With the aim of solving the coverage problem of a wireless sensor network, a node deployment algorithm for the wireless sensor network, one based on a perception model, is designed in this work. The simulation results show that this algorithm can effectively deploy the wireless sensor network node, improve the network’s coverage, reduce the energy consumption of the network node, and help the network to function longer.

After the accounting informatization, the original vouchers, ledgers and statements have been translated into varieties of data to be stored in computers; however, the storage strategies of these data will be affected by the accounting information processing procedure, such as temporary storage, translation storage and back-up of the documents data, the generation and output of ledger data and the interfaces of other data transmission system. We should not only take the influences of relationship standardization principle into consideration, but also the influences of accounting information process design. This paper studies the impact of data storage from accounting information processing procedure and points out the problems during informatization.

Underwater wireless sensor network nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. Underwater wireless sensor network if no wireless sensor node is available in the area due to used up energy or any other reasons, the area which is not detected by any wireless sensor node forms coverage holes. The coverage holes recovery algorithm aiming at the coverage holes in wireless sensor network is designed in this article. The nodes movement is divided into several processes, in each movement process according to the balance distance and location relations move nodes to separate the aggregate nodes and achieve the maximum coverage of the monitoring area. Because of gradually increasing the balance distance between nodes, in each movement process the nodes moving distance is small and reduce the sum of the nodes movement distance. The simulation and experimental results show that this recovery algorithm achieves the goal of the nodes reasonable distribution with improving the network coverage and reducing the nodes movement distance thus extends the lifetime of the network in the initial deployment phase and coverage holes recovery phase.

In diverse application fields, the increasing requisitions of Wireless Sensor Networks (WSNs) have more and more research dedicated to the question of sensor nodes’ deployment in recent years. For deployment of sensor nodes, some key points that should be taken into consideration are the coverage area to be monitored, energy consumed of nodes, connectivity, amount of deployed sensors and lifetime of the WSNs. This paper analyzes the wireless sensor network nodes deployment optimization problem. Wireless sensor nodes deployment determines the nodes’ capability and lifetime. For node deployment in heterogeneous sensor networks based on different probability sensing models of heterogeneous nodes, the author refers to the organic small molecule model and proposes a molecule sensing model of heterogeneous nodes in this paper. DSmT is an extension of the classical theory of evidence, which can combine with any type of trust function of an independent source, mainly concentrating on combined uncertainty, high conflict, and inaccurate source of evidence. Referring to the data fusion model, the changes in the network coverage ratio after using the new sensing model and data fusion algorithm are studied. According to the research results, the nodes deployment scheme of heterogeneous sensor networks based on the organic small molecule model is proposed in this paper. The simulation model is established by MATLAB software. The simulation results show that the effectiveness of the algorithm, the network coverage, and detection efficiency of nodes are improved, the lifetime of the network is prolonged, energy consumption and the number of deployment nodes are reduced, and the scope of perceiving is expanded. As a result, the coverage hole recovery algorithm can improve the detection performance of the network in the initial deployment phase and coverage hole recovery phase.

Network available and accessible is of great importance to the Internet of things (IoT) devices. In this study, a novel machine learning method is presented to predict the occurrence of distributed denial-of-service (DDoS) attacks. Firstly, a structure of edges and vertices within graph theory is created to simultaneously extract traffic data characteristics. Eight characteristics of traffic data are selected as input variables. Secondly, the principal component analysis (PCA) model is adopted to extract DDoS and normal communication features further. Then, DDoSs are detected by fuzzy C-means (FCM) clustering with these features. In the case study, 2000 traffic data in dataset CICIDS-2017 are used to verify the practicability of this method. The results of recall, false positive, true positive, true negative, and false negative are 100.00%, 1.05%, 68.95%, 0.00%, and 30.00%. Compared with other methods, the results demonstrate that the detecting reliability is improved, and the method has a good effect on the detection of DDoS attacks.