Compared to privately-deployed sensor networks, human-centric sensing solutions like mobile crowdsensing systems can achieve more flexible and economical sensing. However such systems suffer from unpredictable human behaviour and privacy concerns, which affect usability and accuracy. This project aims at developing a series of human-centric sensing solutions with data quality guarantees in privacy-preserving scenarios, which can be widely adopted to solve real-world problems with economical, large scale, and relatively accurate sensing requirements (e.g. urban information collection, environment monitoring and exploring).

Explaining the science

User incentivisation 

Because of the self-interested nature of humans, potential participants will not take part in a sensing task unless there is considerable reward. However, since the difference in participants’ proficiencies in specific sensing tasks could be massive, in terms of knowledge, sensing capacity, and available time, the reward to each participant needs to be determined subtly as an effective stimulation. This project aims at modelling human-centric sensing tasks as competitive games among the service provider and multiple participants, and developing rational rewarding schemes to encourage active participation.

Truthful reporting

Since privacy-preservation is critical to the practical implementation of human-centric sensing, the true identity and daily preference of human participators must not be revealed to the service provider. However, such a mandatory requirement casts serious challenge on the truthfulness of the information claimed by each participant, since there is no referable information about historical behaviours of the participants. This project aims at solving the untruthful reporting problem by developing strategy-proof rewarding methods which will theoretically ensure that truthful reporting is always the best choice for each participants.

Data quality evaluation and enhancement

The result of human-centric sensing is determined on reported observations from an undetermined number of human participants. The evaluation of the quality of reported data is challenging since there is no referable ground truth. This project aims at discovering the sensing truth among reported observations using distributed data analysis methods considering the difference among each participant’s proficiency. Furthermore, adaptive methods based on reinforcement learning should also be developed to allow all participants to incrementally enhance their data quality for higher sensing profits.

Project aims

The overarching aim of this project is to deliver real-world application-driven research into human-centric sensing techniques, by developing the applicable theory, algorithms, architectures and applications. Focusing on the problem of sensing data that lacks predictive quality, a thorough human-centric sensing framework, entailing user recruitment to quality-driven rewarding is expected. 

Potential solutions to specific real-world problems like urban traffic monitoring and management, environmental pollution monitoring and mapping, and adaptive location-based recommendation should also be explored.

The expected results in terms of user incentivisation, truthful reporting, and data quality evaluation and enhancement will provide an enlightening solution to the data quality problem that is hindering the practical implementation of emerging human-centric sensing applications. These application could be a significant accompaniment to existing privately-deployed sensing infrastructures.

This project is part of the Data-centric engineering programme's Grand Challenge of 'Resilient and robust infrastructure'.


The expected results of this work can be applied to any sensing applications that depend on multiple self-interested agents to collect high quality sensing data, where agents’ privacy are preserved and none of the historical information is available. 

For example, promising applications include (but are not limited to) crowdsourced urban traffic modelling and scheduling, fine grained air quality and noise monitoring, and crowd-based environmental outlier detection and alarming (e.g. fire, flood).

Recent updates

September 2018

Investigation on enhancing the granularity of environmental data through crowdsensing for precise short-term rainfall prediction.

August 2018

Established the data quality-driven incentivisation algorithm based on game theory.

July 2018

Cong Zhao, Shusen Yang, Ping Yan, Qing Yang, Xinyu Yang, Julie McCann, ‘Data Quality Guarantee for Credible Caching Device Selection in Mobile Crowdsensing Systems’, IEEE Wireless Communications, 25(3): 58-64, July 2018, published.


Researchers and collaborators

Contact info

Dr. Cong Zhao - [email protected]