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Presently, the wireless sensor network (WSN) plays an important role in smart farming. However, due to the limitation of wireless sensor network resources, the time and space correlation of data acquisition is strong. In order to reduce the number of nodes participating in data compression, the robust and secure data fusion algorithm based on intelligent sensing is proposed. The algorithm can divide the whole network into many clusters. In order to maintain energy balance of nodes in the cluster, the probability of each node in each cluster participating in each round of data collection is computed according to the residual energy of the node. On the sink node, the number of sampling rounds of joint reconstruction of collected data is designated according to the application requirements and reconstruction accuracy requirements, and the number of nodes participating in is further reduced. The simulation results show that the number of nodes participating in the data collection of the proposed scheme in this paper is lower than that of the ordinary intelligent sensing LEACH data acquisition scheme. Meanwhile, the proposed scheme can dramatically extend the network lifetime. This paper provides an insight into various needs of WSN used in agriculture and challenges involved in the deployment of WSN.

The wireless sensor network consists of many low-cost tiny sensor nodes, with the ability to compute, communicate, and acquire data [

Donoho et al. proposed the theory of compressive sensing [

Due to the dense deployment of the sensor nodes, there is a lot of spatial correlation between the sensing data [

The remainder of this article is organized as follows. We review related work in Section

At present, the research on a data collection problem in large-scale wireless sensor networks mainly focuses on three aspects: (1) improving the compression performance of sensing data, so as to reduce the number of the collecting measurements [

Based on the intelligent sensing data collection process, values for almost all nonzero elements in the measurement matrix are data collected by dense random projection scheme. A dense random projection data collection scheme is used in References [

A lot of data collection strategies for sparse random projection are also proposed, such as in Reference [

The design principle of the measurement matrix is to satisfy the RIP (Restricted Isometry Property) condition or low correlation with arbitrary orthogonal representation, whether it is a dense random projection or a sparse random projection. This is also the reason why the measurement matrix cannot be sufficiently sparse, and we can only use the measurement matrix to match the application scene.

References [

After a data collection round, the sink node can use an orthogonal matching pursuit algorithm (OMP) to reconstruct the information of the whole environment monitoring area according to the measurement vector

The intelligent sensing theory is introduced into the data fusion of the meteorological sensor network. The temporal and spatial correlation of the cluster head nodes is intelligent and fused, the data transmission of the cluster head is reduced, and the network lifetime is prolonged by constructing the diagonal Gauss matrix. In this model, each node in the model needs to be involved in the process of data acquisition, and how to reduce the amount of data transmission and reduce the number of nodes participating in data acquisition is a problem that needs to be considered.

However, the CS-LEACH does not give the process of constructing the measurement matrix

So this paper proposes the energy balance data fusion algorithm based on intelligent sensing (EBDFACS), in order to reduce the number of nodes that participates in data compression.

In an enviromental monitoring area with the length L, the width W, as shown in Figure

Wireless sensor network developed on a rectangular area.

It divides the entire monitoring area into many cells of

In temperature monitoring, for example,

It is convenient to express data collected from the network in the form of a vector

The expression

Energy balance data fusion model based on intelligent sensing.

When the nodes are deployed evenly in the network, first of all, the clustering algorithm is used to select the cluster head, and the

After the establishment of the cluster structure, the stable data collection and transmission stage is started. In this stage, we can combine the intelligent sensing random measurement process with the random selection of nodes in the cluster, in which not every node in clusters needs to be involved in the data collection. Each cluster node generates a random number, and compared with the probability threshold, it decides whether this node anticipates the data acquisition process in the sampling round. A weight coefficient is set on every node. The node multiplies the weight coefficients of the node by its own collecting value after collecting the data value and then sends the results to the next node. The next node multiplies the weight coefficients of the node by its own collecting value, and the result includes the value of the parent nodes. The results continue forwarding. In the process of forwarding, the data is processed until transmitted to the cluster head. The cluster head node receives the fusion result and sends the result value to the sink node by the shortest path algorithm. This process corresponds to the random measurement process of intelligent sensing, equivalent to building a measurement matrix

Due to the data sequence existing in temporal correlation, this temporal correlation can be used to reconstruct the sampled data, repeat data acquisition, and transfer operation by

When the nodes in the clusters collected abnormal data, it will destroy the sparse of sampling data of the entire network. The abnormal data are transmitted to the sink node, the reconstruction algorithm on the sink node cannot effectively recover the abnormal data, and the data error between the recovered data and the original collected data is too large; it cannot meet the requirements of the application; at this time, it needs to deal with the abnormal data, and the abnormal value will be discarded or sent to the sink node to make early warning.

