An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

Sedghani, Hamta; Ardagna, Danilo; Passacantando, Mauro; Lighvan, ‪Mina Zolfy; Aghdasi, Hadi S.

doi:10.1016/j.adhoc.2021.102626

Cited by 19 publications

(2 citation statements)

References 38 publications

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“…Game theory methods, specially Stackelberg game and auction mechanism are the most common way to incentivize the users to run the sensing tasks and receive a reward in return. An incentive mechanism based on Stackelberg game has been proposed in [29], where the MCS platform estimates the users' utility function parameters and solves the optimization problem of the game in a centralized manner instead of exchanging a lot of messages in distributed methods.…”

Section: A Incentive Mechanisms In Mcsmentioning

confidence: 99%

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

et al. 2022

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Mobile Crowdsensing (MCS) is a new paradigm that leverages the collective sensing ability of a crowd so that a special task can be performed through the aggregation of information collected from personal mobile devices. While MCS brings several benefits, its application is prevented by challenges such as the efficient recruitment of users, effective mechanisms for rewarding users to encourage participation, and an effective and fast enough approach for managing the underlying resources that support large-scale MCS applications involving a large number of people in data collection. On the other hand, Artificial Intelligence (AI) applications, which are mostly based on Deep Neural Networks (DNN), are becoming pervasive today and are executed by the end users' mobile devices, which are characterised by limited memory and computing power, and low battery level. This paper describes and evaluates an incentive mechanism for a mobile crowdsensing system with an AI sensing task based on a one-leader multi-follower Stackelberg game. The MCS platform, as a leader, provides an AI sensing task to be executed by a DNN, which can be deployed in two different ways: fully on the user device or partially on the device and partially on edge or cloud resources. The users, as followers, make their decisions regarding their participation to the MCS system and select their desired deployment given the energy and memory available on their device and the deployment reward proposed by the MCS platform. The goals of the MCS platform are: i) to motivate the users to participate in the system, ii) to maximize its profit, and iii) to identify the optimal resources supporting the sensing task that minimizes the cost and provide performance guarantees. This problem has been formulated as a mixed integer nonlinear program and propose an efficient algorithmic approach to solve it quickly. The proposed approach has been compared with some baseline methods and with BARON state-of-the-art solver. Results show that our approach converges to the optimal solution much faster than BARON (up to orders of magnitude) especially in large scale systems. Furthermore, the comparison to the baseline methods shows that our approach always beats the best baseline method under different scenarios providing up to 16% improvement for the MCS platform profit.

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Section: A Incentive Mechanisms In Mcsmentioning

confidence: 99%

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

et al. 2022

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“…Obviously, "DL" is strongly related to the "neural network" in machine learning, and "neural network" is also its main algorithm and method. "DL" is called an "improved neural network" algorithm (Kumar et al, 2020;Sedghani et al, 2021). We present the theoretical model in Figure 1.…”

Section: Construction Of Game Performance Improvement Model Based On DLmentioning

confidence: 99%

An Empirical Study on the Influence of Mobile Games and Mobile Devices for Contemporary Students' Education and Learning Behavior

Lin

Liu

2022

Journal of Organizational and End User Computing

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This paper aims to realize the impact of mobile educational games on contemporary students' learning behavior. Firstly, the research status of educational games is analyzed. Secondly, an online game acceleration method is designed based on deep learning technology. Mobile games and learning behaviors are combined. College students and primary school students are selected as research samples. The main reasons for students' usage of mobile phones are analyzed through a questionnaire survey. In addition, the impact of different social media on students' learning behavior is analyzed. Finally, the experiment is carried out by integrating game elements into the teaching process of primary school students. The results show that about 60% of college students rarely or occasionally play mobile games. The remaining 13% never play games. It shows that many college students play games, but not many have been addicted to games for a long time.

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An incentive mechanism design for multitask and multipublisher mobile crowdsensing environment

Esmaeilyfard

Moghisi

2022

J Supercomput

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An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

Cited by 19 publications

References 38 publications

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

An Empirical Study on the Influence of Mobile Games and Mobile Devices for Contemporary Students' Education and Learning Behavior

An incentive mechanism design for multitask and multipublisher mobile crowdsensing environment

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