iLOCuS: Incentivizing Vehicle Mobility to Optimize Sensing Distribution in Crowd Sensing

Xu, Susu; Chen, Xinlei; Pi, Xidong; Joe‐Wong, Carlee; Zhang, Pei; Noh, Hae Young

doi:10.1109/tmc.2019.2915838

Cited by 63 publications

(32 citation statements)

References 61 publications

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“…This clearly shows the importance of adopting the recent advances in machine learning for generating revenues and meeting the market demands on intelligent IoT systems. Moreover, market models and pricing strategies are integral for maximizing the profit of selling products and services including wired and wireless network access [10]- [12], cloud computing [13], and mobile crowdsensing [14], [15], just to name a few.…”

Section: Related Workmentioning

confidence: 99%

“…Using (16) and (15) to determine the optimal service setting, we find that the optimal data size n * = 18.68 and subscription fee p * s = 0.41 resulting in an optimal profit of F (n * , p * s ) = 8.31.…”

Section: Separate Selling Of Servicesmentioning

confidence: 99%

“…The optimal solutions are n * 1 = 19.29, n * 2 = 7.01, p * b = 0.658 which generate an optimal profit of F K (p * b , n * 1 , n * 2 ) = 19.67. Figure 12 illustrates the market equilibrium of selling Services 1 and 2 separately or as one bundle under coalition K. Using (16), (15), and (5) to analyze the separate sales, we find that the optimal subscription fee and profit values of Services 1 and 2 are p * s1 = 0.41, F 1 (n * 1 , p * s1 ) = 8.31, p * s2 = 0.39, and F 2 (n * 2 , p * s2 ) = 9.58, respectively. • For separate sales (Figure 12a), the optimal market equilibrium is shown as the vertical θ 1 q 1 = p * s1 = 0.41 and horizontal θ 2 q 2 = p * s2 = 0.39 lines for Services 1 and 2, respectively.…”

Section: Service Bundlingmentioning

confidence: 99%

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Optimal Pricing of Internet of Things: A Machine Learning Approach

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IEEE J. Select. Areas Commun.

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Section: Related Workmentioning

confidence: 99%

Section: Separate Selling Of Servicesmentioning

confidence: 99%

Section: Service Bundlingmentioning

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Optimal Pricing of Internet of Things: A Machine Learning Approach

Alsheikh

Hoang

Niyato

et al. 2020

IEEE J. Select. Areas Commun.

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“…The system incentivized the participants to match the sensing distribution of the sampled data to the desired target distribution with a limited budget. They formulated the incentivizing problem as a knapsack problem and proposed an algorithm named iLOCuS to solve the problem [41].…”

Section: A Incentive Mechanismsmentioning

confidence: 99%

A Learning-Based Credible Participant Recruitment Strategy for Mobile Crowd Sensing

Gao

Xiao

Han

et al. 2020

IEEE Internet Things J.

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Mobile Crowd Sensing (MCS) acts as a key component of Internet of Things (IoTs), which has attracted much attention. In an MCS system, participants play an important role, since all the data is collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high quality data. In this paper, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations which relatively few data has so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality and truthfulness. We evaluate the proposed scheme via simulation using three real datasets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with other two methods.

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“…Multi-agent systems have drawn great attention recently due to their capabilities to handle complex and computation heavy tasks [1][2][3][4]. Benefiting from the distributed computation, the tasks can be distributed to multiple agents to execute independently or collaboratively, which have been proved effective in various applications including environmental monitoring, urban surveillance, system diagnosis, market simulation and etc [5][6][7].…”

Section: Introduction Multi-agent Systemmentioning

confidence: 99%

A generative simulation platform for multi-agent systems with incentives

Zhang

et al. 2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Multi-agent systems have attracted much attention in the recent years due to their capabilities to handle complex and computation-heavy tasks and compatibility with incentive schemes. Considering the difficulty of creating an actual prototype and environment for evaluation, a simulation platform is a cheap and efficient way in analyzing and testing, prior to real environmental implementations. Existing simulators for multi-agent systems are inadequate to analyze the effects of different customized incentive schemes on agents' behavior patterns due to two reasons: 1) They lack the functionality to support various types of complex incentives, e.g., mixture of monetary incentives and non-monetary incentives, which influences agents' behaviors explicitly and implicitly; 2) They are not able to emulate heterogeneous agents' realtime behaviors that are influenced by complex incentives and deviate from their original behavior patterns shown in historical traces. In this paper, we focus on mobile agents that can move in a patio-temporal space, and we present a physical knowledge aided multi-agent simulation platform considering the influence of both direct and indirect incentives unified through a general utility-driven agent reaction function. The behaviors of agents are then emulated in three * These three authors contribute equally to this work. Xinlei Chen is corresponding author.

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iLOCuS: Incentivizing Vehicle Mobility to Optimize Sensing Distribution in Crowd Sensing

Cited by 63 publications

References 61 publications

Optimal Pricing of Internet of Things: A Machine Learning Approach

Optimal Pricing of Internet of Things: A Machine Learning Approach

A Learning-Based Credible Participant Recruitment Strategy for Mobile Crowd Sensing

A generative simulation platform for multi-agent systems with incentives

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