Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in the dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, Jamming is considered as one of the most challenging security threat in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.

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One of the difficult duties in chemical industrial units is the determination of the level of liquid for real – time monitoring. Determination of this parameter is useful in process control loop. Hence present study is devoted for this purpose by employing microbend based optical fiber sensor. In this work, in order to continuously monitor liquid level in petroleum and chemical industries, an optical fiber sensor based on microbend effect was designed and implemented. The system is consist of a sensor that is composed of a microbend modulator, sensing fiber, emitting / detecting devices, in addition to liquid container unit, and an electronic circuit that was used to control the liquid level. The results showed that the laser technique is both accurate and immediate.

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