A sensing coverage analysis of a route control method for vehicular crowd sensing

Masutani, Osamu

doi:10.1109/percomw.2015.7134070

Cited by 21 publications

(10 citation statements)

References 15 publications

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“…Solutions belonging to the latter category, on the other hand, typically involve custom routing algorithms that aim at distributing vehicles more uniformly on the road network, and do not consider the problem of incentivizing drivers to accept possibly sub-optimal routes (e.g. : [2], [23], [24]). Thus, the solution we propose is not directly comparable with any of the above-mentioned approaches, as it combines the idea of a custom routing algorithm designed to achieve a more uniform sensing coverage with the key concepts of incentivization and budgetary constraints that are typically used only in solutions supporting ad-hoc sensing tasks.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, very little research has been aimed at obtaining, in a cost-effective way, an overall broader and more uniform sensing coverage (e.g. : [1], [24]), even if this is required for a multitude of use cases, such as air quality monitoring in a urban scenario. Moreover, most incentivization mechanisms presented in the literature require a centralized, sensing demand-aware computing infrastructure to decide which vehicles should be recruited for sensing [47], to compute their new trajectories and to allocate the given incentivization budget.…”

Section: Introductionmentioning

confidence: 99%

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Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: A Decentralized Incentivization Solution

Martino

Starace

2022

IEEE Open J. Intell. Transp. Syst.

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Vehicular Crowd-Sensing (VCS) is a well-known data collection approach leveraging sensors of connected vehicles to efficiently gather contextual information in urban environments. High-mileage vehicles such as taxis are often regarded as effective VCS platforms, due to their pervasiveness in modern cities, even though the road network coverage achievable by these vehicles is still an open issue. Indeed, their drivers generally follow the most-efficient route to destination, leading to major roads being frequently visited, while others are often neglected. To address this issue, many centralized incentivization solutions have been proposed to recruit/reward drivers accepting minor detours towards roads with higher sensing demand. However, these works mostly focus on assigning specific sensing tasks to drivers, rather than achieving an overall better-balanced urban sensing coverage, which is nonetheless required for many use cases, such as air quality monitoring. To fill this gap, we present ROUTR, an incentivization budget-aware routing solution designed to achieve more uniform coverage in VCS without requiring central coordination, thus significantly reducing back-end infrastructure costs. We empirically evaluated the proposal using taxi traces collected in the City of San Francisco. Results highlighted that, even with small incentivization budgets, our proposal leads to significantly more uniform urban road network coverage.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: A Decentralized Incentivization Solution

Martino

Starace

2022

IEEE Open J. Intell. Transp. Syst.

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“…In order to solve the problem, they proposed an agent-based data collection scheme that can help achieve close to 100% data collection rate. Similarly, in order to enhance sensing coverage, Masutani [39] proposed a route control method. The simulation experiment shows that the sensing coverage can be enhanced significantly without increasing the number of sensing vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…[38,39], Zhang et al [40] proposed the maximum coverage quality with a budget constraint problem. They proposed a new algorithm by selecting some of mobile users to maximize the coverage quality.…”

Section: Related Workmentioning

confidence: 99%

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

Wang

et al. 2017

Sensors

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Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

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“…Masutani [22] proposes a route control method to maximize the coverage of vehicle crowdsensing. In his work, he uses a coverage metric based on sensing demands.…”

Section: The Work By Zhang Et Al Introduces a Coverage Metric For Mobile Crowd-mentioning

confidence: 99%

A delay-aware coverage metric for bus-based sensor networks

Cruz

Couto

Costa

et al. 2020

Computer Communications

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Embedding sensors in urban buses is a promising strategy to deploy city-wide wireless sensor networks. By taking advantage of the mobility of buses, it is possible to achieve extended spatial coverage with fewer sensors as compared to a static setup. The trade-offs are that urban buses only cover part of the city, and that the frequency of the buses, and consequently of the data collection, is inhomogeneous across the city. Depending on the communication technology, buses may be unable to deliver the collected data on time. In this paper, we propose a coverage metric that takes into account the delivery delays of sensed data and the frequency at which a given region is sensed. The metric indicates, for a given time window, the proportion of streets that can be sensed under the requirements of an application. We apply the metric to more than 19 million GPS coordinates of 5,706 buses in Rio de Janeiro, mapping the coverage achieved for different application needs during a week. We build an abacus relating the coverage to different application requirements. We also calculate the coverage of a scenario only with static sensors. We show, among several observations, that bus-based sensing increases the coverage of applications by up to 7.6 times, in the worst case analyzed, when compared to a pure static scenario.

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A sensing coverage analysis of a route control method for vehicular crowd sensing

Cited by 21 publications

References 15 publications

Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: A Decentralized Incentivization Solution

Towards Uniform Urban Map Coverage in Vehicular Crowd-Sensing: A Decentralized Incentivization Solution

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

A delay-aware coverage metric for bus-based sensor networks

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