Energy-efficient Data Acquisition Algorithms For Heterogeneous Wireless Sensor Networks Based On Spatio-Temporal Association

Because node energy is limited,energy consumption control is one of the key points of wireless sensor networks(WSNs).A large number of existing studies can be summarized as "normal sampling,selective transmission." However,in the big data background,for the sensor nodes with high sampling energy consumption,the sampling frequency must be controlled to save energy.For a single node,due to the continuity of time,the data collected in a certain period of time(WSNs)can be used to monitor the state of the target nodes.The time-dependence can be used to increase the sampling interval and reduce the unnecessary sampling.Therefore,this paper focuses on how to make good use of the spatial correlation and time correlation of WSNs,while maintaining the monitoring accuracy while carrying out the study of sampling energy consumption control in order to achieve the purpose of energy saving.The main idea of this paper is to study the influence of the sampling frequency of non-fixed period on the energy consumption and performance in the fusion monitoring system with stable target state.In order to improve the performance of the sensor network,a new topology control algorithm is proposed.Secondly,based on Hidden Markov Model,communication strategy in a cluster is designed by using the state-score Viterbi algorithm.Then,based on time-series prediction model,the non-uniform sampling frequency control algorithm is studied on a single node.Finally,considering the temporal correlation and spatial correlation of the nodes,the adaptive sampling control algorithm is designed based on spatiotemporal association.The contribution of the paper is summarized as follows:Aiming at the problem of large number of backbone nodes,a new fault-tolerant data collection algorithm based on Branching and Reduce Paradigm is proposed,which can effectively reduce the number of working nodes.In this paper,we propose a new method to construct minimum connected dominating set by Branching and Reduce Paradigm.We also improve the idea of fault-tolerant topology control.We do not need the information of node location information to solve the problem of faulty topology control.In the process of data collection,the common node can generate the fault-tolerant topology,and reduce the number of active nodes.The energy consumption of the network is optimized from the node energy balance point.Aiming at the problem that the energy consumption of the cluster head node is not easy to control,a strategy of node communication based on state-score Viterbi algorithm is proposed to reduce the unnecessary data sampling of the node.The multi-hypothesis test is applied to the nodes in a cluster,and the Hidden Markov Model is used as the classification framework.At each sampling time,the child node makes a classification judgment based on the received signal,and sends the result to the cluster head node.The cluster head node judges the target state in the monitoring area.Based on the Hidden Markov Model,the state information of the target object is modeled,which further reduces the number of hypothetical states,and adjusts the nodes with poor working condition to reduce the amount of transmission information.By improving the working state of each sub-node and improving the computing state of Viterbi maximum likelihood sequence of sub-nodes,the communication strategy of cluster nodes is improved and unnecessary sampling and transmission are reduced.A fast sampling interval control algorithm based on the thought of TCP congestion control is proposed to solve the problem of delay in response to target state change by the existing sampling interval control algorithm.The sampling frequency can adapt to the change of target state quickly and further save the network energy consumption.Considering in a single node,the next sampling time is calculated based on the time series prediction algorithm,and the idea of TCP is used to make the sampling frequency adapt to the target state change quickly.And the exponential weighted average method is used for event detection feedback.Compared with the existing sampling frequency control algorithm,the adaptive sampling algorithm with TCP congestion control idea can adjust the sampling frequency quickly according to the state of the target signal so as to remove the redundant sensing data more efficiently and save the energy.Aiming at the problem of randomness and large node energy consumption in clustering algorithm of existing WSNs,a K-means algorithm and autoregressive model are proposed to collect spatiotemporal correlation data.The initial clustering points in the network region are selected based on the minimum spanning tree algorithm.The nodes in the network are clustered by K-means algorithm.The nodes with the most residual energy in each cluster are taken as the cluster header in the cluster.The data is transmitted to the cluster head node by one hop.All the cluster header form the minimum spanning tree routing network,and through the multi-hop communication of the tree nodes,the data is finally sent to the Sink node.