Research On Wireless Link Data Format And Network Topology For Low-energy Internet Of Things

The Internet of Things(Io T)is an important part of the next generation of information technology and a crucial development stage in the information age.The rapid development of information and communication technologies represented by the Io T is greatly affecting the world economic pattern.After nearly a decade of development,the Io T and its industry have reached the world's leading level in related technologies,playing an important role in the transformation from "made in China" to "made in China intelligently" and the deep integration of industrialization and informatization.With the arrival of 5G era,various terminal devices access to the Io T will increase significantly,and the Io T will become the foundation and pillar of data-driven economy.At the same time,Io T has led to the rapid development of sensors.Sensors have played a decisive role in the success of the Io T worldwide.The characteristics of low power consumption,small size,distribution and self-organization of Wireless Sensor Networks(WSNs)bring about a revolution in information perception.However,the sensor node has the characteristic of energy limitation,which affects the life cycle of WSNs.Therefore,how to reduce the communication energy consumption of sensor nodes,decrease the amount of network data transmission,and extend the network life cycle is an important research content in the field of low-energy Io T.Aiming at the wireless link data processing and network architecture in low-energy Io T,the data transmission technologies in WSNs are studied deeply in this paper to decrease the amount of data transferred in the network and reduce energy consumption.The main contributions of this paper are as follows:Firstly,from the perspective of system design,the influence of network clustering and node location perception on network structure is deeply analyzed.In wireless sensor networks,the topology of the network affects the load balance of the sensor nodes and the capacity and life cycle.Clustering structure is an effective topology control method.Based on information interaction between nodes,such as current energy value,link quality,relative position,etc.,different clustering decisions are made according to different network application backgrounds.Among them,location information is widely needed in both static and mobile networks.In view of the importance of location sensing in WSNs,the threshold based TOA estimation algorithm in the energy detection receiver architectureis the best alternative for wireless sensor networks ranging.Considering the threshold detection error,miss error and false alarm error,the closed representation between the sampling signal sequence statistical characteristics and channel parameters is derived.By analyzing the influence of attenuation characteristics of multipath components on the received signal under different channel conditions,an optimal threshold selection scheme with minimum error index is proposed.Simulation results show that the proposed algorithm achieves ideal ranging accuracy under different channels and integration periods,which provides a theoretical basis for realizing high-precision node positioning and network structure optimizing in the low-energy Io T.To solve the problem of premature node death caused by uneven load distribution in wireless sensor networks,a data collection algorithm based on clustering is proposed.The clustering network structure has excellent expansibility and performance in network load balancing,resource allocation and data fusion processing.In the proposed algorithm,the clusters are adjusted following the game theory after the initial clustering based on cellular virtual structure.The numbers of nodes in each cluster is optimized to be as balanced as possible,and the correlation of sampled data is removed to facilitate data collection and forwarding of cluster heads so as to maximize the network life.Simulation results show that the algorithm can effectively balance the number of member nodes of each cluster,reduce the communication burden of cluster heads,and fully extend the network life.Secondly,aiming at the problem of high redundancy of sampled data in clustering wireless sensor networks,a data acquisition algorithm based on sequence correlation is proposed.The cluster head and the cluster member execute their respective compression algorithms to remove the perceived data correlation.The cluster head removes the temporal correlation by grouping the received data,and then sends the grouping information to the cluster members.According to the received grouping information,a piecewise fitting compression model is established,and the adaptive fitting between the key points and the data compression rate is realized by combining with the piecewise key point selection mechanism.Considering the compression ratio,compression error,communication energy consumption and computational energy consumption requirements,an evaluation index based on energy discrimination is proposed to evaluate the performance of the compression algorithm.Simulation results show that the proposed algorithm can effectively reduce the data compression error,the energy consumption and data transmission in the process of data acquisition.Finally,aiming at the problem that data recovery of unreliable links in wireless sensor networks is susceptible to noise,a data reconstruction algorithm based on structured noisematrix completion is proposed.The algorithm proposes the rank-1 matrix completion algorithm and derives the model of the noisy matrix completion.Constructing the block-sparse observation matrix to realize the compression processing of the sampled data by combining the operator splitting technique.The lost data packet index mechanism is established,and the whole network data is recoved by the lost data position information in the observation matrix and the compression strategy.The simulation results show that the proposed algorithm can effectively reduce the network data transmission,balance the distribution of network energy consumption,improve the data transmission efficiency and data reconstruction accuracy on unreliable links.By studying the key technologies such as clustering,data de-redundancy,ranging and localization in wireless sensor networks,this paper improves the data acquisition and processing of wireless transmission link under the Io T environment from multiple perspectives.The study provides meaningful results and valuable references for the subsequent research on low-energy IoT.