Abstract
Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node. Then, each ordinary node joins the cluster that is nearest to it. The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes. The simulation results demonstrate that when the compression ratio is set to ten, the data transfer volume is reduced by a factor of ten. When compared to clustering and SPT without CS, it is reduced by 75% and 65%, respectively. When compared to SPT with Hybrid CS and Clustering with hybrid CS, it is reduced by 35% and 20%, respectively. Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation. SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62% and 80%, respectively. When compared to SPT with hybrid CS and clustering with hybrid CS, the latter two were reduced by 41% and 19%, respectively.
Highlights
A wireless sensor network (WSN) is usually composed of several wireless sensor nodes deployed in the monitoring area
The sink node in SPT with Hybrid compressive sensing (CS) collects the data of the sensing node through the shortest path tree constructed, and the number of child nodes in the tree is greater than the observed value threshold
The suggested approach outperforms SPT with Hybrid CS in terms of data transfer volume. This is because in the clustering structure, the sensing node only needs to transmit the data to the cluster head node roughly located in the center of the cluster, and the sensing node in SPT with Hybrid CS transfers data to the parent node that is close to the sink node, which will greatly increase the number of transmission hops and increase the amount of data transmission in the network
Summary
A wireless sensor network (WSN) is usually composed of several wireless sensor nodes deployed in the monitoring area. CMC, 2022, vol., no.1 transmits it to the sink node in a wireless multi-hop manner [1]. Wireless sensor nodes are usually deployed in unattended field areas or complex industrial control sites, so it is extremely inconvenient to replace the battery. The computing, storage and energy resources of the wireless sensor node are extremely limited [2,3]. The monitoring data collected by the sensor node is compressed before proceeding and transmission can effectively extend the survival period of wireless sensor networks. Data compression and reconstruction techniques in wireless sensor networks have become one of the core issues in the field of research [4,5]
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