Compression determination method for lowering power consumption of wireless sensor network

Abstract

A compression decision method which is used for reducing energy consumption and used in a wireless sensor network belongs to a wireless sensor network technology, in particular to the technical field of energy consumption optimization design. A compression decision module is set during data processing of a microprocessing unit (MCU) of the wireless sensor; once transmission power of a radio frequency module or precision requirement of a user changes, the compression decision module can obtain time overhead of compressing data of a single byte by an optimum compression ratio prediction model which is preset for executing an LTC compression algorithm and a shortest execution time prediction model, and determines the optimum compression ratio at a breakeven point according to two parameters including a ratio of the time overhead of compressing one byte of data by the MCU to the time overhead of transmitting one byte of data, and a ratio of the power of the MCU and the transmission power so as to decide whether to transmit after compression or directly transmit. The method can effectively avoid unnecessary energy loss up to 10%-30%.

Description

technical field [0001] The technical field of direct application of "a compression decision method for reducing energy consumption in wireless sensor networks" is the optimal design of energy consumption of compression algorithms in wireless sensor networks. Combining the method described in the present invention with the data compression technology generally used in the wireless sensor network, the energy of the whole sensor node can be optimally controlled. Under the premise of meeting application requirements, sensor nodes can save more energy consumption and prolong their survival time. Background technique [0002] Wireless Sensor Networks (WSNs) are widely used in fields such as ecological monitoring, military monitoring, medical care, and smart home due to their high integration, low cost, and self-organization. Its typical operation mode is that in the designated monitoring area, the widely deployed sensor nodes (Sensor Node) route the collected monitoring data to t...

AI Technical Summary

Problems solved by technology

Taking the data compression algorithm as an example, if the redundancy of the data itself is small, that is, the original data is not strongly correlated, it is difficult to greatly reduce the amount of data during the execution of the compression algorithm; if the application requires high data accuracy, such as If the data is required to be lossless, the compression algorithm cannot obtain a satisfactory compression ratio

This results in a small communication energy saving for the compression algorithm

[0009] Based on the above three points, it can be found that in some cases, the data processing in the network may not achieve the expected energy-saving effect, but because the additional computing energy consumption greatly exceeds the saved communication energy consumption, the execution of the compression algorithm will outweigh the benefits.

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