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( Kaiyang Bao ),( Xiaoyuan Ma ),( Jianming Wei ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.6
Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.
Jianning Li,Zhujian Li,Yufei Xu,Kaiyang Gu,Wendong Bao,Xiaobin Xu 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.5
This paper investigates the non-fragile state estimation problem for discrete nonlinear Markov jump neural networks(MJNNs) with sensor failures. Due to the limit communication resource, we adopt a kind of eventtriggered mechanism to determine whether the sensor sampling information is sent or not. By selecting suitable Lyapunov functions, a sufficient condition is obtained to guarantee the mean-square exponential stability of the augmented system. Finally, a numerical example is given to show the effectiveness of the proposed method.