Collaborative Internet of Things (C-IoT):
The Internet of Things (IoT) is everywhere. Many sources forecast that, in few years, there will be billions of connected devices and many zettabytes (ZB) of data produced daily. However, often IoT-based ecosystems behave as closed islands, hardly interoperable with data produced and consumed by external sources. The Collaborative IoT (C-IoT) aims to bridge the gap among such isolated systems through data integration and crowd sensing campaigns. This brings along many open issues such as open data integration, analysis, aggregation and classification, incentivization of users to contribute, data inconsistency detection and service composition.
Distributed algorithms for Mobile Crowdsensing (MCS):
In order to gather data about a poorly monitored environment, often many rely on opportunistic sources such as mobile phones. Users joining a Mobile Crowdsensing (MCS) campaign provide their sensor data without being actively involved. For such scenarios, online data analysis and distributed algorithms are a crucial component, since data inconsistency and redundancy have to be avoided, while a sufficient coverage as well as a low effort for the users have to be ensured.
