Research On Energy Prediction And Management In Solar Energy Harvesting Sensor Networks

Battery-powered wireless sensor networks nodes are very sensitive to energy consumption.Once the battery power runs out,the nodes will be unable to work and will be forced to quit the network.Therefore,the sustainable energy supply of nodes is one of the bottlenecks in the development of wireless sensor networks.Energy harvesting technology is one of the most promising technologies to solve the continuous energy supply of wireless sensor network.It collects energy from the surrounding environment,such as solar energy,electromagnetic energy,wind energy,vibration energy,etc.,to provide energy for the nodes of wireless sensor network and extend the life cycle of the nodes.Among the various energy harvesting technologies,solar energy has a high power density,enough to meet the energy demand of low-power sensor nodes.However,solar energy is intermittent and unstable,which is greatly affected by day,weather,season and surrounding environment.Therefore,it is necessary to take appropriate energy prediction and management methods to rationally allocate energy of nodes to provide stable and reliable energy supply for nodes.In this thesis,the prediction and management methods of solar energy have been studied in depth.Aiming at the problem that the forecasting accuracy of solar energy decreases when the weather changes dramatically,a short-term solar energy predction method based on improved Weather Condition Moving Average(WCMA)is proposed.On this basis,a clustering transmission protocol based on energy prediction has been proposed to analyze and manage node energy.The main work is as follows:1.Firstly,the basic structure of the solar energy harvesting wireless sensor network is introduced,the advantages and disadvantages of the classical solar energy prediction algorithm are analyzed,and the basic principle of cluster transmission protocol is expounded.2.In view of the problem that the energy prediction error of the sensor node harvesting solar energy for power supply increases and the prediction accuracy decreases when the weather changes dramatically,a short-term solar energy prediction method based on the improved WCMA is proposed.Firstly,according to the principle of minimum mean absolute error,the most similar weather is selected from the stored energy distribution model,and the energy value of the corresponding time slot in the most similar weather is used to replace the energy value collected in the previous time slot,and the energy prediction model is linearly combined with the incremental part composed of weather condition factors.At the same time,the dynamic weight factor is set to adjust the weight between the energy value of the previous moment and the energy value of the current moment in real time,so that the weight factor can adjust the contribution degree of each component in the prediction model according to the weather change,which makes the prediction accuracy further improved.The experimental results show that,compared with other forecasting methods,the proposed method has smaller prediction error and higher prediction accuracy when the weather changes dramatically in short time.3.The energy harvesting and consumption of nodes in large-scale wireless sensor network are analyzed,the energy collection and consumption model has been established,and the clustering transmission protocol based on energy prediction is to reduce the energy consumption of nodes and increase the network life time.In terms of cluster head selection,cluster heads are selected according to the predicted energy value,so that nodes with more remaining energy and higher predicted energy have a greater probability to be selected as cluster heads,so that nodes with sufficient energy have more opportunities to undertake more data transmission tasks and thus improve the transmission performance of the network.Simulation results verify the effectiveness of the proposed clustering transmission protocol based on energy prediction.