Compression method for sensor network data based on Huffman encoding and random optimization policy

Abstract

The invention discloses a compression method for sensor network data based on Huffman encoding and a random optimization policy. The current data compression methods have inefficiency. The invention aims at the fact that an encoding threshold is a dynamically changing value in the practical application, and by executing the random optimization policy, a base station can judge the values received by nodes every time in real time to determine the optimum transmission threshold, and the energy consumption of the nodes for receiving the base station information and the energy consumption for transmitting raw data or codes can be effectively balanced, thereby adapting to different network sizes and the fluctuation range of different monitoring values. By using the method of the invention, the average energy consumption of the nodes can be effectively reduced, the accuracy of the base station restoring the perception data of the nodes is improved, and an overall indicator formed by data accuracy, the average energy consumption of transmitting unit data nodes and the number of network failure nodes is optimized, thereby prolonging the service life of the network and expanding the overall performance of the network.

Description

technical field [0001] The invention belongs to the technical field of data compression, and relates to a sensor network data compression method based on Huffman coding and a random optimization strategy. Background technique [0002] In a monitoring system based on wireless sensor networks (WSNs), each sensor node collects local information around itself, processes it and transmits it to the sink node, and the sink node aggregates the local data collected by all nodes and sends it to the base station to obtain the region of interest. overall information. In wireless sensor networks, due to the influence of various factors such as node energy, node computing and storage capacity, background noise and wireless communication instability, there are often certain errors in the sensory data information acquired, processed and transmitted by nodes, and There is a certain degree of uncertainty, however in some applications a certain amount of error is usually allowed. That is, un...

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Problems solved by technology

[0003] Aiming at the space and time redundancy in the node perception data, the amount of coding sent to the node by the wireless sensor network data compression algorithm based on Huffman coding is determined by using a fixed preset threshold at the base station. However, in the wireless sensor network In practical applications, the network scales are different, and with the energy consumption of nodes, the number of nodes actually included in the network also changes dynamically. At the same time, the monitoring values ​​obtained by nodes also change dynamically with time, so a fixed threshold method is adopted. It is difficult to meet the needs of practical applications, and also affects the comprehensive monitoring performance of wireless sensor networks

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