An adaptive energy aware strategy based on game theory to add privacy in the physical layer for cognitive WSNs

Abstract The expansion of big data and the evolution of Internet of Things (IoT) technologies will play an important role in the feasibility of Smart City initiatives. In this IoT network infrastructure Cognitive Wireless Sensor Networks (WSNs), as a network of nodes that work in a cooperative way to sense the spectrum and control the environment surrounding them, are one of the main technologies. Security and privacy appear as key aspects for the development of new applications and services. In this work we propose a novel artificial noise generation strategy based on game theory in order to improve the security against privacy attacks in CWSNs. Artificial noise generation consists in introducing interferences in the spectrum in order to mask the real information. The decision whether or not to introduce artificial noise is modeled through a light non-cooperative game designed for low-resources networks that balance security enhancement and energy consumption. We show, using several simulations, that even with a cognitive attacker our strategy has reduced the information obtained by the attacker (Secrecy Outage Probability) to a value under 10%. The overhead (energy consumption and spectrum occupancy) of the strategy has also been deeply analyzed. All possible cases our approach provides better results, in the ratio energy consumption - security, than not using the strategy or using a random noise generation strategy. Also, although the saturation of the radio spectrum is strongly affected, the reliability of the network is maintained in 95% of application packages received. Therefore, we can conclude that an improvement of the privacy in the physical layer is obtained, taking into account energy consumption and maintaining levels of spectrum saturation suitable for the application proposes.