Abstract
In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.
Highlights
Over the years, the technology of heterogeneous wireless sensor networks (HWSNs) has been developed by leaps and bounds
(4) improved sparrow search algorithm- (ISSA-)self-organizing maps (SOM)(ω1i (n), ω2kl, n, N )//Set parameters and variables, ω1i (n), ω2klare the weight, n is the number of training times, N is the number of network training times
Data collection and cluster head selection for heterogeneous wireless sensor networks are very important. e proper selection of cluster head nodes can greatly improve the performance of heterogeneous wireless sensor networks
Summary
The technology of heterogeneous wireless sensor networks (HWSNs) has been developed by leaps and bounds. Erefore, when selecting the cluster head, the remaining energy of the node and its average energy consumption per round are considered at the same time so that the greater the remaining energy and the smaller the average energy consumption per round, more chances the nodes have to become cluster heads It can effectively prolong the life time of the sensor network [11]. A cluster head selection strategy for HWSNs based on the improved sparrow search algorithm (SSA) optimization self-organizing maps (SOM) is proposed in this paper. (1) Characterize the issues of the data collection for HWSNs, and classify the current data collection of HWSNs (2) Propose a data collection method of heterogeneous wireless sensor network based on the improved sparrow search algorithm (ISSA) optimized selforganizing maps (SOM) (ISSA-SOM). (3) Evaluate the performance of the proposed algorithms by comparing them with the data collection methods of the PSO-SOM, GWO-SOM, and SSASOM algorithms
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