Research On Energy Collection Forecasting And Routing Scheduling For Solar Sensor Networks

With the continuous development and maturity of 5G technology and mobile Internet,the Internet of Things will have more room for development.As an important part of the Internet of Things,the Wireless Sensor Networks(WSN)have an increasing number of application scenarios and increasing demands.Because wireless sensor networks are self-organizing and highly fault-tolerant,they are widely used in environmental monitoring and industrial production.However,since wireless sensor networks need to adapt to complex and changeable environments,they have obvious disadvantages such as difficulty in power supply and limited power.Through energy-saving routing protocols and low-power chips,the network life can be further improved.However,these methods do not completely solve the problem of node energy limitation.The use of clean energy such as solar energy to power wireless sensor networks is an effective way to solve the above problems.At the same time,the energy collected by solar nodes is highly volatile due to weather,geographical location,and season.It is of great significance to study strategies such as energy collection prediction and routing scheduling methods for solar sensor networks.In order to overcome the shortcomings of the current technology,this subject will study from two aspects of energy collection prediction and routing scheduling.Main tasks as follows:First of all,this topic aims at the most common application scenarios of data acquisition and aggregation upload in solar sensor networks,and works on the setting of the forecast period in the existing solar charge prediction research.The importance of the parameter of prediction period and the serious impact on the life of the network are illustrated by examples.After that,a set of Dynamic Period Adjustment Strategy(DPAS)is given.The characteristic of the DPAS method is that it does not interfere with the existing process of the optical charging prediction algorithm,and optimizes the network life by adjusting the prediction period.Finally,the strategy of this paper is verified through experiments.Simulation experiments show that the DPAS method can effectively reduce the mortality of the nodes,and then increase the network life.Compared with existing studies that do not dynamically adjust the forecast period,it can increase the network lifetime by 5%-27%.Secondly,this paper proposes a routing strategy based on data aggregation for how to overcome the "famine" in solar sensor networks under dark conditions such as night.This topic analyzes several existing research schemes such as data compression,node and routing scheduling,data traffic aggregation,etc.,and conducts research on the problems of data loss and energy consumption of compression calculations in these studies.Based on the power consumption characteristics of RF sensors,data aggregation is used as a means,and energy consumption is the target.A data aggregation-based routing strategy(DABRS)was proposed.Finally,the effect of DABRS strategy to prolong the life of the network is verified through experiments.