Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning

Zhao, Yinuo; Liu, Chi Harold

doi:10.1109/tits.2020.3014263

Cited by 59 publications

(14 citation statements)

References 47 publications

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“…Such pervasive, real-time data gathered through VCS enables many novel and interesting use cases, ranging from the monitoring of spatio-temporal phenomena of interest to the creation of smarter Intelligent Transportation Systems [41]. In practice, however, vehicles are not uniformly distributed over the road-network [4], limiting the feasibility of many VCS-based use cases [49]. To overcome this limitation, many researchers have proposed ad-hoc solutions to help achieve an adequate distribution of the collected data to support VCS use cases.…”

Section: Related Workmentioning

confidence: 99%

“…a certain sensing task, and on a centralized recruitment back-end which uses a game-theoretical approach to select the best vehicles to recruit. In [49], the authors investigate the impact of including vehicular social networks effect and intrinsic rewards into incentive mechanisms design. In that work, the authors envision that vehicles benefiting from the data sensed by other participants (e.g.…”

Section: A Solutions Supporting Ad-hoc Sensing Tasksmentioning

confidence: 99%

“…Many prior works (e.g. : [7], [10], [16], [44], [49]) have proposed suitable incentive mechanisms to re-route these vehicles towards areas/streets in which there is a sensing need, offering some sort of monetary and/or social incentivization to compensate drivers willing to accept the rerouting. Nevertheless, to the best of our knowledge, most of these works focus on an ad-hoc sensing scenario, in which a central system, aware of sensing demands over the entire map, assigns incentivized tasks (e.g.…”

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: A Solutions Supporting Ad-hoc Sensing Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The aim of a VCS system is different from a VFC system but has common challenges like modeling vehicular mobility and the need for an incentive mechanism for the VCS system. Zhao et al [20] derived a longterm strategy to build a deep reinforcement learning-based incentive mechanism. They model the vehicle dynamics via a dynamic radio channel with a selection of sine, piece-wise linear, and Markov-chain channel models.…”

Section: Crowdsensing In Vehicular Networkmentioning

confidence: 99%

Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Sharma¹,

Butler²,

Jennings³

2021

Preprint

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Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of on-board sensors, video cameras and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behaviour of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics are developed to find optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimise resource usage. We compare our heuristic to an integer linear program solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency, which is a crucial requirement for safety-related applications.

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“…Therefore, suitable incentive mechanisms need to be devised for compensating users' contributions and promoting their participation in the monitoring tasks. Research approaches can be categorized in two main groups: monetary incentive mechanisms, in which users are paid with a monetary reward [274,275], and non-monetary mechanisms where instead users are rewarded with alternative incentives such as gaming, social entertainment, or virtual credits (e.g., coupons) [276,277]. In the former case, the monitoring system has the additional burden of implementing suitable automatic strategies to select the more convenient users, usually based on the distance from the task location.…”

mentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning

Cited by 59 publications

References 47 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

Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

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