A photovoltaic power supply with a simple structure and high tracking efficiency is needed in self-powered, wireless sensor networks. First, a maximum power point tracking (MPPT) algorithm, including the load current maximization-perturbation and observation (LCM-P&O) methods, with a fixed step size, is proposed by integrating the traditional load current maximization (LCM) method and perturbation and observation (P&O) method. By sampling the changes of load current and photovoltaic cell input current once the disturbance is applied, the pulse width modulation (PWM) regulation mode, i.e., increasing or reducing, can be determined in the next process. Then, the above algorithm is improved by using the variable step size strategy. By comparing the difference between the absolute value of the observed current value and the theoretical current value at the maximum power point of the photovoltaic cell with the set threshold value, the variable step size for perturbation is determined. MATLAB simulation results show that the LCM-P&O method, with a variable step size, has faster convergence speed and higher tracking accuracy. Finally, the two MPPT algorithms are tested and analyzed under constant voltage source input and indoor fluorescent lamp illumination through an actual circuit, respectively. The experimental results show that the LCM-P&O method with variable step size has a higher tracking efficiency, about 90%–92%, and has higher stability and lower power consumption.

To reduce the influence of received signal strength indication (RSSI) on ranging error, as well as the influence of least squares support vector regression (LSSVR) on localization algorithm, a node three-dimensional localization algorithm based on RSSI and LSSVR parameter optimization is proposed. First, the RSSI average values at 1–25m in four different directions are collected by experiments and the weighted recursive mean optimization method is used to optimize the values of RF factor and propagation factor. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. Finally, the RSSI range values are used as the input of LSSVR localization model, and the LSSVR regression model is used to solve, in this way, the location estimation of unknown WSN nodes is realized. The simulation results show that the average localization error of the algorithm without parameter optimization is 21.82%, and the localization error of the algorithm after parameter optimization is 11.70%, which has higher localization accuracy. At the same time, a node three dimensional localization experiment platform was built to verify the proposed algorithm in the actual environment, and the test results verified the effectiveness and superiority of the proposed algorithm.

Background: The elderly are prone to do some abnormal behaviors, such as tumbling or stepping out of the guardians’ monitoring area. These abnormal behaviors bring enormous hidden dangers to the health of the elderly, which need to be monitored effectively in order to be dealt with in time. Objective: Provide an approach based on Wireless Sensor Network (WSN) and Multi-Layer Perceptron (MLP) to establish the behaviors monitoring system for the elderly. Methods: A behavior monitoring system based on wireless sensor network and neural network is proposed in this paper, according to the behavior characteristics of the elderly. The system collects real-time behavior data of the elderly by wearing a bracelet with acceleration sensors wore on their hands. And then a behavior recognition model of the elderly is established through the MLP and the collected behavior data. The established behavior recognition model is used to classify and identify the five typical behavior characteristics of the elderly, such as walking, sitting, lying, standing and tumbling. At the same time, the location information of the elderly is estimated by the centroid localization technology based on Received Signal Strength Indication (RSSI) ranging. Results: The experiment results show that the designed system can timely acquire the behavior characteristic parameters of the elderly, and it can accurately identify the five typical behaviors with a 100% recognition accuracy rate. And also, it can timely give the warning of the abnormal behaviors of the elderly, such as tumbling or walking out of the active area. Conclusion: The proposed system in this paper can accurately identify the abnormal behaviors of elderly and timely inform the guardians. The proposed monitoring method can effectively reduce the hurt injury elderly, and can improve the work efficiency of guardians. And it has its theoretical and practical value.