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Task Allocation and Incentive Mechanism Design for Mobile Crowdsensing

Task Allocation and Incentive Mechanism Design for Mobile Crowdsensing
Author: Tao, Xi
Publisher:
Total Pages: 0
Release: 2020
Genre: Crowdsourcing
ISBN:
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Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of users with mobile devices (e.g., smartphones, tablet computers, and wearables) are recruited as workers to move around in a specific region and carry out sensing tasks. There are two key problems in MCS, i.e., task allocation problem and incentive mechanism design. In this thesis, we build a MCS framework and provide solutions to its two key problems. Specifically, we focus on (1) the task allocation problem in the static scenarios, in which the information of tasks and workers are known at the beginning of sensing activities; (2) the task allocation problem in the dynamic scenarios when the platform cannot obtain the information of workers before their arrivals; and (3) the incentive mechanism design that motivates workers to participate in the sensing activities. Our proposed MCS framework is associated with two important components to deal with the task allocation problem and incentive mechanism design, respectively. The task allocation problem is considered and formulated as a path planning problem since the tasks in the MCS framework are generally location-dependent. We first plan paths for workers in the static scenarios. Meanwhile, we take two different modes of path planning into account, i.e., the platform-centric mode and workercentric mode. To solve the path planning problem in these two modes, we propose an evolutionary algorithm and a heuristic algorithm, respectively. Second, we investigate the dynamic task allocation problem. Although the platform has incomplete information of workers, we explore several online algorithms to achieve the satisfactory performance. Third, we design a location-protected and truthful incentive mechanism to motivate workers to move around and accomplish sensing tasks. Based on the results of path planning, we use the auction theory to ensure workers to provide their true private information including costs and task sets. The overall performance of our proposed framework is extensively evaluated through simulations and the simulation results illustrate the effectiveness and efficiency of our solutions in the proposed framework.

Mobile Crowd Sensing: Incentive Mechanism Design

Mobile Crowd Sensing: Incentive Mechanism Design
Author: Fen Hou
Publisher: Springer
Total Pages: 52
Release: 2018-10-24
Genre: Technology & Engineering
ISBN: 3030010244
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This SpringerBrief investigates and reviews the development and various applications of mobile crowd sensing (MCS). With the miniaturization of sensors and the popularity of smart mobile devices, MCS becomes a promising solution to efficiently collect different types of information, such as traffic conditions, air quality, temperature and more, which is covered in this brief. The features, novelty, and applications of MCS are elaborated in detail in this brief. In addition, the basic knowledge about auction theory and incentive mechanism design is introduced. Incentive mechanism design plays a key role in the success of MCS. With an efficient incentive mechanism, it is possible to attract enough mobile users to participate in a MCS system, thus enough high quality sensing data can be collected. Two types of incentive mechanisms with different system models are introduced in this brief. One is the reputation-aware incentive mechanism, and another is the social-aware incentive mechanism. This SpringerBrief covers the significance and the impacts of both reputation and social relationship of smartphone users (SUs) in MCS and presents extensive simulation results to demonstrate the good performance of the proposed incentive mechanisms compared with some existing counterparts. The target audience for this SpringerBrief is researchers and engineers in the area of wireless communication and networking, especially those who are interested in the mobile crowd sensing or incentive mechanism design. Meanwhile, it is also intended as a reference guide for advanced level students in the area of wireless communications and computer networks.

