Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Abstract

Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

Fault‐tolerance based on augmenting approach in wireless sensor networks

“…Figures 9 and 10 indicate the value of the objective function (pairwise connectivity) and the cardinality of the largest connectivity component according to the number of removed critical nodes in dense networks. To illustrate the performance of our proposal, we compare GA‐CNP with other important node evaluation methods proposed in the literature: MADME, 51 IE‐matrix, 64 and betweenness 65 . The results obtained demonstrate that GA‐CNP maximizes the pairwise connectivity among the remaining nodes, also minimizes the variance in the cardinalities of the components.…”

“…Centrality 49,50,51 Centralized 37,40,44 Distributed 20,41,42,43 Data transmission 39 Energy 38,51 k-connected graph 14,45,46,47 F I G U R E 2 Classification of critical nodes variants on WSN…”

“…Recently, Yin et al 51 proposed a new evaluation of node importance, called MADME, which combines some measures of WSN performance with the structural parameters of the graph. They considered four indicators: centrality degree, number of spanning trees, delay, and network energy consumption.…”

Power Grid-Oriented Cascading Failure Vulnerability Identifying Method Based on Wireless Sensors

“…Under intentional attacks, important nodes in the network will be attacked. Important nodes are often measured by the node's betweenness centrality, degree [33], k-shell [34] value, etc. Therefore, we study three attacks that sort these three metrics in descending order to attack.…”

Optimal Selection of Lightweight Cipher Algorithm and Topology Construction Protocol in Wireless Sensor Networks

“…A famous approach, the AHP method, was applied in a study aiming to determine the costeffectiveness of the concept of smart city in a context of technological criteria (Escolar et al, 2019) and is constantly being improved (Kou el al., 2017). Using the methods TOPSIS and MADME, researchers determined the importance and impact of a sensor node within the WSN based on four criteria (Yin et al, 2019). Many studies compared the MCDM methods in the context of decision criteria, decision-making approaches, and their field of use (Wu & Zhang, 2011).…”