Neural dynamic optimization algorithm based on event triggered algorithm and its application

Wireless sensor networks consist of microprocessor controlled sensors that communicate with each other over multi-hop communication networks. In WSN, the energy consumption of sensor networks for communication can be obviously bigger than the energy required to operate computation that would bring us unimaginable benefits if communication and computation between each node can be somehow isolated. In this paper, a neurodynamic optimization approach is proposed based on the event -triggered algorithm for handling standard NUM problem in WSN. We first confirm that the equilibrium point set of the designed neural network model based on event -triggered algorithm corresponds to the optimal solution of the NUM problem. Then, it is proved that the proposed neural network model is stable in the sense of Lyapunov and is convergent to the optimal solution. Finally, a numerical example is provided to illustrate the performance of the proposed neural network.