On the performance of overlaid wireless energy harvesting cognitive industrial sensor networks under jamming attacks

Two or more wireless sensor networks coexist in the same space while low energy consuming devices mobiles in a secondary network harvest ambient RF energy from transmissions by nearby active transmitters in the primary network. The channels are allocated to the primary network, while the overlaid secondary network can access the idle channel allocated to perform data transmission opportunistically and operate properly. In this paper, with the jammer implanted, we propose a novel solution in which we execute a deception strategy to exhaust the energy of the jammers. As a result, the energy constraint jammers will be challenging to achieve jamming attacks when the secondary transmitters (STs) transmit information. We formulate the problem first to tackle the issue; that is, we regard throughput optimization issues for ST under jamming attacks as a Markov decision process (MDP). Then, since the focus is mainly on the throughput of the secondary network, a learning algorithm is adopted to maximize it. Through the learning process, the STs can adapt to the dynamics of the primary network while executing proper actions to benefit the overall throughput online. Simulations validate the efficiency and the convergence of the algorithm we proposed.