Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time.

Fuzzy event-triggered tracking control for nonlinear unreliable networked systems.

This article addresses a fuzzy event-triggered tracking control problem of unreliable networked systems. An event-based tracking controller is put forward to control the plant in fuzzy form. The looped Lyapunov-Krasovskii functional method is made use of conducting stability analysis, and sufficient conditions on the controller are determined. As a result, the tracking control performance is achieved for the controlled system. The feasibility of the presented tracking control design is verified by some examples.

Anisotropic Vector Hysteresis Simulation of Soft Magnetic Composite Materials Based on a Hybrid Algorithm of PSO-Powell.

To simulate the anisotropic hysteresis characteristics of soft magnetic composite (SMC) materials accurately, an improved vector hysteresis model was proposed and utilized to adjust the shape of hysteresis curves by introducing two parameters. These two parameters are correlated with the amplitude of the vector Everett function and the projection of magnetic flux density along different directions. An experimental platform was built to measure the two-dimensional (2-D) magnetic properties of the SMC material under rotational magnetizations. The scalar and vector Everett functions were constructed by the measured limiting hysteresis loops. A hybrid optimization strategy based on the particle swarm optimization (PSO) and Powell technique was proposed to identify the parameters of the improved model efficiently and precisely, which significantly improved the local optimization ability of the PSO algorithm. The simulated results strongly agree with the measured ones, and thus the effectiveness of the improved vector model and the parameter identification method proposed in this paper was verified.

Magnetostrictive Properties of the Grain-Oriented Silicon Steel Sheet under DC-Biased and Multisinusoidal Magnetizations.

As an intrinsic property, elasticity of soft material is affected significantly by the externally applied alternating magnetic field. Magnetostrictive properties of the grain-oriented (GO) silicon steel under DC-biased and multisinusoidal magnetizations are measured by using a laser-based measuring system. Magnetostriction curves of the GO silicon steel sheet under different magnetizations are obtained and the influence of frequency and DC bias on the magnetostrictive property is observed and analyzed based on the measured data. In addition, the spectrum of magnetostriction under harmonic magnetization is obtained, and the acoustic noise level of the GO silicon steel sheet represented by the A-weighted decibel value caused by magnetostriction is measured under DC-biased and multisinusoidal magnetizations. The measurement results are applied to the simulation of the three-limb laminated core model, and the effects of DC bias and harmonics on magnetic flux density and displacement are analyzed.