Incentive Mechanism for Socially-Aware Mobile Crowdsensing: A Bayesian Stackelberg Game

2020 ◽  
pp. 394-406
Author(s):  
Jiangtian Nie ◽  
Jun Luo ◽  
Zehui Xiong ◽  
Dusit Niyato ◽  
Ping Wang ◽  
...  
Keyword(s):  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Mobile Crowdsensing

scholarly journals A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4478
Author(s):  
Jing Zhang ◽  
Xiaoxiao Yang ◽  
Xin Feng ◽  
Hongwei Yang ◽  
An Ren
Keyword(s):  
Information Quality ◽  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Response Strategy ◽  
Mobile Users ◽  
Target Area ◽  
Mobile Crowdsensing ◽  
Sensing Data ◽  
Priority Order

Selection of the optimal users to maximize the quality of the collected sensing data within a certain budget range is a crucial issue that affects the effectiveness of mobile crowdsensing (MCS). The coverage of mobile users (MUs) in a target area is relevant to the accuracy of sensing data. Furthermore, the historical reputation of MUs can reflect their previous behavior. Therefore, this study proposes a coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory for MCS. First, the location information and the historical reputation of mobile users are used to select the optimal users, and the information quality requirement will be satisfied consequently. Second, a two-stage Stackelberg game is applied to analyze the sensing level of the mobile users and obtain the optimal incentive mechanism of the server center (SC). The existence of the Nash equilibrium is analyzed and verified on the basis of the optimal response strategy of mobile users. In addition, mobile users will adjust the priority of the tasks in time series to enable the total utility of all their tasks to reach a maximum. Finally, the EM algorithm is used to evaluate the data quality of the task, and the historical reputation of each user will be updated accordingly. Simulation experiments show that the coverage of the CRJC-IMA is higher than that of the CTSIA. The utility of mobile users and SC is higher than that in STD algorithms. Furthermore, the utility of mobile users with the adjusted task priority is greater than that without a priority order.

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

2021 ◽  
pp. 102626
Author(s):  
Hamta Sedghani ◽  
Danilo Ardagna ◽  
Mauro Passacantando ◽  
Mina Zolfy Lighvan ◽  
Hadi S. Aghdasi
Keyword(s):  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Budget Constraint ◽  
Mobile Crowdsensing

scholarly journals Incentive mechanism design for citizen reporting application using Stackelberg game

2022 ◽  
Vol 12 (2) ◽  
pp. 2147
Author(s):  
I Made Ariya Sanjaya ◽  
Suhono Harso Supangkat ◽  
Jaka Sembiring ◽  
Widya Liana Aji
Keyword(s):  
Optimization Problem ◽  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Budget Constraint ◽  
Multi Objective Optimization ◽  
Mobile Crowdsensing ◽  
Limited Budget ◽  
Incentive Model ◽  
The Incentive Mechanism ◽  
Incentive Mechanism Design

<p>The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.</p>

scholarly journals Incentive mechanism based on Stackelberg game under reputation constraint for mobile crowdsensing

2021 ◽  
Vol 17 (6) ◽  
pp. 155014772110230
Author(s):  
Xiaoxiao Yang ◽  
Jing Zhang ◽  
Jun Peng ◽  
Lihong Lei
Keyword(s):  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Stackelberg Equilibrium ◽  
Response Strategy ◽  
Past Performance ◽  
High Quality ◽  
Mobile Crowdsensing ◽  
Sensing Data ◽  
Resource Contribution ◽  
Flexible Application

Encouraging a certain number of users to participate in a sensing task continuously for collecting high-quality sensing data under a certain budget is a new challenge in the mobile crowdsensing. The users’ historical reputation reflects their past performance in completing sensing tasks, and users with high historical reputation have outstanding performance in historical tasks. Therefore, this study proposes a reputation constraint incentive mechanism algorithm based on the Stackelberg game to solve the abovementioned problem. First, the user’s historical reputation is applied to select some trusted users for collecting high-quality sensing data. Then, the two-stage Stackelberg game is used to analyze the user’s resource contribution level in the sensing task and the optimal incentive mechanism of the server platform. The existence and uniqueness of Stackelberg equilibrium are verified by determining the user’s optimal response strategy. Finally, two conversion methods of the user’s total payoff are proposed to ensure flexible application of the user’s payoff in the mobile crowdsensing network. Simulation experiments show that the historical reputation of selected trusted users is higher than that of randomly selected users, and the server platform and users have good utility.

Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach

2017 ◽  
Vol 129 ◽  
pp. 399-409 ◽  
Author(s):  
Yang Liu ◽  
Changqiao Xu ◽  
Yufeng Zhan ◽  
Zhixin Liu ◽  
Jianfeng Guan ◽  
...  
Keyword(s):  
Stackelberg Game ◽  
Incentive Mechanism ◽  
Edge Computing ◽  
Computation Offloading

scholarly journals The User Participation Incentive Mechanism of Mobile Crowdsensing Network Based on User Threshold

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hua Su ◽  
Qianqian Wu ◽  
Xuemei Sun ◽  
Ning Zhang
Keyword(s):  
Large Scale ◽  
Incentive Mechanism ◽  
User Participation ◽  
Task Completion ◽  
Processing Efficiency ◽  
Mobile Crowdsensing ◽  
Budget Control ◽  
Experiment Validation ◽  
Incentive Strategies ◽  
Incentive Strategy

Mobile crowdsensing (MCS) network means completing large-scale and complex sensing tasks in virtue of the mobile devices of ordinary users. Therefore, sufficient user participation plays a basic role in MCS. On the basis of studying and analyzing the strategy of user participation incentive mechanism, this paper proposes the user threshold-based cognition incentive strategy against the shortcomings of existing incentive strategies, such as task processing efficiency and budget control. The user threshold and the budget of processing subtasks are set at the very beginning. The platform selects the user set with the lowest threshold, and the best user for processing tasks according to users’ budget. The incentive cost of the corresponding users is calculated based on the user threshold at last. In conclusion, through the experiment validation and comparison with the existing user participation incentive mechanism, it was found that the user threshold-based incentive strategy is advantageous in improving the proportion of task completion and reducing the platform’s budget cost.

A Task-Oriented User Selection Incentive Mechanism in Edge-Aided Mobile Crowdsensing

2020 ◽  
Vol 7 (4) ◽  
pp. 2347-2360 ◽  
Author(s):  
Jinbo Xiong ◽  
Xiuhua Chen ◽  
Qing Yang ◽  
Lei Chen ◽  
Zhiqiang Yao
Keyword(s):  
Incentive Mechanism ◽  
User Selection ◽  
Mobile Crowdsensing ◽  
Task Oriented
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