The ubiquitous nature of sensor-equipped smartphone devices, smart home assistants, vehicles with communication and sensing capabilities, ultrapersonal devices, such as wearables, and a growing number of IoT devices has given rise to previously unthinkable possibilities in terms of data collection. Virtually every device in our lives, including the most inconspicuous and most limited ones, has the ability to sense data from their environment, transform it using on-board computing capabilities, and transmit it through Internet connectivity. The large availability of big data cloud computing environments has also given us the ability to collect and analyze huge-scale quantities of data. These capabilities carry with them the tremendous potential of crowdsensing and collective intelligence through user participation, but also several implications and open challenges, such as privacy protection, data obfuscation and anonymization, data quality and ground-truth discovery with unreliable sensors, incentive and reward mechanisms to drive user participation, and trust and reputation of users and devices. Recently, forms of mobile crowdsensing have been proposed as effective instruments for contagion tracing and containment for the COVID-19 pandemic. These applications, however, raise urgent questions of technical and ethical nature, such as how to reconcile user privacy and public interest.
This Special Issue has the goal of publishing the results of recent research studies related to crowdsensing and its varied applications, with a focus on privacy, trust, and incentive mechanisms.
Keywords
mobile crowdsensing
crowdsourcing
data quality
big data
data privacy and anonymity
trust
reward and incentive systems
citizen science
contact tracing
Dr. Alessandro Bogliolo
Dr. Lorenz Cuno Klopfenstein
Guest Editors
