Data Aggregation Based On Canonical Correlation Analysis In Wireless Sensor Networks

As an important part of the Internet of Things,wireless sensor networks consists of hundreds of inexpensive and micro sensor nodes which are randomly deployed in the sensing area,and a multi-hop and self-organizing network system is formed by wireless communication.It aims to sense,collect and process the data information in the coverage area of the wireless sensor network and send the results to the user.The energy of those sensor nodes in the sensing area is quite limited and can not be replenished.Therefore,how to reduce the energy consumption of those sensor nodes as much as possible to extend the lifetime of the wireless sensor network is the focus of the current study.The sensor nodes are usually much dense in order to cover the sensing area in all respects.Therefore,there may exist significant redundancy in sensed data due to spatial and temporal correlation among sensor nodes.In fact,data aggregation becomes an effective method to eliminate redundancy,minimize the amount of transmission data and then save energy.In previous works,many people have proposed a number of effective methods to eliminate data redundancy.However,these existing methods have either low energy efficiency or high complexity.In the cluster-based network structure,the energy is mainly consumed in the transmission from the cluster head node to the Sink node.Thus,we mainly consider data aggregation at the cluster head to reduce the amount of transmission data in order to decrease the energy consumption.Based on these considerations,we proposed a data aggregation method based on Canonical Correlation Analysis in deadline-constrained wireless sensor network in this paper.Firstly,in order to balance the energy consumption among clusters,we aim to minimize the total energy consumption in the whole network subject to the deadline constraint,and obtain the optimal number of clusters in the network by Lagrangian dual method.Secondly,to avoid congestion in the transmission process,we use “time-slotted” clustering algorithm to construct an optimal deadline-constrained aggregation tree and ensure that a transmission takes exactly one time slot in the transmission process.Thirdly,at cluster heads,we proposed a data aggregation scheme based on Canonical Correlation Analysis which can handle various multi-dimensional with low complexity to eliminate data redundancy at the cluster heads so as to minimize the amount of transmission data and save energy in the network.The final simulation results show that,compared with the existing methods,our method can not only reduce the amount of transmission data,save energy consumption in the network,but also reduce the delay of the whole network and prolong the lifetime of the Wireless Sensor Network greatly.