A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

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.

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

“…Parameter I 2 is the network connectivity rate calculated by formula (23). The network capacity I 3 is the network survival probability, which generally represents the ratio of the current network node survival nodes to the number of all nodes [ 38 ]. The comparison of the network reliability of the four algorithms is shown in Figure 10 .…”

Two FCA-Based Methods for Reducing Energy Consumption of Sensor Nodes in Wireless Sensor Networks

“…In [2], the authors developed two different system models that use optimal node placement strategies compared with traditional equidistant placement strategies to minimize energy consumption in linear wireless sensor networks (LWSNs). Based on improved sparrow search algorithm (ISSA) optimized self-organizing maps (SOM), a cluster head selection strategy used in heterogeneous wireless sensor network (HWSN) is proposed in the literature [7]. is strategy comprehensively considers the residual energy, distance, and the times the node becomes a cluster head.…”

“…With the popularity and rapid development of the elds of Internet of ings, there are many meaningful research directions such as point of interest recommendation [1], reducing the energy consumption of WSNs [2,3], dynamic task o oading [4], and admission control for edge computing [5]. In recent years, WSNs have been more and more widely used in economic life, which are usually made up of a large number of sensor nodes, and are scalable [6,7]. But because of their limited power and computing power, they are often placed in areas that are hard for people to reach [2].…”

Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things

“…As the most flexible and extensible network structure in the wireless communication technologies, WSNs can support potential surveillance and control applications throughout the power network [ 12 – 14 ]. For example, on the generation side of the power system, the applications for WSNs include the monitoring thermal power plants, wind farms, photovoltaic panels, and various types of distributed generation equipment [ 15 , 16 ]. In the power transmission, substation, and distribution side of power systems, the applications of WSNs include the monitoring overhead transmission lines, the underground transmission lines, wire towers, utility poles, and various substations and substation equipment.…”