Mobile crowdsensing is a popular platform that takes advantage of the onboard sensors and resources on mobile nodes. The crowdsensing platform chooses to assign several sensing tasks each day, whose utility is based o...Mobile crowdsensing is a popular platform that takes advantage of the onboard sensors and resources on mobile nodes. The crowdsensing platform chooses to assign several sensing tasks each day, whose utility is based on the quality of harvested sensing data, the payment of transmitting data, and the recruitment of mobile nodes. An Internet serviceprovider (ISP) selects a portion of access points (APs) to power on for uploading data, whose utility depends on threeparts: the traffic income of transmitting sensing data, the energy cost of operating APs, and the energy cost of data transmissions by APs. The interaction between the crowdsensing platform and ISP is formulated as an iterated game, with social welfare defined as the sum of their expected utilities. In this paper, our objective is to unilaterally control social welfare without considering the opponent’s strategy, with the aim of achieving stable and maximized social welfare. Toachieve this goal, we leverage the concept of a zero-determinant strategy in the game theory. We introduce a zero-determinant strategy for the vehicular crowdsensing platform (ZD-VCS) and analyze it in discrete and continuous models in thevehicular crowdsensing scenario. Furthermore, we analyze an extortion strategy between the platform and ISP. Experimental results demonstrate that the ZD-VCS strategy enables unilateral control of social welfare, leading to a high andstable value.展开更多
Mobile Crowdsensing(MCS)represents a transformative approach to collecting data from the environment as it utilizes the ubiquity and sensory capabilities of mobile devices with human participants.This paradigm enables...Mobile Crowdsensing(MCS)represents a transformative approach to collecting data from the environment as it utilizes the ubiquity and sensory capabilities of mobile devices with human participants.This paradigm enables scales of data collection critical for applications ranging from environmental monitoring to urban planning.However,the effective harnessing of this distributed data collection capability faces significant challenges.One of the most significant challenges is the variability in the sensing qualities of the participating devices while they are initially unknown and must be learned over time to optimize task assignments.This paper tackles the dual challenges of managing task diversity to mitigate data redundancy and optimizing task assignment amidst the inherent variability of worker performance.We introduce a novel model that dynamically adjusts task weights based on assignment frequency to promote diversity and incorporates a flexible approach to account for the different qualities of task completion,especially in scenarios with overlapping task assignments.Our strategy aims to maximize the overall weighted quality of data collected within the constraints of a predefined budget.Our strategy leverages a combinatorial multi-armed bandit framework with an upper confidence bound approach to guide decision-making.We demonstrate the efficacy of our approach through a combination of regret analysis and simulations grounded in realistic scenarios.展开更多
基金supported by the National Key Research and Development Program of China under Grant Nos.2021YFB3101302 and 2021YFB3101303the National Natural Science Foundation of China under Grant Nos.62020106013 and 82241060。
文摘Mobile crowdsensing is a popular platform that takes advantage of the onboard sensors and resources on mobile nodes. The crowdsensing platform chooses to assign several sensing tasks each day, whose utility is based on the quality of harvested sensing data, the payment of transmitting data, and the recruitment of mobile nodes. An Internet serviceprovider (ISP) selects a portion of access points (APs) to power on for uploading data, whose utility depends on threeparts: the traffic income of transmitting sensing data, the energy cost of operating APs, and the energy cost of data transmissions by APs. The interaction between the crowdsensing platform and ISP is formulated as an iterated game, with social welfare defined as the sum of their expected utilities. In this paper, our objective is to unilaterally control social welfare without considering the opponent’s strategy, with the aim of achieving stable and maximized social welfare. Toachieve this goal, we leverage the concept of a zero-determinant strategy in the game theory. We introduce a zero-determinant strategy for the vehicular crowdsensing platform (ZD-VCS) and analyze it in discrete and continuous models in thevehicular crowdsensing scenario. Furthermore, we analyze an extortion strategy between the platform and ISP. Experimental results demonstrate that the ZD-VCS strategy enables unilateral control of social welfare, leading to a high andstable value.
基金supported in part by NSF(Nos.SaTC 2310298,CNS 2214940,CPS 2128378,CNS 2107014,and CNS 2150152).
文摘Mobile Crowdsensing(MCS)represents a transformative approach to collecting data from the environment as it utilizes the ubiquity and sensory capabilities of mobile devices with human participants.This paradigm enables scales of data collection critical for applications ranging from environmental monitoring to urban planning.However,the effective harnessing of this distributed data collection capability faces significant challenges.One of the most significant challenges is the variability in the sensing qualities of the participating devices while they are initially unknown and must be learned over time to optimize task assignments.This paper tackles the dual challenges of managing task diversity to mitigate data redundancy and optimizing task assignment amidst the inherent variability of worker performance.We introduce a novel model that dynamically adjusts task weights based on assignment frequency to promote diversity and incorporates a flexible approach to account for the different qualities of task completion,especially in scenarios with overlapping task assignments.Our strategy aims to maximize the overall weighted quality of data collected within the constraints of a predefined budget.Our strategy leverages a combinatorial multi-armed bandit framework with an upper confidence bound approach to guide decision-making.We demonstrate the efficacy of our approach through a combination of regret analysis and simulations grounded in realistic scenarios.
