Method for detecting abnormal events of wireless sensor network in distributed way

Abstract

The invention discloses a method for detecting abnormal events of a wireless sensor network in a distributed way. The method comprises the following steps of: (1) acquiring the initial data of all the sensor nodes, and determining observation number of networks; (2) clustering all the nodes, acquiring the current observation number of each cluster, and constructing an initial vector for each cluster; (3) acquiring the current observation vector of each cluster; (4) acquiring the current reconstructed data vector of each cluster; (5) acquiring the current restored data vector of each cluster; (6) judging whether each node in each cluster has leaking detection or a false detection or not; (7) judging whether the times of weighing base tracing reconstruction of each cluster reach a preset value or not, if so, performing step (10), otherwise, updating weighing matrix parameters of the weighing base tracing reconstruction of each cluster; (8) updating the observation number of each cluster; (9) after updating the random Gauss matrix of each cluster, returning to the step (3); and (10) calculating the total number of elements of which the element value is 1 in the restored data vector of all the clusters.

Description

technical field [0001] The invention relates to a method for distributed detection of abnormal events in a wireless sensor network by means of clustering. Background technique [0002] Compressed Sensing is an emerging signal processing technology in recent years. Its core idea is to combine data sampling and compression. First, the non-adaptive linear projection (measurement value) of the signal is collected, and then according to the corresponding reconstruction The algorithm recovers the signal from the measured values. Compressed sensing has two basic requirements: the sparsity of the signal, and the non-correlation between the observation base (observation matrix) and the transformation base (transformation matrix). For any signal in nature, there is a specific representation space, which makes the signal sparse in this space. It has been proved by relevant theories that a random matrix, that is, a matrix whose elements are random numbers, has a good non-correlation w...

AI Technical Summary

Problems solved by technology

At the same time, with the continuous expansion of wireless network scale, the application of traditional compressed sensing technology and the improvement of routing methods cannot restrain the increase of data transmission loss.

This not only reduces the accuracy of data transmission and increases the noise, but also limits the transmission distance, which is the bottleneck for further expansion of the wireless sensor network scale.

At the same time, the distribution of events in the wireless sensor network is uneven, with suddenness and randomness. Some areas may have relatively dense events, while the probability of events in other areas is relatively low.

Therefore, it is unscientific to monitor the wireless sensor network as a whole. There is a waste of resources and loopholes in monitoring. Police, this will have serious consequences

Images