Energy-saving transmission adaptive LMS (Least-Mean Squares) distributed detection method for wireless sensor network

Abstract

The invention discloses an energy-saving transmission adaptive LMS (Least-Mean Squares) distributed detection method for a wireless sensor network. The method comprises the following steps of: transmitting full network measured data into a bridge network and performing distributed LMS weight calculation to inherit the advantages of a full network distributed LMS detection algorithm; calculating a distributed LMS increment update weight by using the full network measured data to keep performance which is equivalent to that of full network distributed LMS detection; performing information diffusion on a bridge node set to converge transmission strategy bridge node estimation towards a desired result in an allowed mean square error range; and performing distributed LMS detection judgment on each bridge node to complete a distributed LMS detection function based on a bridge node diffusion strategy. According to the method, the converging speed of the algorithm is ensured, and unnecessary network communication amount of the conventional full network is avoided, so that node energy consumption is saved, the communication amount and node operation amount are reduced, the network service life is prolonged, network delay is reduced effectively, and high real-time property is achieved.

Description

technical field [0001] The invention belongs to the field of communication technology, and relates to distributed parameter estimation and distributed detection methods, in particular to a wireless sensor network energy-saving propagation self-adaptive LMS distributed detection method, which is used for distributed data detection of wireless sensor networks. On the premise of not reducing the distributed detection performance of the wireless sensor network, it can save network energy consumption, reduce network traffic, and prolong the service life of nodes. Background technique [0002] Wireless sensor network (WSN) integrates sensor technology, embedded computing technology, modern network and wireless communication technology, distributed information processing technology, etc., and can monitor, sense and collect various environments in real time through various integrated micro sensors. or monitoring object information. When wireless sensor networks operate under harsh ...

AI Technical Summary

Problems solved by technology

[0004] 1) Since the distributed LMS detection method based on the propagation strategy performs parameter estimation on each node of the entire network, the collected data and estimated information of all nodes can be utilized to the maximum extent. Jump neighbor nodes for information exchange, so the traffic is large

Especially when the network is huge and the number of nodes is large, the network communication load will be greater. On the one hand, it is easy to cause network communication congestion. On the other hand, when the data communication is not smooth, it will affect the convergence speed of the algorithm. The method of parameter estimation has a large amount of calculation and communication, which shortens the life of the node

[0005] 2) In the distributed LMS detection method based on the propagation strategy, in the weight diffusion link, since each node diffuses the weight estimation value to the neighbor nodes, this process will also bring a lot of pressure to the network when the network is huge and the number of nodes is large. To a huge communication burden, while reducing the service life of the node

However, when the network is huge and the number of nodes is large, due to the communication burden caused by the above two aspects of 1) and 2), the information cannot be transmitted in time, and the algorithm in the distributed LMS detection method based on the propagation strategy cannot converge quickly. Real-time performance cannot be fully reflected

[0007] To sum up, when the network is huge and the number of nodes is large in the information collection link and weight diffusion link of the original algorithm, there will be disadvantages such as network communication blockage, large amount of node calculation, and high energy consumption of nodes, which will further cause problems in the detection and judgment link. Because the information communication is not smooth, the information cannot be transmitted in time or the information is lost, resulting in the shortcomings of the distributed LMS detection algorithm that cannot converge quickly.

[0008] The above defects limit the performance improvement of wireless sensor networks, resulting in increased energy consumption, shortened life cycle and increased network delay, thus affecting the application performance of distributed LMS detection methods based on propagation strategies in wireless sensor networks

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