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.

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 Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 325 ◽  
Author(s):  
Shijun Chen ◽  
Huwei Chen ◽  
Shanhe Jiang
Keyword(s):  
Electric Vehicles ◽  
Heterogeneous Networks ◽  
Demand Uncertainty ◽  
Stackelberg Game ◽  
Optimal Solution ◽  
Stackelberg Equilibrium ◽  
Optimal Decision ◽  
Charging Station ◽  
Simulation Results ◽  
Grid Operators

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.

scholarly journals Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2391 ◽  
Author(s):  
Dan Tao ◽  
Shan Zhong ◽  
Hong Luo
Keyword(s):  
Incentive Mechanism ◽  
Extensive Study ◽  
Mobile Crowd Sensing ◽  
Space Correlation ◽  
Data Utility ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Time Space ◽  
Punishment Mechanism ◽  
Mobile Crowd

Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.

scholarly journals Dynamic SPICE-model of resource allocation in marketing networks

2020 ◽  
Vol 13 ◽  
pp. 8-23
Author(s):  
Movlatkhan T. Agieva ◽  
◽  
Olga I. Gorbaneva ◽  
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Target Audience ◽  
Opinion Leaders ◽  
Stackelberg Equilibrium ◽  
Common Interest ◽  
Theoretic Model ◽  
Goods And Services ◽  
Spice Model ◽  
Private Interests

We consider a dynamic Stackelberg game theoretic model of the coordination of social and private interests (SPICE-model) of resource allocation in marketing networks. The dynamics of controlled system describes an interaction of the members of a target audience (basic agents) that leads to a change of their opinions (cost of buying the goods and services of firms competing on a market). An interaction of the firms (influence agents) is formalized as their differential game in strategic form. The payoff functional of each firm includes two terms: the summary opinion of the basic agents with consideration of their marketing costs (a common interest of all firms), and the income from investments in a private activity. The latter income is described by a linear function. The firms exert their influence not to all basic agents but only to the members of strong subgroups of the influence digraph (opinion leaders). The opinion leaders determine the stable final opinions of all members of the target audience. A coordinating principal determines the firms' marketing budgets and maximizes the summary opinion of the basic agents with consideration of the allocated resources. The Nash equilibrium in the game of influence agents and the Stackelberg equilibrium in a general hierarchical game of the principal with them are found. It is proved that the value of opinion of a basic agent is the same for all influence agents and the principal. It is also proved that the influence agents assign less resources for the marketing efforts than the principal would like.

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
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