Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Abstract

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support

“…Game theory can be applied to define the incentive model where multiple players are involved and the interaction between players in the form of cooperative and noncooperative games. Incentive mechanisms can be defined for crowdsensing in the context of a CPSS, such as the classification of users based on the level of trust [14], while different incentive schemes for MCS can be classified in terms of platform-centric and user-centric approaches [15].…”

“…This section presents the research done in the field of serious games, with applicability in water resource management. There are different game theory models, such as cooperative and non-cooperative (alliances/competitions), information games (decisions based on perfect/complete/incomplete information), evolutionary games (strategy updates, highly competitive), static and dynamic games (simultaneous/sequential decisions), zero-sum games (sum of the payoffs equals zero, e.g., poker, chess) [14].…”

A Platform-Based Incentive Mechanism for Autonomous Vehicle Crowdsensing

“…We use Yang's result as part of our local level approach (Sensing Plan Adjusting (SPA) algorithm), and a greedy strategy to build the participant trajectories. The interested reader can find a comprehensive review of several game theoretical methods applied to crowdsensing here [22], [23].…”

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

“…Specifically, platform-centric methods refer to that the number, allocation, and adjustment of monetary rewards are charged by the organizer. For example, through some optimized incentive strategies based on game theory and statistics [252], the organizer leads the task and adjusts the strategy by measuring the individual/overall performance of the participants [253]. The user-centric methods are conducted in an auction manner, in which, generally, users bid for the sensing task published by the organizers, and the subset of participants with the lowest bid is dynamically allocated to complete the sensing tasks within minimal cost [254].…”

“…For example, this work did not include professional medical systems for medicare/rehabilitation/assisted living purposes, such as medical sensors [150]- [154], [312], Medical IoTs/CPSs [155]- [177], and medical robots [178]- [183]. Furthermore, there have been a number of great works surveying or reviewing this area and related fields [1], [15], [38], [92], [139], [155], [210], [214], [216], [220], [224], [252], [264], [275], [311], [313]- [318], while we have not compared our taxonomy systems with these works.…”