Название: Incentive Mechanism for Mobile Crowdsensing: A Game-theoretic Approach
Автор: Youqi Li, Fan Li, Song Yang, Chuan Zhang
Издательство: Springer
Серия: SpringerBriefs in Computer Science
Год: 2024
Страниц: 137
Язык: английский
Формат: pdf (true), epub
Размер: 12.1 MB

Mobile Crowdsensing (MCS) is emerging as a novel sensing paradigm in the Internet of Things (IoTs) due to the proliferation of smart devices (e.g., smartphones, wearable devices) in people’s daily lives. These ubiquitous devices enable the possibility of harnessing the wisdom of crowds by recruiting mobile users to collectively perform sensing tasks that largely collect data about a diverse range of human activities and the surrounding environment. However, users suffer from resource consumption like battery, computing power, and storage, which discourage users’ participation. To ensure a participation rate, it is necessary to employ an incentive mechanism to compensate users’ costs such that users are willing to take part in crowdsensing. Designing an appropriate incentive mechanism is nontrivial due to different practical challenges like modeling and computational hardness. Capturing the different roles’ utility maximization, game theory is widely used to address incentive mechanism design problems. While many existing papers study incentive mechanism in MCS, to the best of our knowledge, there is few books giving a comprehensive review of the incentive mechanism for MCS, especially from the game-theoretic perspective. This book aims to fill this void.

This book sheds light on designing incentive mechanisms for MCS in the context of game theory. Particularly, we present several game-theoretic models for MCS in different scenarios. In these game-theoretic models, many techniques are involved, such as the Stackelberg game, online learning, Lyapunov optimization, convex optimization, KKT condition, equilibrium analysis, and utility modeling. The purpose of this book is to fill in the book publishing gaps, especially in considering how game theory is applied to address incentive mechanism design problems for MCS.

This book is of particular interest to the readers and researchers in the field of IoT research, especially in the inter-discipline of network economics and IoT, because this book brings a number of innovative game-theoretic technologies to summarize the incentive mechanism and how to use this a set of model frameworks to address the practical issues of data collection in MCS.

The main benefits of reading this book include: (1) summarizing the game-theoretic incentive mechanism model and practice of MCS; (2) understanding the importance and design principle of incentive mechanism for MCS; (3) drawing inspiration from the book’s specific data collection applications, which provide game-theoretic solutions for designing incentive mechanism in more practical MCS fields.

The prerequisite for reading this book is a basic understanding of the mobile crowdsensing infrastructure, game theory, equilibrium analysis, and convex optimization.