Research On Energy Efficient Data Gathering Method For Wireless Sensor Networks

Recently,wireless sensor networks(WSNs)have been extensively studied as a key component of the internet of things(Io Ts).The consisting of a vast number of densely deployed and collaborative battery-powered sensors,have been widely used for the various monitoring and measuring purposes in many applications.Due to the bottleneck of battery technology of sensors,the limited network lifetime becomes the main challenge of the WSNs.This dissertation focuses on the energy limitation problem of wireless sensor network.Considering the spatial-temporal correlation of data in the network,the authers jointly apply the many technologies,e.g.,compressed sensing,network coding,prediction model and mobile sink to reduce the redudency of information in the network and to reduce the energy consumption for data gathering.Specifically,the main achievements of this research are as follows:(1)Considering the wireless sensor network with random routing,an improved energy-efficient data communication method based on compressed sensing and network coding is proposed for the WSNs.In the proposed method,the next hop selection principle is employed to determine the next transmitter to further reduce the number of the transmissions.Based on the random graph theory,we derive the expression for total number of communications to verify the correctness and the efficiency of the proposed method.Moreover,the proposed method can be used in the large-scale and dense networks to improve the energy efficiency of the networks.(2)Considering the cluster-based wireless sensor networks,A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper.In the proposed method,dual prediction is used in the inter-cluster transmission to reduce the temporal redundancy,and hybrid compressed sensing is employed in the intra-cluster transmission to reduce the spatial redundancy.Moreover,an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy,which makes the proposed method well suitable for different application environments.In addition,the transmission energy consumption is derived to verify the efficiency of the proposed method.Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes,and the sink can recover measurements with reasonable accuracy by using the proposed method.(3)Considering the wireless sensor network with mobile sink,a data collection method based on critical nodes is proposed for the Unmanned Aircraft System(UAS)-aided WSNs to maximize the network lifetime.In the proposed method,some critical nodes are selected to communicate with the UAS for data collection,and the UAS works as a mobile sink.Moreover,the data value is defined based on the correlation of data to measure the importance of the nodes.In addition,a critical nodes selection algorithm is given based on data value,residual energy and location of nodes to reduce and balance the energy consumption in data collection.Simulation results show that the proposed method can prolong the network lifetime greatly compared with the existing schemes.Compared with the existing schemes,the network lifetime can be greatly extended by using the proposed method.Moreover,the proposed method can perform well in the large-scale and dense WSNs.