Computer simulation algorithm of gymnastics formation change path based on wireless sensor

Today with the rapid development of the information age, the exchange of science and technology has brought closer the closeness of countries, and our country has also begun to conduct in-depth research on WSN. This research mainly discusses the computer simulation algorithm of gymnastics formation change path based on wireless sensor. In this research, an improved Leader-Follower method is designed. In the research of gymnastics formation transformation of mobile nodes in the wireless sensor network environment, the traditional three types of nodes are divided into four categories according to the different formation responsibilities, namely, coordinator, beacon node, master mobile node (Leader), slave mobile node (Follower). After it accurately locates itself with the help of the information of the beacon node, the information should be sent out in the form of broadcast, and the coordinator sends the information to the host computer through the serial port for tracking display. In order to enable the mobile nodes in the network to keep the current gymnastic formation moving toward the target point after completing the gymnastic formation transformation, this paper uses the l-φ closed-loop control method to modify the gymnastic formation in real time. The method based on the received signal strength is selected to realize the positioning of the beacon node to the mobile node, and combined with the positioning engine in the core processor CC2431 of the mobile node, efficient and low-energy wireless positioning can be realized. Multiple mobile nodes coordinate with each other to control communication between each node in a wireless manner, and sense their own heading angle information through geomagnetic sensors, so as to make judgments and adjustments on the maintenance and transformation of the current gymnastics formation. During the formation change, after analysis, it is concluded that the maximum offset of Follower2 from the ideal path in the process of traveling to the desired position in the triangular queue is + 0.28 m. This research effectively realized the computer simulation of autonomous formation.

An Optimized Path Planning Model for Anchor-Free Localization in Wireless Sensor Networks

In Wireless Sensor Network (WSN), localization process is considered as a major challenge which is intended to maximize with minimized traveling distance of the beacon node. Further, the important issue is to improve coverage area of the anchor-based node and accuracy in calculation of the location of nodes. This paper mainly focuses on an enhanced path planning model using beacon node based upon their location. The proposed model focuses to improve coverage of the network topology by moving in zig-zag path fashion so that it will enhance the reachability of message in almost every possible corner of the deployed area. The proposed model is simulated extensively in a self-simulator with different scenarios and compared with SCAN and anchor-based model. The tested performance of the model is presented along with its analytical model. The simulation result shows that the proposed model gives the better performance as compared to all others existing model in terms of percentage of nodes settled and energy consumption.

Computer Simulation Algorithm of Gymnastics Formation Change Path Based on Wireless Sensor

Today, with the rapid development of information age, the communication of science and technology is getting closer to each other, and our country has begun to conduct in-depth research on WSN. This study mainly discusses the computer simulation algorithm of gymnastics formation transformation path based on wireless sensor. In this study, an improved leader follower method is designed. In the research of gymnastics formation transformation of mobile nodes in wireless sensor network environment, the traditional three types of nodes are divided into four categories according to different formation responsibilities, namely coordinator, beacon node, leader and follower. When it makes accurate positioning with the help of beacon node information, it will send the information in the form of broadcast, and then the coordinator will send the information to the host computer through the serial port for tracking display. In order to make the mobile nodes in the network keep the current gymnastics formation moving towards the target point after completing the gymnastics formation transformation, this paper uses the L - φ closed-loop control method to modify the gymnastics formation in real time. The method based on the received signal strength is used to locate the mobile node. Combined with the positioning engine in the core processor CC2431 of the mobile node, the efficient and low-energy wireless positioning can be realized. Multiple mobile nodes coordinate and control each other, and each node communicates with each other through wireless mode, and senses its own heading angle information through geomagnetic sensor, so as to judge and adjust the maintenance and transformation of the current gymnastics formation. In the process of formation transformation, the analysis shows that the maximum offset of follower2 relative to the ideal path is + 0.28M in the process of marching to the desired position in the triangle queue. This research effectively realizes the computer simulation of autonomous formation.

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

In order to improve the positioning accuracy of underground targets, especially the positioning accuracy of moving targets, an improved weighted Monte Carlo positioning algorithm is proposed. In the sampling initialization stage, the beacon node gradually constructs the sampling area according to the RSSI size and combines the Monte Carlo method to further narrow the range and improve the sampling success rate. In the filtering stage, refer to the sampling area at time and further improve the sample quality at t − 1 after two filterings. In the recollection stage, cooperate with invalid sample sets to reduce the number of recollections and weigh the final samples to improve the positioning accuracy of the nodes to be tested.

Signcryption Based Security for PSO-GD Localization in WSN

A signcryption based security for localization using PSO-GD algorithm in WSN is proposed which combines particle swarm optimization (PSO) and gradient decent (GD) methods for secure localization. Initially, the beacon node signcrypt the message with the keys and broadcasted the information to the surrounding nodes so as to enable receiver to identify the sender. Then the received location information is unsigncrypted by each unknown sensor node and verified. If the unsignsryption is performed successfully, the unknown node accepts the message, otherwise discarded. From the location information gathered, the unknown node estimates its location coordinates through PSOGD localization algorithm.

A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid

Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.