Abstract
In order to effectively reduce the energy consumption, improve the efficiency of data collection in HWSNs, and prolong the lifetime of the overall network, the clustering method is one of the most effective methods in the data collection methods for HWSNs. The data collection strategy of HWSNs based on the clustering method mainly includes three stages: (1) selecting the appropriate cluster head, (2) forming between clusters, and (3) transferring data between clusters. Among them, the selection of the cluster heads in the first stage. The optimal number of cluster heads in the formation of clusters in the second stage is the core and key to the clustering data collection of HWSNs. In the stage of cluster head selection, a data collection strategy for HWSNs based on the clustering method is proposed. Sink establishes an extreme learning machine neural network model. The cluster member nodes select cluster heads based on the remaining energy of the sensor node, the number of the neighbor node, and the distance to the sink. The best cluster head node is selected through the adaptive learning of the online sequence extreme learning machine. Through comprehensive consideration of various factors to complete the clustering process, the gray wolf algorithm is used to optimize the number of clusters, balance the effect of clustering, and improve the efficiency of data collection while reducing energy consumption. An energy efficient and reliable clustering data collection strategy for HWSNs based on the online sequence extreme learning machine and the gray wolf optimization algorithm is proposed in this paper. The simulation results show that the proposed algorithm not only significantly improves the efficiency of the data collection and reduces energy consumption but also comprehensively improves the reliability of the network and prolongs the network's lifetime.
Highlights
To achieve the optimal network performance, this paper defines the optimal network performance, that is, the optimization goal, as the problem of minimizing network energy consumption. rough the obtained optimal node awareness rate and physical link transmission rate, the goal of minimizing network energy consumption can be achieved
Combining gray wolf optimization algorithm and online sequence extreme learning machine, the two methods cooperate with each other to optimize the performance of heterogeneous wireless sensor networks, prolong the network’s lifetime, and effectively balance the communication energy consumption of the sensor nodes and the collection delay of data collection. erefore, the purpose of this paper is to propose an efficient and low-energy data collection and network energy-saving optimization strategy so that the network can efficiently collect sensor node data and reduce network energy consumption
A clustering mathematical model of HWSNs is established based on energy consumption. e monitoring area is a square of length M, and the total number of nodes is N, including K cluster head nodes, and there are N/K-1 cluster member nodes. e energy consumption generated by the data fusion is ECH, and the information is transmitted to sink, and the distance between the cluster head and the base station is dChtoBS. e energy consumption of cluster head nodes mainly includes receiving data sent by the heterogeneous sensor nodes and forwarding data to sink. e energy consumption ENEN of cluster member nodes includes collecting data and forwarding data to the cluster head [21]
Summary
According to the characteristics of the sensor node, the appropriate node is selected as the cluster head, and the surrounding cluster member nodes are chosen to join the corresponding cluster according to the actual situation. e routing node transmits information, and the cluster head node performs data fusion and sends it to the sink. Dutt et al [11] proposed a two-layer channel selection threshold method for the problems of large energy consumption and high system complexity in the process of selecting cluster head nodes. Is method is referred to as the CREEP clustering data collection strategy. The proposed method increases network energy consumption and algorithm processing time. A genetic algorithm-based optimized clustering protocol (GAOC) is proposed, which greatly improves network collection efficiency and reduces network energy consumption, this method increases the processing time of the algorithm and at the same time increases the computational complexity of the algorithm. E above-mentioned methods reduce the energy consumption of the network and prolong the life of the network, but these methods increase the processing time of the algorithm and at the same time increase the computational complexity of the algorithm Al-Kiyumi et al [10] proposed a distributed energy-aware fuzzy logic routing algorithm (DEFL), which simultaneously solves the problems of energy efficiency and energy balance. e above-mentioned methods reduce the energy consumption of the network and prolong the life of the network, but these methods increase the processing time of the algorithm and at the same time increase the computational complexity of the algorithm
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