The First International Workshop on Pervasive Computing Challenges in Trustable Crowdsensing Systems
The rapid advancement of pervasive computing and the ubiquity of mobile devices have paved the way for crowdsensing systems, where individuals, their smart devices, and Internet of Things (IoT) sensors collaboratively contribute data for various applications such as environmental monitoring, urban planning, healthcare, and transportation. However, the design, implementation, and deployment of effective crowdsensing systems pose numerous challenges at various levels: data aggregation and processing, trust and data quality, data protection, incentivization techniques, mobility, application semantics and privacy to name some of the hot facets.
The workshop on Pervasive Computing Challenges in Crowdsensing Systems (PerCrwod) aims to bring together researchers, practitioners, and industry experts, and provide a platform to share their experiences, insights, and research findings related to crowdsensing systems. The goal is to promote a discussion that can lead to identifying the key challenges faced in developing and deploying pervasive computing solutions for crowdsensing, explore innovative approaches and discuss the implications of crowdsensing on various application domains.
We encourage submissions that present novel ideas, theoretical frameworks, empirical studies, and practical solutions in the field of pervasive computing challenges in crowdsensing systems. Authors are expected to provide sufficient evaluation and validation of their proposed approaches and methodologies. We solicit original contributions in topics of interest including, but not limited to, the following:
Architecture and design of crowdsensing systems
Data quality, trust, and privacy in crowdsensing
Energy-efficient sensing and communication techniques
Systems and platforms for crowdsensing
Machine learning and data analytics for crowdsensing data aggregation and classification
Sensing and data fusion techniques in crowdsensing
Incentive mechanisms and participant engagement strategies
Scalability and robustness of crowdsensing systems
Social, economic, and ethical aspects of crowdsensing
Performance evaluation of crowdsensing applications
Crowdsensing testbeds and platforms
Security and privacy-preserving techniques in crowdsensing
Modeling and simulation in pervasive crowdsensing applications