scholarly journals Implementation of Protocol Stack for Three-Dimensional Wireless Sensor Network

2016 ◽  
Vol 89 ◽  
pp. 193-202 ◽  
Author(s):  
Abhishek Joshi ◽  
Sarang Dhongdi ◽  
K.R. Anupama ◽  
Pritish Nahar ◽  
Rishabh Sethunathan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Protocol Stack

scholarly journals V2X-Based Mobile Localization in 3D Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Iram Javed ◽  
Xianlun Tang ◽  
Kamran Shaukat ◽  
Muhammed Umer Sarwar ◽  
Talha Mahboob Alam ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Three Dimensional ◽  
Link Quality ◽  
Wireless Sensor ◽  
Localization Error ◽  
Localization Algorithm ◽  
Position Information ◽  
Mobile Anchor ◽  
Anchor Nodes

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.

Application of Wireless Sensor Network in Embedded Smart Home System

2015 ◽  
Vol 738-739 ◽  
pp. 74-78
Author(s):  
Peng Ju Zhang ◽  
Gai Zhi Guo ◽  
Zong Zuo Yu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Smart Home ◽  
Electrical Equipment ◽  
Wireless Sensor ◽  
Protocol Stack ◽  
System Solution ◽  
Control Centre ◽  
Communication Module ◽  
Smart Control

This paper presents an Embedded Smart Home system solution using wireless sensor network (WSN). The Smart Home system can be sectioned into four parts: wireless sensor network, embedded smart control centre, Server and Client. The major technical of the wireless sensor network is ZigBee. The wireless sensor network includes coordinator node and Sensor node. It is developed based on the Z-Stack protocol stack and the wireless chip CC2530. It is mainly responsible for collecting the environmental parameter of the house and controlling the electrical equipment in the house. It can also support the RFID access control and camera monitor. The control centre communicates with the wireless sensor network by the serial port. It communicates with Server by the TCP socket and transmits data to each client, or communicates with the client by using the wireless communication module directly. Partial hardware electric diagram and software flowchart were provided. Field using indicates that this system is economical and flexible.

scholarly journals Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986987 ◽  
Author(s):  
Zhanjun Hao ◽  
Nanjiang Qu ◽  
Xiaochao Dang ◽  
Jiaojiao Hou
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm Optimization Algorithm ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Passive Monitoring ◽  
Link Model

3D coverage is not only closer to the actual application environment, but also a research hotspot of sensor networks in recent years. For this reason, a node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network is proposed in this article. According to wireless link-aware area, the link coverage model in three-dimensional wireless sensor network is constructed, and the cube-based network coverage is used to represent the quality of service of the network. This model takes advantage of the principle that the presence of human beings can change the transmission channel of the link. On this basis, the intruder is detected by the data packets transmitted between the wireless links, and then the coverage area is monitored by monitoring the received signal strength of the wireless signal. Based on this new link awareness model, the problem of optimal coverage deployment of the receiving node is solved, that is, how to deploy the receiving node to achieve the optimal coverage of the monitoring area when the location of the sending node is given. In the process of optimal coverage, the traditional genetic algorithm and particle swarm optimization algorithm are introduced and improved. Based on the genetic algorithm, the particle swarm optimization algorithm which integrates the idea of simulated annealing is regarded as an important operator of the genetic algorithm, which can converge to the optimal solution quickly. The simulation results show that the proposed method can improve the network coverage, converge quickly, and reduce the network energy consumption. In addition, we set up a real experimental environment for coverage verification, and the experimental results verify the feasibility of the proposed method.

Three-Dimensional Wireless Sensor Network Localization Algorithm Based on DV-Hop

2012 ◽  
Vol 155-156 ◽  
pp. 445-449
Author(s):  
Fu Cai Wan ◽  
Yu Ji Shen
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Dimensional Space ◽  
Average Distance ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
External Anchor ◽  
Anchor Nodes ◽  
Improved Algorithm

Node positioning technology in wireless sensor network plays an important role in the whole network, and a lot of scholars engage in this field. According to the background that wireless sensor network is applied in Three-Dimensional space, an improved algorithm is proposed in this paper. The algorithm makes the average distance of each hop more rational through choosing the external anchor nodes. After the achievement of the unknown nodes positioning, initial positioning location would be corrected in order to get a higher positioning accuracy. Simulation results show that the accuracy of the improved algorithm is 13% higher than the traditional DV-Hop algorithm.

scholarly journals An Extraction Method of Volleyball Spiking Trajectory and Teaching Based on Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meifang Wang ◽  
Zhange Liang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Extraction Method ◽  
Teaching Method ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Data Link ◽  
Speed Error ◽  
Spatial Coordinates

It is helpful to analyze volleyball spiking technology and improve spiking quality to extract volleyball spiking trajectory. This article studies the extraction method and teaching method of volleyball spiking trajectory based on a wireless sensor network. The acceleration sensor and gyroscope sensor are used to collect the spiking action state information of volleyball players. The collected information is transmitted to the PC terminal through the wireless sensor network, including physical layer, data link layer, network layer, transmission layer, and application layer, using the LEACH clustering routing protocol algorithm. In the PC terminal, the feedback filtering method is used to preprocess the received information and calculate the integral of each sensor node’s acceleration, connecting the spatial coordinates of each time to obtain the upper limb trajectory in three-dimensional space and realize the trajectory extraction of volleyball spike action. The experimental results show that the position error is less than 0.01 m and the speed error is less than 0.15 m/s. The application of this method in volleyball teaching can effectively improve the quality of volleyball teaching.

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