…To exploit the power of citizen science, the crowdsensing approach has been recently used to collect travel data. Specifically, users are encouraged to self-report and share travel data via mobile devices [23][24][25][26][27]. Moreover, as a world-leading sports and fitness social media, Strava has released a data production named Strava Metro after aggregating individual GPS traces of Strava's users to the streets or census areas.…”

Section: Introductionmentioning

confidence: 99%

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Sun

Ren

Sun

2020

IJGI

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Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.

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“…Ul Hassan and Curry [14] studied the combinatorial fractional optimization approach for efficient task assignment. They used the semi-bandit learning method to reduce participants' travel costs [19]. They called this method the Distance-Reliability Ratio (DRR) algorithm.…”

Section: Related Workmentioning

confidence: 99%

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

Shin

2017

Symmetry

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With the development of sensor technology and the popularization of the data-driven service paradigm, spatial crowdsourcing systems have become an important way of collecting map-based location data. However, large-scale task management and location privacy are important factors for participants in spatial crowdsourcing. In this paper, we propose the use of an R-tree spatial cloaking-based task-assignment method for large-scale spatial crowdsourcing. We use an estimated R-tree based on the requested crowdsourcing tasks to reduce the crowdsourcing server-side inserting cost and enable the scalability. By using Minimum Bounding Rectangle (MBR)-based spatial anonymous data without exact position data, this method preserves the location privacy of participants in a simple way. In our experiment, we showed that our proposed method is faster than the current method, and is very efficient when the scale is increased.

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Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method

Cited by 37 publications

References 29 publications

ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection

ROVERS: Incentive-Based Recruitment of Connected Vehicles for Urban Big Data Collection

Uber Movement Data: A Proxy for Average One-way Commuting Times by Car

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

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