Device-centric Sensing: an Alternative to Data-centric Approaches
|Title||Device-centric Sensing: an Alternative to Data-centric Approaches|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Distefano, S., G. Merlino, and A. Puliafito|
|Journal||Systems Journal, IEEE|
|Keywords||BigData, Cloud, IaaS, Mobiles, sensing abstraction and virtualization, sensors and actuators|
When pieces of information originate from the physical world through sensing infrastructure, there is a pressing need to cope with the overhead and inherent limitations lying in merely shifting huge amounts of aggregated data across the net. In this scenario, a key point is the minimization of wasted bandwidth to accommodate for ever-growing demands of sensing data. For effective treatment of sensing data, BigData principles and approaches should be adopted, in particular the one by which computing has to be brought as near as possible to data. In this paper we propose a new approach to deal with sensing data inspired by this principle, injecting intelligence on the device instead of just using it as source of data, thus reversing the trend from the current data-centric paradigm towards a device-centric one. This way, we shift the focus from the application level onto the infrastructure one, adopting a Cloud-oriented approach to abstract and virtualize sensor-hosting boards ready to be reconfigured with custom logic, such as MapReduce, by providing resources on demand, as a service. Theoretical, design and technical aspects have been addressed in this paper through the evaluation of a device-centric Sensing IaaS stack implementation. In particular, a prototype for mobiles is described, getting into platform-dependent details where needed. The facilities so far implemented under the Android platform have been put under preliminary testing through a mobile application.