A crowdsensing-based platform for transportation infrastructure monitoring and management in smart cities

Shirzad-Ghaleroudkhani, Nima; Mei, Qipei; Gül, Mustafa

doi:10.1016/b978-0-12-817784-6.00005-9

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The main focus of this paper is to come up with an incentive mechanism for the sensing tasks that are related to different dynamics of a city (e.g. road condition monitoring [3,4], mobility pattern [5], air pollution monitoring [6,7], noise pollution monitoring [8,9], etc.). From this perspective, a city can be viewed as different functional regions such as educational areas, residential areas, business areas, areas with major road segments, etc.…”

Section: Introductionmentioning

confidence: 99%

Double Auction based Incentive Mechanism for Participatory Sensing Systems

Middya

Roy

2023

Preprint

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The sensors that are available in smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users’ participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms.

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