Research On Energy Saving Algorithm In Wireless Sensor Networks

It is an impossible task to replace or recharge the big amounts of batteries within large-scale Wireless Sensor Networks(WSNs)while routine deploying.Therefore,to reduce the energy consumption of the network and prolong the network lifetime are key factors in WSNs design.In the existing networks' performance optimization algorithms,the problems that balancing the energy consumption of the overall network still do not have a satisfactory answer.Data fusion in WSNs can effectively reduce the amount of data transmission and the energy consumption of sensor nodes,but most of the data fusion algorithm in WSNs have deficiencies in the accuracy,applicability or stability.We will study these problems in this thesis.The main work is as follows:Firstly,we propose a novel energy aware hierarchical cluster-based routing algorithm for WSNs.The proposed algorithm works in rounds and each of these rounds are divided into two phases:cluster setup phase and steady state phase.In the setup phase,clusters are organized and nodes with low remaining energy are picked from each cluster,these nodes will fall asleep till next round.Cluster heads are changed in each round depending on the residual energy of nodes.Data transmission occurs in the steady state phase.The cluster head node will choose its next hop node according to the remaining battery power of its neighbor head nodes as well as the optimum fairness among them.We model the choice of the relay node as a nonlinear programming problem and use the property of convex function to find the optimal solution.Simulation results show that this algorithm could not only balances the energy consumption of the overall network,delays the death time of the nodes,but also provides more reliable data delivery.Secondly,we propose a data fusion algorithm based on Radial Basis Function(RBF)neural network for WSNs.The proposed algorithm aims at reducing the number of data packet transmitted by the cluster head and the energy consumption of the head node.According to such implementation,the conflict probability of the data packet can be decreased and the network lifetime can be prolonged.The proposed algorithm includes the following aspects:first,the cluster based wireless sensor network is modeled according to the RBF neuron model and neural network structure to find out the input layer,hidden layer and output layer of the neural network.Then the learning method and training process of the neural network are proposed to determine the basic function and the output layer weight value.Simulation results show that the data fusion algorithm based on RBF neural network can effectively improve the accuracy of the collected data,reduce the energy consumption and prolong the lifetime of the network.