A practical implementation of an adaptive Compressive Sensing encryption scheme

In the new era of IoT, hundreds or even thousands of interconnected miniature sensors have made feasible the creation of novel applications spanning in multiple areas like e-health, environmental monitoring, on-farming, etc. Despite the technological advances in this domain, the sensors are still severe constrained devices, in terms of memory and processing. These limitations cannot only compromise applications' performance but can also affect trust and security in the IoT ecosystem. Besides security and trust, energy efficiency is also of paramount importance as sensors are often battery-operated. For energy minimisation and data security purposes, several contributions have mainly focused either on data compression, or data encryption; however, considering those as two independent operations. The last few years, the Compressive Sensing theory has shown that compression and encryption can be used simultaneously, given that data are sparse in some domain. As data sparsity cannot be known in advance, here, we present a practical implementation of a compressive sensing system where the data sparsity is estimated, and the compression rate is selected accordingly. Our system consists of two entities: a server implemented in Java running on a powerful machine, and a client that runs in a miniature sensor, developed in C and executing in the Contiki operating system. The evaluation results show the superiority of the proposed scheme against a non-adaptive one.