Incentive Mechanism for Mobile Crowdsensing

Incentive Mechanism for Mobile Crowdsensing
Author: Youqi Li
Publisher: Springer Nature
Total Pages: 137
Release: 2024-01-03
Genre: Computers
ISBN: 9819969212
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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 provide an opportunity to harness the wisdom of crowds by recruiting mobile users to collectively perform sensing tasks, which largely collect data about a wide range of human activities and the surrounding environment. However, users suffer from resource consumption such as battery, processing power, and storage, which discourages users’ participation. To ensure the participation rate, it is necessary to employ an incentive mechanism to compensate users’ costs such that users are willing to take part in crowdsensing. This book sheds light on the design of incentive mechanisms for MCS in the context of game theory. Particularly, this book presents several game-theoretic models for MCS in different scenarios. In Chapter 1, the authors present an overview of MCS and state the significance of incentive mechanism for MCS. Then, in Chapter 2, 3, 4, and 5, the authors propose a long-term incentive mechanism, a fair incentive mechanism, a collaborative incentive mechanism, and a coopetition-aware incentive mechanism for MCS, respectively. Finally, Chapter 6 summarizes this book and point out the future directions. This book is of particular interest to the readers and researchers in the field of IoT research, especially in the interdisciplinary field of network economics and IoT.

Efficient Online Incentive Mechanism Designs for Wireless Communications

Efficient Online Incentive Mechanism Designs for Wireless Communications
Author: Gang Li
Publisher: Springer Nature
Total Pages: 119
Release: 2024
Genre: Wireless communication systems
ISBN: 3031584538
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This book initially delves into its fundamentals to initiate the exploration of online incentive mechanisms in wireless communications. Three case studies are provided to elaborate details on designing online mechanism design in practical system. For crowdsensing with random task arrivals, this book introduces a linear online incentive mechanism model with insurance of the quality of information for each incoming task. In the context of edge computing systems, the authors model a nonlinear online incentive mechanism with the consideration of mobile users energy budget constraints. It also explores online incentive mechanism for collaborative task offloading in mobile edge computing to achieve on-arrival instant responses. This book not only disseminates current knowledge but also sheds light on future research directions. The design of incentive mechanisms in wireless communication systems is of paramount importance as it encourages dormant terminals within networks to contribute their valuable resources. The consideration of randomness of network processes enhances the mechanism design under online settings and decision making on the fly. This book endeavours to bridge existing knowledge gaps by comprehensively presenting and developing fundamental insights into online incentive mechanisms and their design methods in the realm of wireless communications. Its one of the first books to provide a comprehensive understanding of the fundamental principles of online incentive mechanisms and their intricately designed methods in the dynamic world of wireless communications. Future research directions include an investigation in the evolving domain of online incentive mechanism designs within wireless communications. This book strikes a balance between theoretical knowledge and practical application, making it a valuable resource for both researchers and practitioners in the field of wireless communications and network economics. Advanced-level students majoring in computer science and/or electrical engineering will want to purchase this book as a study guide.

Mobile Crowdsourcing

Mobile Crowdsourcing
Author: Jie Wu
Publisher: Springer Nature
Total Pages: 456
Release: 2023-07-16
Genre: Computers
ISBN: 3031323971
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This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted. This book will be of value to researchers and students targeting this topic as a reference book. Practitioners, government officials, business organizations and even customers -- working, participating or those interested in fields related to crowdsourcing will also want to purchase this book.

Mobile Crowdsensing

Mobile Crowdsensing
Author: Cristian Borcea
Publisher: CRC Press
Total Pages: 184
Release: 2016-12-01
Genre: Computers
ISBN: 1498738451
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Mobile crowdsensing is a technology that allows large scale, cost-effective sensing of the physical world. In mobile crowdsensing, mobile personal devices such as smart phones or smart watches come equipped with a variety of sensors that can be leveraged to collect data related to environment, transportation, healthcare, safety and so on. This book presents the first extensive coverage of mobile crowdsensing, with examples and insights drawn from the authors’ extensive research on this topic as well as from the research and development of a growing community of researchers and practitioners working in this emerging field. Throughout the text, the authors provide the reader with various examples of crowdsensing applications and the building blocks to creating the necessary infrastructure, explore the related concepts of mobile sensing and crowdsourcing, and examine security and privacy issues introduced by mobile crowdsensing platforms. Provides a comprehensive description of mobile crowdsensing, a one-stop shop for all relevant issues pertaining to mobile crowdsensing, including motivation, applications, design and implementation, incentive mechanisms, and reliability and privacy. Describes the design and implementations of mobile crowdsensing platforms of great interest for the readers working in research and industry to quickly implement and test their systems. Identifies potential issues in building such mobile crowdsensing applications to ensure their usability in real life and presents future directions in mobile crowdsensing by emphasizing the open problems that have to be addressed.

