Target Tracking via Crowdsourcing: A Mechanism Design Approach

Cao, Nianxia; Brahma, Swastik; Varshney, Pramod K.

doi:10.1109/tsp.2015.2398838

Cited by 27 publications

(1 citation statement)

References 36 publications

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“…MKCP is widely adopted in the Computer Science community, where a certain resource must be allocated among different entities. In the work of Cao, Brahma, and Varshney (2015), a central information aggregator receives information from several selfish sensors, which can transmit it at several precision levels: the more the precision level the higher the sensor cost in terms of energy. The aggregator needs to select one precision level (or none) per sensor and compensates the corresponding loss of energy of each sensor via payments.…”

Section: Multiple Choice Knapsack Problem In Computer Science and Oth...mentioning

confidence: 99%

Personalised incentives with constrained regulator's budget

Javaudin,

Araldo,

de Palma

2023

Transportmetrica A: Transport Science

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We consider a regulator driving individual choices towards increasing social welfare by providing personal incentives. We formalize and solve this problem by maximizing social welfare under a budget constraint. The personalized incentives depend on the alternatives available to each individual and on her preferences. A polynomial time approximation algorithm computes a policy within few seconds. We analytically prove that it is boundedly close to the optimum. We efficiently calculate the Maximum Social Welfare Curve to achieve for a range of incentive budgets. This curve provides the right incentive budget to invest. We extend our formulation to enforcement, taxation and non-personalized-incentive policies. We analytically show that our personalized-incentive policy is also optimal within this class of policies and construct close-to-optimal enforcement and proportional tax-subsidy policies. We then compare analytically and numerically our policy with other state-of-the-art policies. Finally, we simulate a large-scale application to mode choice to reduce CO2 emissions.

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Section: Multiple Choice Knapsack Problem In Computer Science and Oth...mentioning

confidence: 99%

Personalised incentives with constrained regulator's budget

Javaudin,

Araldo,

de Palma

2023

Transportmetrica A: Transport Science

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Finding Mobility Pattern of Movable Target in Wireless Sensor Networks by Crowdsourcing Designed Mechanism

Dehdasht-Heydari

Kavand

2019

Wireless Pers Commun

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An Efficient Target Tracking Approach Through Mobile Crowdsensing

et al. 2019

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As the widespread of mobile devices in recent years, mobile crowdsensing (MCS) has become a powerful mechanism to produce knowledge by collecting the individual contributed sensor data. In this paper, we aim to solve the target tracking problem through mobile crowdsensing. The traditional tracking method tends to rely on photos or videos provided by pre-deployed monitors, which may consume much power resources. Different from the traditional tracking method, the tracking approach through mobile crowdsensing (TAMC) proposed in this paper utilizes the wireless communication of mobile users to collect and contribute the valuable information about the target's whereabouts. Specifically, whenever the mobile users witness the target person, they will take photos of the target person and report the location and time of witnessing the target to the platform. Due to the fact that mobile users communicate with the platform only when they witness the target, the crowdsensing network composed of mobile users can be seen as a green network. In this way, the visited location history and corresponding time sequence of the target are available through the reports of mobile users. Once a new report is uploaded to the platform, the location history is updated. Then, according to the latest report, we apply a tree-based location prediction model named XGBoost, which is a scalable machine learning system, to predict the next place to be visited by the target. Finally, we conduct extensive experiments on a large-scale real-world dataset, namely, the Gowalla check-in dataset. The experimental results show that compared with the baseline methods, the tracking approach can predict the next places accurately.INDEX TERMS Mobile crowdsensing, target tracking, mobility prediction.

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Target Tracking via Crowdsourcing: A Mechanism Design Approach

Cited by 27 publications

References 36 publications

Personalised incentives with constrained regulator's budget

Personalised incentives with constrained regulator's budget

Finding Mobility Pattern of Movable Target in Wireless Sensor Networks by Crowdsourcing Designed Mechanism

An Efficient Target Tracking Approach Through Mobile Crowdsensing

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