With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this paper, we propose a method of Hierarchical Unsupervised Network Alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an Unsupervised Network Alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic datasets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.
Vehicle driving path planning is an important information service in intelligent transportation systems. As an important basis for path planning optimization, the travel time prediction method has attracted much attention. However, traffic flow has features of high nonlinearity, time-varying, and uncertainty, which makes it hard for prediction method with single feature to meet the accuracy demand of intelligent transportation system in big data environment. In this paper, the historical vehicle Global Positioning System (GPS) information data is used to establish the traffic prediction model. Firstly, the Clustering in QUEst (CLIQUE)-based clustering algorithm V-CLIQUE is proposed to analyze the historical vehicle GPS data. Secondly, an artificial neural network (ANN)-based prediction model is proposed. Finally, the ANN-based weighted shortest path algorithm, A-Dijkstra, is proposed. We used mean absolute percentage error (MAPE) to evaluate the predictive model and compare it with the predicted results of Average and support regression vector (SRV). Experiments show that the improved ANN path planning model we proposed can accurately predict real-time traffic status at the given location. It has less relative error and saves time for users’ travel while saving social resources.
With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.
Mobile computing provides useful, accurate, and timely information to any customer at any time and any place, which greatly changes the traditional way of reading data in the warehouse. The shelf layout of a warehouse center is one of the important factors affecting the efficiency of operation. Modern distribution centers use the mobile computing technology to store warehouse data, and these new layouts can significantly reduce the order turnover time and cost. This paper focuses on order picking at a storage center, a real-time communication system using data read-write devices, removes the limitation of the picking channel being a straight line and performs the curve design of the main channel of V-shaped and fishbone layouts. Using mobile computing technology, we can obtain the order position in real time and store relevant data, gather the order in time, and reduce the operating costs. Afterward, we built a storage area utilization model with the main channel selected as the curve and a stochastic model of return-type picking path with the main channel as the curve and verified the effectiveness of the mathematical model through simulation using the MATLAB software.INDEX TERMS Curved channel, mobile computing, random memory type, return-type picking,V-shaped layout.
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