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In unmanned aerial vehicle Ad hoc network (UANET), increasing the network size will increase the average number of hops between nodes, making network management and routing protocol operation more difficult. Clustering structure can be used to optimize network management and improve network scalability. This paper proposed a fast and stable weighted clustering algorithm to address the instability and poor robustness of cluster structures caused by the high mobility of drones. Compared with existing weighted clustering algorithms, this algorithm improved the selection of four indicators: link retention rate, node degree and relative speed. It proposed an efficient cluster maintenance mechanism to address the cluster oscillation caused by cluster head node disconnection in battlefield and emergency scenarios. Finally, it verified the performance of this algorithm through simulation. The results show that compared with existing improved weighted clustering algorithms, this algorithm can effectively reduce clustering time, and quickly recover in the event of cluster head nodes dropping, making it more suitable for network deployment in complex environments. [ABSTRACT FROM AUTHOR]

The traditional Graph Embedding algorithm and graph neural network model only use the attribute information or feature information of the nodes themselves when classifying the nodes in the network, but rarely use the structural information of the nodes in the network. It is also a problem worthy of further study to introduce the node network structure information to improve the classification accuracy when aggregating the graph neural network. Therefore, based on the GraphSage model, a new aggregation function was designed and a new GraphSage-Degree model was proposed on the node degree and node importance in the network. Firstly, the model obtained the importance of the nodes in the neighbourhood based on the node degree, then aggregated the features of the nodes relied on the importance, so that the important nodes in the network can aggregate as much feature information as possible, and set a hyperparameter D related to the node degree in the GraphSage-Degree, which can be adjusted to achieve the best classification state on different data sets.The GraphSage-Degree was tested on three publicly available datasets, Cora, Citeseer and Pubmed. The average improvement values of macro-F1 compared respectively with other methods are 8.72%, 10.37% and 8.29%, with the maximum improvement value of 38.84% on Pubmed. The average improvement values of micro-F1 are 8.97%, 11.16%, and 6.9%, respectively, with a maximum boost value of 38.39% on Pubmed. [ABSTRACT FROM AUTHOR]

In FANET, the high-speed movement of nodes will result in frequent updates of the network topology, which makes network management more complicated. Clustering can increase network capacity and reuse spatial resources, which is an effective way to optimize network management. In the large-scale and high-speed mobile environment .this paper proposed a multiparameter weighted clustering algorithm. This algorithm introduced the clustering index in the maximum speed similarity algorithm into the weighted clustering algorithm. and improved the link retention, node degree difference and node residual energy to select the network node with largest weight as the cluster head, by considering these four parameters synthetically. The simulation results show that the clustering algorithm can reduce the number of clusters and the switching rate between clusters, which improves the stability of clustering, and can extend the minimum survival time of nodes, which improves the overall endurance of the network. [ABSTRACT FROM AUTHOR]

Energy consumption is the key factor affecting the network lifecycle in wireless sensor networks, so low power consumption is expected for the industry. Several existing network clustering protocols, such as LEACH protocol, can solve and reduce network energy consumption to some extent. Although they can prolong the life cycle of the network to a certain extent, there are yet many incalculable factors in the network environment and actual environment. LEACH protocol ignores the residual energy of the current node and the node distribution situation. In this regard, we present a low power clustering protocol based on fuzzy control (LECPFC) to solve the aforementioned problems. In the process of selecting cluster heads, node energy, node degree, node center, distance and listen density etc. are focused. Network configuration and simulation results show that the proposed protocol can greatly improve the work efficiency of the network. [ABSTRACT FROM AUTHOR]