A Framework for Heterogeneous Sensing in Big Sensed Data
Sharief Oteafy · 2016
The rising tide of data from Sensor Networks, Internet of Things devices and novel sensing systems (e.g. smart devices and wearable technology) are introducing a number of opportunities as well as challenges. While the diversity and abundance of sensing resources are providing a wealth of data for Information Services, we are facing growing challenges in coping with the volume, diversity, and inconsistency of data, both in reported values and measures of accuracy, in addition to challenges in interoperability among these systems to truly realize ubiquitous services that can harness information from aggregated data. At a time when real-time access to data is critical to many applications, especially decision-making processes, the status quo in coping with Big Sensed Data (BSD) is faltering. In this paper we build on recent advancements in interoperability, and frameworks for managing IoT, to present a framework for heterogeneous sensing in BSD (HetSense-BSD), which adopts a multi-phase approach in soliciting heterogeneous resources to serve Information services. We introduce a novel Selective Sensor Fusion (S2F) algorithm for pruning superfluous data at the source, to the reduce communication footprint of data with inferior quality, and better utilize access networks for delivering the best possible data from available resources to the services. We present a use case for HetSense-BSD, and elaborate on the design of this framework as a first milestone in harnessing the aggregated potential of ubiquitously available resources in novel sensing systems.