Adaptive compressive sensing for energy efficient smart objects in IoT applications

The IoT (Internet-of-Things) concept has been introduced as a strategic innovation aspect that will benefit society in many ways. Environmental monitoring, smart traffic control, pollution monitoring, crime prevention, smart metering, etc., have now been made feasible due to the ongoing research and development towards IoT. Smart Objects (SOs) are among the fundamental blocks of an IoT architecture consisting of a sensing element (sensor) that senses the environment, and other software/hardware entities. SOs are interconnected forming wireless sensor networks (WSNs) that often convey sensitive information in a multi-hop fashion. As SOs are severe resource-constrained devices, energy-efficient mechanisms for data collection and transmission are of paramount importance. A popular technique for achieving energy efficiency is Compressive Sensing (CS). CS's major advantage is that it can sample and compress information in a single step. At the same time, CS offers lightweight data encryption during compression. In this work, we propose an adaptive CS scheme where a central SO with enhanced capabilities utilizes a learning phase, associating different sparsity levels with the reconstruction error. Based on this scheme, this SO provides feedback to the rest of the SOs to adapt their CS parameters in order to serve applications with a diverge range of quality-of-service requirements.