Research On Energy-saving Routing Technology Of Rechargeable Sensor Network

Wireless sensor network(WSNs)is one of the hot research fields that have attracted much attention in the world.The wide application of WSN provides a good technical equipment and information platform for environmental monitoring,resource protection,military monitoring and other related fields.In the actual environment deployment,WSN has restricted the application effect of the network due to the limited energy of its battery power.Therefore,how to extend the network life of WSNs has always been a key research goal of academic attention.The research around extending the life of the network can be divided into three categories from the technical characteristics: internal energy saving,external energy collection,and wireless charging.As the solar energy collection technology matures,it is gradually applied to the actual deployment of WSN environments.In the face of the supply from non-constant energy,how to make full use of energy collection to extend the life of the network is the focus of energy saving in rechargeable sensor networks.This paper focuses on the application environment of common solar charging in Energy Harvesting Wireless Sensor Networks(EH-WSNs),we have designed a solar energy data collection sensor system based on lithium batteries,and conducted transmission experiments of solar energy data sets in various environments.It provides reliable data support and a flexible and controllable deployment environment for the subsequent research of energy prediction algorithms and the optimization of routing strategies.In this paper,in order to solve the problem of the existing prediction algorithm that the output power error is large when weather changes are fluctuating,and energy collection cannot be accurately predicted.we proposes a Correlation Least Mean Square(C-LMS)prediction model that introduces the correlation factor of weather changes.The algorithm has low complexity with a certain flexibility,which can solve it quickly and effectively improve the accuracy of short-term prediction.Experimental results show that the error rate of the C-LMS prediction algorithm is reduced by about 15%,and the prediction accuracy is significantly improved dealing with weather fluctuation.At the same time,based on the above lightweight prediction algorithm,the impact of the predicted charging and remaining energy on the topology of the sensor network is reconsidered,and the network lifetime after optimization is improved by nearly 31.7%.Secondly,by further using the prediction algorithm of this paper to optimize and apply it to the two clustering routing protocols of LEACH and DEEC,we have realized the optimization of routing performance for rechargeable wireless sensor networks.The experimental results show that the optimized network data throughput is about 3 times the LEACH protocol network throughput.Also for the optimized DEEC network,its data throughput is about 75% higher than the optimized LEACH.Finally,through multiple sets of experimental simulation analysis,it is verified that the routing optimization strategy in this paper can optimize network performance and ensure network stability and reliability in most cases,and it can improve the network life cycle in a variety of network topology scenarios to achieve Stable and efficient optimization goals.