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Sustainable Incentive Mechanisms for Mobile Crowdsensing

Feature Topic


Mobile devices are explosively growing in our daily lives. It is estimated that the number of smartphones in use globally has reached the 2-billion milestone in 2015. These mobile devices are widely equipped with sophisticated embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. The emerging paradigm of crowdsensing allows this large number of mobile devices to measure phenomena of common interest, which provide a new societal fashion of data sensing and sharing. A typical crowdsensing application leverages the ubiquitous mobile devices and the pervasive wireless network infrastructure to collect and analyze sensed data far beyond the scale of what was possible before, without the need to deploy thousands of static sensors.

The incentive mechanism is the most critical concern in the development of mobile crowdsensing because “crowd” participants are the foundations of all crowdsensing applications. Classic incentive mechanisms attract numerous participants by competitive payment designs. However, to achieve a sustainable crowdsensing, advanced incentive mechanisms need to pay attention to not only the payment but also many other features such as energy conservation and secure communications. For example, mobile devices may be compromised by hackers, important data may be stolen by eavesdroppers during wireless communications, and partially sensed data may expose participants’ private information. Obviously, without secure communications, a crowdsensing application will keep losing its participants even with good pay. Although plenty of incentive mechanisms have been developed for mobile crowdsensing, many challenges still remain to be addressed. It is important to explore this timely research topic to support the promising crowdsensing in practice.

This feature topic is intended to promote high-quality research in “Sustainable Incentive Mechanisms for Mobile Crowdsensing”, and move the theoretical and practical boundaries forward for a deeper understanding in fundamental algorithms, modeling, and analysis techniques from academic and industrial viewpoints. Authors from both academia and industry are invited to submit unpublished papers to this feature topic. The topics suggested can be discussed in terms of concepts, the state of the art, standards, implementations and evaluation, and running experiments and/or applications. Topics of interest include, but are not limited to:

  • New platform/architecture/infrastructure for mobile crowdsensing
  • Security and privacy in incentive mechanisms for mobile crowdsensing
  • Energy-efficient incentive mechanisms for mobile crowdsensing
  • Data-driven incentive mechanisms for mobile crowdsensing
  • Practical implementations of large-scale mobile crowdsensing
  • Sustainable social based mobile crowdsensing
  • Reliable communication paradigm for sustainable crowdsensing
  • Game theory in incentive mechanisms
  • Green wireless communications in sustainable crowdsensing


Linghe Kong
Shanghai Jiao Tong University, China

Kui Ren
State University of New York at Buffalo, USA

Muhammad Khurram Khan
King Saud University, Kingdom of Saudi Arabia

Qi Li
Tsinghua University, China

Ammar Rayes
Cisco Systems, USA

Mérouane Debbah
Huawei, France

Yuichi Nakamura
NEC Corporation, Japan