It sets an exception threshold

In the data stable transmission stage, the cluster head knows the location information of each member node, and the member nodes send the information to the cluster head node in a certain sequence. Each sensor node has a random number generator to generate random number

In a sampling round, the weight coefficients of each node in the whole network can be abstracted into matrix

Matrix

The fusion result of the cluster head node transmitted to the sink node is

When the collected data compared with the abnormal data threshold

When the sink node receives the data from each cluster all over the network in a sampling round, the data is temporarily stored, and the sink node does not run the intelligent sensing reconstruction algorithm immediately. When performing the data acquisition process in the network, it runs

After the sink node receives the fusion result of the

Formula (

The process of joint reconstruction on the sink node is not a fixed sampling round

The steps of the energy balance data fusion algorithm based on intelligent sensing are as follows:

After deploying meteorological sensor nodes in the monitoring area, the node runs the clustering algorithm in the network to establish

In one sampling round, the nodes in each cluster are generated by a random number generator and

The data is transmitted to the cluster head node of each cluster, and the cluster head node forwards the data

According to different application requirements, the sink node constructs the fusion result of

In this paper, the performance of the energy balance data fusion algorithm based on intelligent sensing is evaluated by using the MATLAB tool. The data is the measurement of ambient environment temperature from EPFL Sensor Scope WSN [

The reconstruction algorithm on the sink node uses the orthogonal matching pursuit algorithm. In the simulation, the data recovery algorithm runs 400 times, the average value of the reconstruction error of all times is the same as that of the data reconstruction error, and the related experimental parameters are shown in Table

Experimental parameters.

Parameter name | Parameter definition | Value (unit) |
---|---|---|

Sparseness | 2 | |

Anomaly threshold | 40 (°C) | |

RF energy coefficient | 50 (nJ/bit) | |

Power amplifier circuit energy coefficient under free-space model | 10 (pJ/bit/m^{2}) | |

Power double fading model amplifying circuit energy factor | 0.0013 (pJ/bit/m^{4}) | |

Distance threshold | ||

Initial energy | 0.5 (J) | |

The total number of nodes | 100 |

In the simulation experiment, the relationship between the reconstruction error of the algorithm and the number of clusters

There is a high spatial correlation for sampling values of the dense sensor node deployment; Figure

The three-dimensional graph of collected data for a sampling round.

First of all, this simulation verifies the validity of the EBDFACS algorithm (data in Figure

The three-dimensional graph of recovered data for a sample round on the sink node.

In the energy balance data fusion algorithm based on compressive sensing, the number of clusters in the network corresponds to the measured value

Reconstruction error changes with cluster number

The number of clusters in the network affects the quality of data reconstruction on the sink node. In each cluster, the number of nodes involved in data collection also affects the quality of reconstruction error. It can be seen from Figures

The relationship between the reconstruction error and the number of nodes involved in data acquisition in each cluster, when the cluster number is 4.

When the number of clusters is relatively large in the network, as shown in Figure

The relationship between the reconstruction error and the number of nodes involved in data acquisition in each cluster, when the cluster number is 10.

From the comparison between Figures

Joint reconstruction of the energy balance data fusion algorithm based on compressive sensing is a kind of flexible joint reconstruction, and the number of sampling rounds can change according to the application requirements. From the above analysis, we can see that EBDFACS is very suitable for smaller numbers of clusters within the network, better suited for a small-scale short sampling period of meteorological sensor networks. Our scheme is also suitable for large-scale networks by adjusting the sample number of rounds. Relative to CS-LEACH, EBDFACS can reach smaller reconstruction error, higher precision for data recovery, and lower demands on clusters within the number of nodes involved in the data collection.

When the network is involved in the data acquisition of the abnormal data, it is necessary to deal with the abnormal data. If the abnormal value is not processed, the original data sequence cannot be recovered. As shown in Figure

Recovery effect for joint reconstruction after processing sparse anomalies.

Figure

Life cycle of the network.

According to the problems in the meteorological sensor network, such as sensor nodes being deployed in a large scale and network bandwidth resource and node energy constraints in the network, for efficient transmission and processing of data acquisition, this paper proposed the energy balance data fusion algorithm based on intelligent sensing. In the process of random selection of nodes participating in the data acquisition process, in order to promote uniform energy consumption of nodes, the node residual energy is introduced to calculate the probability

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no competing interests.

This work was supported by the National Natural Science Foundation of China (Nos. 41975183 and 41875184).