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In wireless sensor networks, it is an important problem to adjust the work time window in each working/sleeping period to save energy under light network loads and decrease the packet delay under heavy network loads. In this paper, we introduce reinforcement learning method into this problem. We discuss the algorithm design method in a simple IEEE 802.15.4 network, where an RL-based adaptive algorithm is proposed. Simulation results show that this RL-based algorithm can adapt to the change of data flow and make a good tradeoff between the energy-saving performance and the packet delay performance.

In this paper, we discuss the framework of cognitive radio network, analysis motivations for crosslayer design and security in cognitive radio network (CRN) first. Secondly, we proposed a novel architecture in which the dynamic channel access is achieved by a cross-layer design between the PHY and MAC layers for cognitive radio network. Moreover the resolution of cross-layer security problem is proposed and analysis in mathematic. Finally, we discuss the security issues of spectrum sensing for Centralized CRN and a novel centralized dynamic channel access mechanism is proposed, simulation shows it can improve network performance.

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To solve the trajectory tracking control problem for a class of nonlinear systems with time-varying parameter uncertainties and unknown control directions, this paper proposed a neural sliding mode control strategy with prescribed performance against event-triggered disturbance. First, an enhanced finite-time prescribed performance function and a compensation term containing the Hyperbolic Tangent function are introduced to design a non-singular fast terminal sliding mode (NFTSM) surface to eliminate the singularity in the terminal sliding mode control and speed up the convergence in the balanced unit-loop neighborhood. This sliding surface guarantees arbitrarily small overshoot and fast convergence speed even when triggering mistakes. Meanwhile, we utilize the Nussbaum gain function to solve the problem of unknown control directions and unknown time-varying parameters and design a self-recurrent wavelet neural network (SRWNN) to handle the uncertainty terms in the system. In addition, we use a non-periodic relative threshold event-triggered mechanism to design a new trajectory tracking control law so that the conventional time-triggered mechanism has overcome a significant resource consumption problem. Finally, we proved that all the closed-loop signals are eventually uniformly bounded according to the stability analysis theory, and the Zeno phenomenon can be eliminated. The method in this paper has a better tracking effect and faster response and can obtain better control performance with lower control energy than the traditional NFTSM method, which is verified in inverted pendulum and ball and plate system.