Research Topics

Please get in contact with us if you would like to collaborate, share thoughts, or have questions.

These are our (main) research topics:

Empowering crowds with mobile sensing devices

Mobile crowdsensing is a recent and emerging paradigm which leverages the sensing data from the mobile devices of a huge number of people (the crowd) to serve various goals. These include business goals as for example designing and evaluating a health care product. But mobile crowdsensing systems may also be used to improve public and individual services. Concrete examples include weather monitoring in rural areas of East Africa as well as participatory citizen sensing systems for sharing information on water conditions and flooding in Vicenza, Italy and Doncaster, UK.

In current applications of mobile crowdsensing the view point of crowd participants is frequently neglected or extremely simplified. We have conducted several experiments suggesting that this neglect may yield to enormous efficiency loss in terms of costs and time. Crowdsensing can be viewed as a market. Unlike old-known markets where goods are supplied by companies, here the goods are the cumulative product of a large amount of people. The participants in crowdsensing usually operate no specialized business, but qualify due to circumstances that they own desired resources. They obtain benefits for their participation in the form of idealistic, monetary, or other personal rewards. We view the crowd from a perspective, where contributors are aware of their value. We aim to distribute crowdsensing tasks to balance and minimize loads imposed on the participants.
Empowering crowds with mobile sensing devices

Balancing loads in mobile crowdsensing systems

The efficient operation of mobile crowdsensing systems involves the non-trivial issue of distributing work loads among a huge number of heterogeneous participants to solve crowdsensing tasks. These loads should be balanced to optimize short and long-term system performance with respect to costs, quality of results, user satisfaction and further metrics specific to a certain application.

Consider the following example:
We seek to create an up-to-date tempo-spatial noise level map of an area. We are given a tessellation of this area and a group of crowd participants equipped with smart phones is scattered over the area. Using the external microphones of their smart phones, the crowd participants may provide sensed noise levels with location and time stamp. The question we would like to answer is: How can we cover the whole area while minimizing the amount of time a single participant is involved?
Load balancing provides answers to this question. We will adapt and combine knowledge from resource allocation, scheduling, online optimization, big data analytics, parallel computing and game theory to achieve load balance.

A major part of our research and experiments at swissnex San Francisco is to test the performance of load balancing algorithms for mobile crowdsensing systems.