Self-powered And Transmission System Of Wireless Sensor System Based On Tiny Energy Harvesting

With the intensification of the energy crisis, energy harvesting technology is developing rapidly, at the same time, in order to meet the development needs of human society and economy, the type and number of sensors are growing rapidly, so the combined use of energy harvesting technologies and wireless sensor technology to create a "no battery" Wireless sensor network has been the general trend. In this paper, on the base of an in-depth study of a variety of energy conversion technology and wireless sensor network technology, we first introduced the analysis of the research status at home and abroad of the subject, and then describes the principles of solar and thermal energy collection technology, and finally issues the hardware, software design and make some theory research on related application scenarios.This paper designs the hardware circuit for harvesting solar and thermal energy, and designs the programmable power management circuit based on the practical application scenarios. The energy management efficiency of the power management circuit is tested up to 90%. By coding we achieve the dynamic control for the signal transmission frequency of transceiver module, which brings the energy balance to us.This paper also explores the optimal slotted radio channel power allocation strategy under dynamic energy harvesting. We established a mathematical model of the wireless channel throughput based on dynamic power limited conditions, and then put forward an improved water-filling algorithm. The algorithm achieves the research goal by testing.This paper also studied the wireless transmission scheme of video summary under the dynamic energy harvesting. First, we establish a video summary transmission model under the dynamic energy collection and then use optimization strategies to optimize the model, and finally use bubble method to determine the optimal video summary under energy collection. The simulation shows that the algorithm has good convergence, while the computational complexity is also within the controllable range.