Towards Smart Cities with IoT Based on Crowdsensing

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Towards Smart Cities with IoT Based on Crowdsensing.

The proliferation of Internet of Things (IoT) has paved the way for the future of smart cities. The large volume data over IoT can enable decision-making for various applications, such as smart transportation, smart parking and smart lighting. The key to the success of smart cities is data collection and aggregation over IoT. Recently, crowdsensing has become a new data collection paradigm over IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with the microphones on smartphones. Despite the advantages of crowdsensing and IoT, there are many challenges to utilize crowdsensing over IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users. This Special Section aims to solicit original research works that address the challenging problems in utilizing crowdsensing over IoT for smart cities.

The topics of interest include, but are not limited to:

  • Collaborative sensing and computing
  • Sparse mobile crowdsensing over IoT
  • Task allocation/selection in crowdsensing
  • Fair/long-term/quality-oriented incentive in crowdsensing
  • Data collection/aggregation in crowdsensing
  • Truth discovery in crowdsensing
  • Security, trust and privacy protection for IoT devices
  • Privacy-preserving data collection for crowdsensing
  • Fog computing and edge computing
  • IoT device monitoring and scheduling in smart city
  • Analysis for IoT-based Big Data
  • Vehicular crowdsensing networks in smart city
  • Resource management of IoT devices for smart city
  • Quality measurement of crowdsourced data for smart city
  • Crowdsourced data enabled applications in smart city

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.


Associate Editor:   

Kun Wang, University of California, Los Angeles, USA
Zhibo Wang, Wuhan University, China


Guest Editors:

    1. Ye-Qiong Song, University of Lorraine, France
    2. Dejun Yang, Colorado School of Mines, USA
    3. Shibo He, Zhejiang University, China
    4. Wei Wang, Amazon Inc, USA


Relevant IEEE Access Special Sections:

  1. Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
  2. Data Mining for Internet of Things
  3. Security, Privacy, and Trust Management in Smart Cities


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: