Key node identification of wireless sensor networks based on cascade failure

Abstract

Wireless sensor networks (WSNs) have become one of the core technologies of the internet of things (IoT) system. They are information generation and acquisition systems used by the IoT to sense and identify the surrounding environment. They are also sensor technology, embedding computing technology, communication technology and important product in the development of Internet technology, which have made the whole society more intelligent and humanized. WSNs are multi-hop self-organizing networks consisting of a large number of micro-sensor nodes deployed in the monitoring area. They can collaboratively sense, collect and process the monitored objects and transmit them to the observers. In this paper, we use the cascade failure method to find the key nodes in the WSNs. First, a complex network cascade failure model based on load redistribution is proposed. Differences from the existing model are as follows: (1) for each node, an overload function is defined; (2) the evolution of the network topology is replaced by node weight evolution. Based on the cascade failure model, a method for evaluating the importance of complex load network nodes is proposed and a new definition of node importance is given. This method helps to discover some potential “critical nodes” in the network. The final experimental analysis verifies the effectiveness and feasibility of the proposed method.

Identifying critical nodes in the power grid via deep learning methods

“…(4) The fourth approach concentrates on network dynamic. For instance, the importance of nodes is assessed based on the extent of network disruption caused by its failure [6][7]. The more significant the disruption, the more critical the nodes are.…”

Analysis of Controllability in Cyber–Physical Power Systems under a Novel Load-Capacity Model

“…Subsequently, many researchers built on this concept and used complex network theory to study the mechanisms of cascading failures in various systems. They proposed cascading failure models such as load capacity models [10,11], epidemic models [12], OPA failure models (ORNL-PSERC-Alaska, OPA), sandpile models [13], and power flow models [14] to explain the causes behind cascading failure phenomena. Regarding the common load capacity models, various researchers have proposed different load redistribution strategies.…”

“…Regarding the common load capacity models, various researchers have proposed different load redistribution strategies. Wang [10], based on the betweenness centrality of each node, defined initial load and overload functions for each node and then proposed an evaluation method for the importance of network nodes based on these measures. Wang [15] proposed the strategy of redistributing the load among the nearest neighbors.…”

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“…With the rapid development of information technology, education is facing new challenges and innovations. In recent years, designers of network learning systems have gradually formed several common course structures for different learning needs [3]. These course structures include: classic personal tutoring courses, student-centered personal tutoring courses, customized online tutoring courses, knowledge-first personal tutoring courses, exploratory personal tutoring courses, and generative personal tutoring courses [4,5].…”