Optimal Deployment of Heterogeneous Nodes to Enhance Network Invulnerability

Wireless sensor networks(WSN)can be used in many fields.In wireless sensor networks,sensor nodes transmit data in multi hop mode.The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network,which greatly affects the invulnerability of the network.Therefore,an optimal deployment of heterogeneous nodes(ODHN)algorithm is proposed to enhance the invulnerability of the wireless sensor networks.The algorithm combines the advantages of DEEC(design of distributed energy efficient clustering)clustering algorithm and BAS(beetle antenna search)optimization algorithm to find the globally optimal deployment locations of heterogeneous nodes.Then,establish a shortcut to communicate with sink nodes through heterogeneous nodes.Besides,considering the practical deployment operation,we set the threshold of the mobile location of heterogeneous nodes,which greatly simplifies the deployment difficulty.Simulation results show that compared with traditional routing protocols,the proposed algorithm can make the network load more evenly,and effectively improve energy-utilization and the fault tolerance of the whole network,which can greatly improve the invulnerability of the wireless sensor networks.

Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network

Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.

Network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

Wireless Sensor Network(WSN)is an important part of the Internet of Things(IoT),which are used for information exchange and communication between smart objects.In practical applications,WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption,etc.In order to overcome these problems,a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network.Also,a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics.Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost,and the invulnerability of the network is effectively enhanced.

Vulnerable Point Identification Using Heterogeneous Interdependent Node Theory for Distribution Systems

In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point identification method based on heterogeneous interdependent(HI)node theory and risk theory is proposed.Compared with the methods based on betweenness theory,the method based on HI nodes theory can deal with the shortcomings of the power flow shortest path,and consider the direct and indirect relationship of nodes.It is more suitable for identifying vulnerable points in a realistic power system.First,according to the analysis of heterogenous interdependent networks,the HI nodes are defined and used to evaluate the utility coupling value of each node.Then an identification indicator,which combines the utility coupling value and the risk indicators,is utilized to evaluate the vulnerability of each node.Results show that the proposed method is a suitable one to find the vulnerable points and better than betweennessbased methods for a distribution network.