A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Nie, Jiangtian; Luo, Jun; Xiong, Zehui; Niyato, Dusit; Wang, Ping

doi:10.1109/twc.2018.2885747

Cited by 152 publications

(70 citation statements)

References 48 publications

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“…Thus, in the existence of selfish and rational users, cooperativeness of the participants can be ensured through effective games [79]. A typical use case that leverage game theory is the design of incentives to recruit a sufficient size of participant population for the sensing campaigns [80]. In addition, pricing strategies for the value of the data so to eliminate untrutful participants in MCS campaigns [81] is to address the security and trustworthiness concerns in MCS campaigns.…”

Section: Presentation Of Common Game Theory Modelsmentioning

confidence: 99%

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Dasari

Kantarcı

Pouryazdan³

et al. 2020

Sensors

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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.

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Section: Presentation Of Common Game Theory Modelsmentioning

confidence: 99%

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Dasari

Kantarcı

Pouryazdan³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Both S A and S B are points on the elliptic curve and are computed using the point multiplication. With S A and S B , both the blockchain client and the miner compute (6) and 7as joint secret:…”

Section: T a = K Pra ∈ {23#e}smentioning

confidence: 99%

“…Basically, a crowd sensing platform consists of a cloud-based system and a group of sensing devices (mobile users). The platform publishes a set of sensing task with various purposes, while the mobile users participate in the sensing task [6]. Mobile crowd sensing also plays a key role in the actualization of smart cities [7].…”

Section: Introductionmentioning

confidence: 99%

Applying Diffie-Hellman Algorithm to Solve the Key Agreement Problem in Mobile Blockchain-based Sensing Applications

Owoh¹,

Mahinderjit²

2019

ijacsa

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Mobile blockchain has achieved huge success with the integration of edge computing services. This concept, when applied in mobile crowd sensing, enables transfer of sensor data from blockchain clients to edge nodes. Edge nodes perform proof-of-work on sensor data from blockchain clients and append validated data to the chain. With this approach, blockchain can be performed pervasively. However, securing sensitive sensor data in a mobile blockchain (client/edge node architecture) becomes imperative. To this end, this paper proposes an integrated framework for mobile blockchain which ensures key agreement between clients and edge nodes using Elliptic Curve Diffie-Hellman algorithm. Also, the framework provides efficient encryption of sensor data using the Advanced Encryption Standard algorithm. Finally, key agreement processes in the framework were analyzed and results show that key pairing between the blockchain client and the edge node is a non-trivial process.

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“…The incentive mechanism is the core of the MCS network [ 11 ]. Existing incentive mechanisms can be roughly divided into monetary [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] and non-monetary incentive mechanisms [ 20 , 21 ]. Compared with the non-monetary incentive mechanism, the monetary one is a more flexible reward method.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Yang

Feng

et al. 2020

Sensors

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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.

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A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

Cited by 152 publications

References 48 publications

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

Applying Diffie-Hellman Algorithm to Solve the Key Agreement Problem in Mobile Blockchain-based Sensing Applications

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

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