Advanced Research in Mobile Crowd Sensing Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 5790
Special Issue Editors
Interests: mobile intelligent computing; mobile big data; blockchain and privacy protection
Interests: Internet of Things systems; smart cities
Special Issues, Collections and Topics in MDPI journals
Interests: mobile crowdsensing;mobile edge computing;VANETs
Interests: Mobile Crowdsensing; Mobile Computing; Data Mining
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the development of Internet of Things technologies and the proliferation of mobile smart devices, mobile crowdsensing (MCS) has attracted a great deal of attention from industry and academia. MCS utilizes large number of participants to perform various sensing tasks via the built-in sensors of mobile devices. Compared to traditional wireless sensor networks, MCS can make full use of mobile devices, participants, and networks to achieve strong sensing ability, large coverage, low deployment cost, and high scalability. In recent years, many interesting and useful MCS applications have already been presented and developed, such as air quality monitoring, target tracking, digital map updating, road condition detection, and so on.
Although many researchers have paid attention to MCS, it still faces many challenges. First of all, MCS has to face a great deal of uncertainty in terms of participants, sensing tasks, and even communication scenarios. Thus, how to adjust the strategies (participant selection, task allocation, etc.) to deal with these uncertainties is one of the most pressing challenges. Secondly, MCS aims to provide fine-grained and large-scale sensing services by utilizing widespread participants and large amounts of mobile devices. However, there might still be some data that cannot be sensed. Moreover, the sensing data might also have some errors caused by devices or some extreme events. Thus, how to deal with the missing, corrupted, and abnormal sensing data is another challenge in MCS. Finally, MCS also needs to provide secure and trustworthy data sensing services. In consideration of these points, this Special Issue of Sensors aims to present novel studies in terms of mobile crowdsensing, spatial crowdsourcing, data filling in sparse crowdsensing, and so on.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Mobile crowdsensing applications;
- Machine learning for mobile crowdsensing;
- Incentive mechanisms for mobile crowdsensing;
- Security and trust of mobile crowdsensing;
- Privacy preserving in mobile crowdsensing;
- Truth discovery in mobile crowdsensing;
- Blockchain-based mobile crowdsensing systems;
- Federated learning for mobile crowdsensing;
- Edge computing for mobile crowdsensing;
- Crowdsensing data trading and sharing;
- Big data spatial-temporal analysis in crowdsensing;
- Crowdsensing for intelligent transportation applications.
We look forward to receiving your contributions.
Prof. Dr. Mingjun Xiao
Prof. Dr. Ning Wang
Prof. Dr. Huan Zhou
Prof. Dr. En Wang
Guest Editors
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Keywords
- mobile crowdsensing
- machine learning
- incentive mechanism
- security and trust
- privacy preserving
- truth discovery
- blockchain
- federated learning
- edge computing
- data trading
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