Call for Papers

[IMPORTANT UPDATE]: Due to the COVID-19 Outbreak, the conference will be virtual. More info in the official SECON Website.

The Internet of Things (IoT) paradigm is growing at a significant pace and several services are now built on the data obtained from connected smart objects. The emerging IoT paradigm with the Big Data paradigm, provides the foundations to extract common knowledge from data made available by humans, institutions, or smart objects for supporting decision making. The paradigms of Crowdsourcing and Mobile Crowdsensing (MCS) have long been used to seek contributions from a crowd of participants who commit to perform certain agreed tasks. With the omnipresence of IoT, underpinned by mobile smart devices, IoT-Crowdsensing (IoT-CS) is gaining increased interest where smart mobile devices and IoT devices undertake the task of collecting data about phenomena of interest.

Although fascinating and potentially disruptive, this paradigm inherently carry a set of technical challenges at various levels, which should be studied by different research communities: battery efficiency, efficient participant recruiting, data aggregation and processing, data quality, incentivization techniques, mobility, application semantics and privacy to name some of the hot facets of IoT-CS.

In line with such objectives, this workshop aims to provide a platform for researchers and practitioners to discuss and share the current and emerging state-of-the-art, challenges and solutions in IoT-CS specifically targeting Smart Cities. We solicit original contributions in topics of interest including, but not limited to, the following:

  • Energy efficiency in MCS services and applications
  • Protocols enhancement for crowdsensed services
  • Social Internet of things
  • Big data semantics
  • Data science for MCS services
  • Environmental Monitoring
  • Opportunistic MCS services
  • Rewarding mechanism for MCS services
  • Fog computing for Collaborative IoT
  • Heterogeneous data aggregation
  • Machine learning techniques for data aggregation
  • Machine learning techniques for data classification
  • MCS testbeds and platforms
  • NLP techniques for crowdsensed services
  • Privacy for crowdsensed data
  • User behavior classification from public data
  • User activity recognition
  • User profiling

Important dates are reported below:

  • March 15th March 29th - Paper Submission Deadline
  • April 20th - Notification of Acceptance
  • May 1st - Camera Ready Submission Deadline