Joint Scheduling and Incentive Mechanism for Spatio-Temporal Vehicular Crowd Sensing

Fan, Guiyun; Jin, Haiming; Liu, Weiqi; Qin, Wei; Gan, Xiaoying; Long, Huan; Fu, Luoyi; Wang, Xinbing

doi:10.1109/tmc.2019.2960328

Cited by 39 publications

(13 citation statements)

References 44 publications

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“…They formulated the incentivizing problem as a knapsack problem and proposed an algorithm named iLOCuS to solve the problem. Fan et al [47] proposed a joint trajectory scheduling and incentive mechanism for spatio-temporal crowd sensing systems. They designed an online incentive mechanism that decided whether to recruit a participant when he/she asked to contribute sensing data.…”

Section: Incentive Mechanisms For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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Mobile crowd sensing (MCS) with human participants has been proposed as an efficient way of collecting data for smart cities applications. However, there often exist situations where humans are not able or reluctant to reach the target areas, due to for example traffic jams or bad road conditions. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a task allocation method, called "UMA" (UAV-assisted Multi-task Allocation method) to optimize the sensing coverage and data quality. The method incentivizes human participants to contribute sensing data from nearby points of interest (PoIs), with a limited budget. Meanwhile, the method jointly considers the optimization of task assignment and trajectory scheduling. It schedules the trajectories of UAVs, considering the locations of human participants, other UAVs and PoIs which are rarely visited by human participants. In detail, UAVs take care of two tasks in our proposal. One is to calibrate the data collected by the human participants whom the UAVs come across along their trajectories. The other is to collect data from the PoIs which are not covered by other UAVs or human participants. We apply deep reinforcement learning to schedule UAVs moving trajectories and sensing activities in order to minimize the overall energy cost. We evaluate the proposed scheme via simulation using two real data sets. The results show that our proposal outperforms the compared methods, in terms of coverage completed ratio, calibrating ratio, energy efficiency, and task fairness.

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Section: Incentive Mechanisms For Mcsmentioning

confidence: 99%

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Gao

Feng

Xiao

et al. 2023

IEEE Trans. on Mobile Comput.

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“…In addition, high user participation is required for keeping sensed information up-to-date. Several research initiatives have already been dealing with the topic of incentives [ 12 , 13 , 14 ], primarily focusing on monetary incentives, which can be tuned to favor honest reports while minimizing expenditures for the provider. Such techniques include reverse auction approaches [ 15 ], Stackelberg game models [ 16 ], reputation systems that quantify users’ trustworthiness [ 17 ], as well as systems for estimating and utilizing the users’ expertise [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments

Hirth

Seufert

Lange³

et al. 2021

Sensors

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Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.

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“…Thus, the solutions proposed in these works do not consider important aspects that should be considered when the client is a vehicle, such as fast mobility, frequent network topology changes, and variations in wireless communication channels [17]. In [18], clients are smartphones of people inside vehicles that send tasks to be processed on traditional cloud servers. However, in this computation offloading process, the distinctive aspects of vehicular networks are also not considered, and the objectives do not include reducing the execution time of vehicular applications.…”

Section: Related Workmentioning

confidence: 99%

A context-oriented framework for computation offloading in vehicular edge computing using WAVE and 5G networks

Souza

Rego

Carneiro

et al. 2021

Vehicular Communications

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Joint Scheduling and Incentive Mechanism for Spatio-Temporal Vehicular Crowd Sensing

Cited by 39 publications

References 44 publications

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

A UAV-Assisted Multi-Task Allocation Method for Mobile Crowd Sensing

Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments

A context-oriented framework for computation offloading in vehicular edge computing using WAVE and 5G networks

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