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

Abstract

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.

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

“…The study then used a greedy approach to optimize participant recruitment to select the most appropriate participants for each task. Zhang et al used the mobile participant's location information and historical reputation to select the optimal participant to satisfy the information quality requirement [21]. Then, the study applied a two-stage Stackelberg game to analyze the perceived level of mobile users to obtain the optimal incentive mechanism.…”

Incentive techniques for the Internet of Things: A survey

“…The accuracy of the sensing data depends on the coverage of mobile users in the target area and also on the previous reputation of the mobile users [82], [83]. The work in [84] considered these factors for incentivizing the participants using Stackelberg game theory. In this work, the authors proposed to use a two-stage Stackelberg gaming approach to determine the levels of sensing of mobile users, who are chosen based on their previous reputation and coverage areas.…”

“…Mobile Crowdsensing [84] Stackelberg game A two-stage Stackelberg gaming approach is proposed to determine the incentive mechanism based on levels of sensing of mobile users, who are chosen based on their previous reputation and coverage areas.…”

Cooperation-Driven Virtual Terminal Coalition Formation Games for Task Assignment in Mobile Crowdsensing

“…At present, some researchers have summarized the research work of task assignment [22][23][24][25][26] and cooperation mechanism [27][28][29][30] from different perspectives.…”

“…In order to motivate the participants, an incentive mechanism was designed by taking the social network effect, which was from the underlying mobile social field, into consideration. An incentive mechanism algorithm of joint coverage and reputation based on Stackelberg game theory was developed in [25]. In the first place, location information and the historical reputation of mobile users were used to select optimal users, meeting the user's requirements for information quality.…”