Big Sensed Data: Evolution, Challenges, and a Progressive Framework

The rise of sensor deployments, uptake of the Internet of Things (IoT), and new manifestations of sensing systems (e.g., crowd sensing, M2M-driven sensing, cloud sensing) has resulted in a tide of sensed data that is potentially drowning our communication resources and hindering big data analytics with superfluous data. We argue that efficient management of IoT systems in smart communities and cities lies not in sensing systems alone, but in the expedited funneling and processing of data as we attempt to prune the unnecessary and build on the valuable. The quest for energy efficiency that dominated sensor networks for so long is now matched with a more pressing demand for access ubiquity and real-time operation. We highlight how big data became a challenge in sensing systems, then elaborate on the status quo in managing this challenge under different research umbrellas. We draw upon three planes that encompass current and future developments for the management of big sensed data (BSD), namely resources, data, and information planes, detailing their pertinent challenges and how evolving solutions can streamline their contributions in light of others. We conclude by highlighting core challenges rising across these three planes, and potential solutions to addressing synergy in coping and scaling with BSD.