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Heterogeneous Participant Recruitment for Comprehensive Vehicle Sensing.

Liu Y, Li X - PLoS ONE (2015)

Bottom Line: In the proposed strategy, the differences between the sensing data requirements and the collected sensing data are modeled.An optimization formula is established to model the optimal participant recruitment problem, and a participant utility analysis scheme is built based on the sensing and mobility features of vehicles.Real trace-driven simulations show that the proposed strategy can collect 85.4% of available sensing data with 34% incentive budget.

View Article: PubMed Central - PubMed

Affiliation: College of Information, North China University of Science and Technology, Tangshan 063009, China.

ABSTRACT
Widely distributed mobile vehicles wherein various sensing devices and wireless communication interfaces are installed bring vehicular participatory sensing into practice. However, the heterogeneity of vehicles in terms of sensing capability and mobility, and the participants' expectations on the incentives blackmake the collection of comprehensive sensing data a challenging task. A sensing data quality-oriented optimal heterogeneous participant recruitment strategy is proposed in this paper for vehicular participatory sensing. In the proposed strategy, the differences between the sensing data requirements and the collected sensing data are modeled. An optimization formula is established to model the optimal participant recruitment problem, and a participant utility analysis scheme is built based on the sensing and mobility features of vehicles. Besides, a greedy algorithm is then designed according to the utility of vehicles to recruit the most efficient vehicles with a limited total incentive budget. Real trace-driven simulations show that the proposed strategy can collect 85.4% of available sensing data with 34% incentive budget.

No MeSH data available.


Related in: MedlinePlus

Data coverage ratio under different incentive budgets.
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pone.0138898.g004: Data coverage ratio under different incentive budgets.

Mentions: This subsection evaluates the impact of total incentive budget on data coverage ratio in temporal and spacial dimensions. The relative data coverage ratio is calculated by the ratio between the amount of collected data and the amount of all the data could be collected in the target sensing area. The total incentive budget is the biggest impact factor to the data coverage ratio since it determines the number of the recruited participants, which further determines the amount of sensing data can be collected. Given the following total incentives for one system duty cycle, {3,6,9,12,15,18,30,45,53}, the sensing data coverage ratio results are given in Fig 4. The collected data could not match all the data requirements, even when 53 available vehicles were all selected. The reason is that the traces are not uniformly distributed in spatial and temporal spaces.


Heterogeneous Participant Recruitment for Comprehensive Vehicle Sensing.

Liu Y, Li X - PLoS ONE (2015)

Data coverage ratio under different incentive budgets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4583486&req=5

pone.0138898.g004: Data coverage ratio under different incentive budgets.
Mentions: This subsection evaluates the impact of total incentive budget on data coverage ratio in temporal and spacial dimensions. The relative data coverage ratio is calculated by the ratio between the amount of collected data and the amount of all the data could be collected in the target sensing area. The total incentive budget is the biggest impact factor to the data coverage ratio since it determines the number of the recruited participants, which further determines the amount of sensing data can be collected. Given the following total incentives for one system duty cycle, {3,6,9,12,15,18,30,45,53}, the sensing data coverage ratio results are given in Fig 4. The collected data could not match all the data requirements, even when 53 available vehicles were all selected. The reason is that the traces are not uniformly distributed in spatial and temporal spaces.

Bottom Line: In the proposed strategy, the differences between the sensing data requirements and the collected sensing data are modeled.An optimization formula is established to model the optimal participant recruitment problem, and a participant utility analysis scheme is built based on the sensing and mobility features of vehicles.Real trace-driven simulations show that the proposed strategy can collect 85.4% of available sensing data with 34% incentive budget.

View Article: PubMed Central - PubMed

Affiliation: College of Information, North China University of Science and Technology, Tangshan 063009, China.

ABSTRACT
Widely distributed mobile vehicles wherein various sensing devices and wireless communication interfaces are installed bring vehicular participatory sensing into practice. However, the heterogeneity of vehicles in terms of sensing capability and mobility, and the participants' expectations on the incentives blackmake the collection of comprehensive sensing data a challenging task. A sensing data quality-oriented optimal heterogeneous participant recruitment strategy is proposed in this paper for vehicular participatory sensing. In the proposed strategy, the differences between the sensing data requirements and the collected sensing data are modeled. An optimization formula is established to model the optimal participant recruitment problem, and a participant utility analysis scheme is built based on the sensing and mobility features of vehicles. Besides, a greedy algorithm is then designed according to the utility of vehicles to recruit the most efficient vehicles with a limited total incentive budget. Real trace-driven simulations show that the proposed strategy can collect 85.4% of available sensing data with 34% incentive budget.

No MeSH data available.


Related in: MedlinePlus