Multi-dimensional Urban Sensing Using Crowdsensing Data

Multi-dimensional Urban Sensing Using Crowdsensing Data
Author: Chaocan Xiang
Publisher: Springer Nature
Total Pages: 204
Release: 2023-03-23
Genre: Computers
ISBN: 9811990069
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Chaocan Xiang is an Associate Professor at the College of Computer Science, Chongqing University, China. He received his bachelor’s degree and Ph.D. from Nanjing Institute of Communication Engineering, China, in 2009 and 2014, respectively. He subsequently studied at the University of Michigan-Ann Arbor in 2017 (supervised by Prof. Kang G. Shin, IEEE Life Fellow, ACM Fellow). His research interests mainly include UAVs/vehicle-based crowdsensing, urban computing, Internet of Things, Artificial Intelligence, and big data. He has published more than 50 papers, including over 20 in leading venues such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE INFOCOM, and ACM Ubicomp. He has received a best paper award and a best poster award at two international conferences. Panlong Yang is a full Professor at the University of Science and Technology of China. He has been supported by the NSF Jiangsu through a Distinguished Young Scholarship and was honored as a CCF Distinguished Lecturer in 2015. He has published over 150 papers, including 40 in CCF Class A. Since 2012, he has supervised 14 master’s and Ph.D. candidates, including two excellent dissertation winners in Jiangsu Province and the PLA education system. He has been supported by the National Key Development Project and NSFC projects. He has nominated by ACM MobiCom 2009 for the best demo honored mention awards, and won best paper awards at the IEEE MSN and MASS. He has served as general chair of BigCom and TPC chair of IEEE MSN. In addition, he has served as a TPC member of INFOCOM (CCF Class A) and an associate editor of the Journal of Communication of China. He is a Senior Member of the IEEE (2019). Fu Xiao received his Ph.D. in Computer Science and Technology from the Nanjing University of Science and Technology, Nanjing, China, in 2007. He is currently a Professor and Dean of the School of Computer, Nanjing University of Posts and Telecommunications. He has authored more than 60 papers in respected conference proceedings and journals, including IEEE INFOCOM, ACM Mobihoc, IEEE JASC, IEEE/ACM ToN, IEEE TPDS, IEEE TMC, etc. His main research interest is in the Internet of Things. He is a member of the IEEE Computer Society and the Association for Computing Machinery. Xiaochen Fan received his B.S. degree in Computer Science from Beijing Institute of Technology, Beijing, China, in 2013, and his Ph.D. from the University of Technology Sydney, NSW, Australia, in 2021. His research interests include mobile/pervasive computing, deep learning, and Internet of Things (IoT). He has published over 25 peer-reviewed papers in high-quality journals and IEEE/ACM international conference proceedings.

Federated Learning

Federated Learning
Author: Qiang Yang
Publisher: Springer Nature
Total Pages: 291
Release: 2020-11-25
Genre: Computers
ISBN: 3030630765
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This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Mobile Computing, Applications, and Services

Mobile Computing, Applications, and Services
Author: Yuyu Yin
Publisher: Springer Nature
Total Pages: 245
Release: 2019-09-24
Genre: Computers
ISBN: 3030284689
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This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2019, held in Hangzhou, China, in June 2019. The 17 full papers were carefully reviewed and selected from 48 submissions. The papers are organized in topical sections on mobile application with data analysis, mobile application with AI, edge computing, energy optimization and application

Machine Learning for Cyber Security

Machine Learning for Cyber Security
Author: Xiaofeng Chen
Publisher: Springer Nature
Total Pages: 623
Release: 2020-11-10
Genre: Computers
ISBN: 3030624609
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This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.