CASPer 2017 : The 4th IEEE PerCom International Workshop on Crowd Assisted Sensing, Pervasive Systems and Communications
Call For Papers
With smartphones in their pockets, more than 1 billion people now have immediate access to sensing, computation, and connectivity, and this makes it possible to harness the power of the crowd to collect and share data about their surroundings and experiences on a massive scale. Crowdsensing/crowdsourcing is a novel data collection paradigm that leverages this vast mobile sensor network, expanding the scope of research endeavours and allowing civic issues to be addressed, without the need to purchase specialized sensors or install and maintain network infrastructure. Data collected using such applications may come from unexpected yet interesting and valuable sources, and may allow the data to come from previously inaccessible locations and contexts.
This new data collection paradigm introduces several research challenges. Privacy is a primary concern for users who contribute sensitive or personally identifiable information (PII). Incentive mechanisms for participation may be needed to encourage people to volunteer their resources to collect data. Methods are needed for processing large-scale, user-generated data sets into meaningful information, and for assessing and understanding the quality of information to help guide decision-making. Approaches which involve the crowd in such data analysis tasks, with humans serving as a source of semantic information, interpretation, and evaluation of crowdsensing/crowdsourcing data, can also help to build an understanding of the physical, computational, and socio-technical environment.
CASPer 2017 provides a forum for discussion, debate, and collaboration focused on ideas, trends, techniques, and recent advances in crowdsensing and crowdsourcing. We invite original research contributions that advance the state of the art as well as position papers that pose a new direction or present a controversial point of view. Topics of interest include, but are not limited to:
• Algorithms to handle, process, and visualize large-scale crowdsensing/crowdsourcing data sets
• Data integrity, security, privacy, and provenance for crowdsensing/crowdsourcing data
• Trust and reputation systems for crowdsensing/crowdsourcing applications
• Determining and assessing Quality of Information for crowdsensing/crowdsourcing data
• Crowd-assisted (human-in-the-loop) approaches to analyzing crowdsensing/crowdsourcing data
• Context modeling and reasoning in crowdsensing/crowdsourcing applications
• Incentive mechanisms for participation in crowdsensing/crowdsourcing applications
• Supporting crowdsensing/crowdsourcing in heterogeneous networks
• Crowd-assisted pervasive systems and communications
• Novel use of sensors for crowdsensing/crowdsourcing applications
• Energy efficient mechanisms for crowdsensing/crowdsourcing applications
• Programming abstractions and middleware for crowdsensing/crowdsourcing applications
• Novel large-scale and enterprise crowdsensing/crowdsourcing applications
Accepted papers will be published in the IEEE PerCom Workshop Proceedings. Authors will submit through EDAS. Submissions are limited to a maximum length of 6 pages and must be formatted in accordance with the IEEE Computer Society author guidelines. Templates (IEEE LaTeX and Microsoft Word) can be found at http://www.computer.org/web/cs-cps/authors.
Please note: as per IEEE PerCom policy, each accepted paper requires a full PerCom registration (no registration is available for workshops only). It is mandatory that at least one author register and participate to present the paper during the technical sessions of workshops.
Imre Lendak, University of Novi Sad, Serbia
Yu Wang, The University of North Carolina at Charlotte, USA
Salil Kanhere, The University of New South Wales, Australia
Raghu Ganti, IBM - Thomas J. Watson Research Center, USA
Waldir Moreira, COPELABS, University Lusofona, Portugal
Károly Farkas (Chair), Budapest University of Technology and Economics, Hungary
Luke Dickens, University College London, UK
Miguel Labrador, University of South Florida, USA
Emil Lupu, Imperial College London, UK
Jamie Payton, The University of North Carolina at Charlotte, USA
Thomas Silverston, The University of Tokyo, Japan / JFLI CNRS UMI 3527