Intelligent spacing selection model under energy-saving constraints for the selection of communication nodes in the Internet of Things

Abstract

Current IoT communication node spacing selection process show may potential areas for improvements such as high delay ratio, high total energy consumption ratio, confusion of the optimal communication information band, intelligent spacing node design under the constraints of the energy-saving selection of IoT communication. Based on energy-saving constraints, the link status between nodes is evaluated through link stability and link quality. In order to prevent the generation of serious noisy nodes and frequency hopping data, the interference nodes under the intrusion of the Internet of Things are identified by determining transition amplitude of the noise nodes in the transmission data sequence. Finally, according to the calculation results of the optimal communication node selection, the design of the intelligent spacing selection model for the communication nodes of the Internet of Things is realized. The simulation results show that the established model not only reduces energy consumption of nodes, shortens the average transmission delay of nodes, but also improves anti-interference effect of node spacing selection.

A Survey of Museum Applied Research Based on Mobile Augmented Reality

“…Edge detection Project 3D model onto a 2D image, matching with the features of the corresponding edge, and calculate 3D camera motion between frames based on 2D displacement of the corresponding feature, to realize pose tracking [12] Point of interest tracking Identify the features of target points from the image database, and then save the location and virtual information; extract feature points in the current view, and match them with the features in the database, to estimate the camera pose [13] Template matching rough recognizing the texture information in the camera view, match with the most relevant images in the image database, to estimate the camera pose [14] Optical flow tracking Under the premise of the constant spatial projection intensity, the physical point in video sequence can be tracked through measuring the speed of pixel position change in the path of projecting a 3D object onto a 2D plane, so as to complete pose tracking [15] Depth imaging Generate depth images with the reference pixel value of the distance between the camera view and the object, and integrate the depth images with RGB images for estimating the camera pose [16] Model-free tracking is method can realize tracking without a model or database; the reconstruct 3D structure of the images through tracking the focal length, the rotation matrix, and the translation vector of the camera, and perform triangulation between the corresponding points of each image, to calculate the camera pose [17] (a) (b) 5) and set multiple guides based on beacon positioning, to meet the needs of users for exhibit explanation and floor navigation. Sun et al [24] designed a method of intelligent spacing selection model, which can improve the problems of high delay and high energy consumption in the Internet of ings. is method can better prevent interference in the process of information transmission in the museum Internet of ings.…”

“…Tsai et al [ 23 ] designed MAR navigation with the image recognition and beacon sensing technology ( Figure 5 ) and set multiple guides based on beacon positioning, to meet the needs of users for exhibit explanation and floor navigation. Sun et al [ 24 ] designed a method of intelligent spacing selection model, which can improve the problems of high delay and high energy consumption in the Internet of Things. This method can better prevent interference in the process of information transmission in the museum Internet of Things.…”

Profile of Intelligent Hybrid Information System in Mobile World

“…The first section of this issue includes six papers, which focuses on the novel intelligent information processing methods in mobile Ad hoc networks, such as opportunistic IoT Networks, Industrial Robot and classifications [6][7][8][9][10][11].…”