Securing IoT uplink communications against eavesdropping

We consider a network of Internet of Things devices transmitting to an IoT Gateway (IoT-GW). Such communications can potentially be overheard by one or multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy in order to enhance security against eavesdropping. We propose a communication design where the potential eavesdroppers are deactivated by means of jamming operations performed by 1) an In-Band Full Duplex (IBFD) IoT-GW and/or by 2) cooperative helpers featuring multiple antennas. We show that the solution where only the IBFD IoT-GW generates AN is feasible for small IoT networks and when a neutralization zone around each IoT-device is assumed. In the case with helpers instead, we show that the Average number of Secure Connections (ASC) increases at least exponentially with the density of the helpers

Imitation-based spectrum access policy for CSMA/CA-based cognitive radio networks

International audienc

Imitation-based Spectrum Access Policy for CSMA/CA-based Cognitive Radio Networks

International audienceIn this paper, we tackle the problem of opportunistic spectrum access in cognitive radio networks where a number of unlicensed Secondary Users (SU) operating on the standard CSMA/CA protocol access a number of frequency channels partially occupied by licensed Primary Users (PU). We apply evolutionary game theory to model the spectrum access problem and derive distributed mechanisms to converge to the Nash equilibrium. To this end, we combine a payoff computation methodology, relying on the estimation on the number of SUs on the same channel, with the channel access policy derived by the evolutionary game model. The conducted numerical analysis shows that a fast convergence is achieved and the proposed mechanisms are robust against errors in payoff computation