Mobile crowdsensing, more efficiently

The idea of using technology to collect data for the benefit of an individual has become rather popular over the last few years. Collecting data for collective benefit is a logical extension of this trend. Why stop at personal health monitoring when we can use sensors carried by individuals to monitor things like air quality and thus have the possibility to have an impact on the health of masses?

A range of domains is expected to benefit from the large amount of sensing data that can be provided by devices equipped with sensors, such as, but not only, mobile phones. Mobile crowdsensing, i.e. collecting data at a larger scale, however, is a complex task that comes with larger challenges.

Recent work of five researchers from the Faculty of Electrical Engineering and Computing at the University of Zagreb that resulted in the paper presented at the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing that took place in Miami in October 2014. The paper focuses on three main issues. It recognizes the need for solutions that provide sufficient sensing coverage without consuming too much energy of the devices; optimize the amount of data transmitted in order to reduce network bandwidth consumption; and process large amounts of data efficiently.

They propose a sensor management scheme that can achieve significant energy consumption reductions without compromising quality. Unlike a standard publish/subscribe-based solution, the filtering process of the CUPUS middleware suppresses redundant sensor-readings. Following an analysis of a real data set measuring user distribution in Seoul, South Korea, the authors conclude that, together with a QoS Sensor Management Function, the CUPUS system can indeed present savings ranging from 40% to 80%.

To learn more about the details of how this efficient solution works, read the full paper here.

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