Practical Incentive Mechanisms for IoT-Based Mobile Crowdsensing Systems | Zendy

Zhuojun Duan | Zendy; Ling Tian | Zendy; Mingyuan Yan | Zendy; Zhipeng Cai | Zendy; Qilong Han | Zendy; Guisheng Yin | Zendy

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Practical Incentive Mechanisms for IoT-Based Mobile Crowdsensing Systems

Author(s) -

Zhuojun Duan,

Ling Tian,

Mingyuan Yan,

Zhipeng Cai,

Qilong Han,

Guisheng Yin

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2751304

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Powerful smart terminals with rich set of embedded sensors promote the development of the Internet of Things (IoTs). Mobile crowdsensing systems (MCSs) can be formed by these mobile smart terminals from IoTs to collect and exchange data. The main idea of MCSs is to outsource sensing tasks (collecting data) to various mobile devices which are carried by people or vehicles. The design of incentive mechanisms in MCSs is one of the hottest current research topics. However, most of the existing studies focus on maximizing the utilities or social welfare while neglecting the practical requirements of MCSs surveillance applications. In this paper, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules. Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a (ε, μ)-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations.

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