Deformation detection algorithm of shallow and large-span tunnel support structure based on wireless sensor network

2020 ◽  
Vol 13 (4) ◽  
pp. 219
Author(s):  
Huawei Wu ◽  
Chuan Sun ◽  
Yicheng Li ◽  
Yong Kuang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Support Structure ◽  
Tunnel Support ◽  
Large Span

Fuzzy logic-based jamming detection algorithm for cluster-based wireless sensor network

2018 ◽  
Vol 31 (10) ◽  
pp. e3567 ◽  
Author(s):  
K. P. Vijayakumar ◽  
P. Ganeshkumar ◽  
M. Anandaraj ◽  
K. Selvaraj ◽  
P. Sivakumar
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Jamming Detection

Vehicle Detector Based on the AMR Sensor

2014 ◽  
Vol 543-547 ◽  
pp. 934-937
Author(s):  
Hong He ◽  
Rui Zheng ◽  
Zhi Hong Zhang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Network Technology ◽  
Parking Space ◽  
Accurate Judgment ◽  
Accurate Perception ◽  
Space State

In order to solve the parking problems in cities which has loomed large, a vehicle detector applied digital three-axis AMR sensor HMC5843 for parking is proposed in this paper. The application of wireless sensor network technology and vehicle detection algorithm in this system realizes the accurate perception of the vehicle and the accurate judgment of parking space state.

scholarly journals Data detection technology of wireless sensor network based on non-stationary filtering

2021 ◽  
Vol 2113 (1) ◽  
pp. 012023
Author(s):  
Xinghua Lu ◽  
Jiahao Huang ◽  
Guohua Luo
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Frequency Domain ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Network Data ◽  
Data Detection ◽  
Time Frequency

Abstract By optimizing the data detection performance of distributed wireless sensor networks, the data sensing and collecting ability of wireless sensor networks can be improved. Traditional methods adopt statistical characteristic parameter detection algorithm for distributed wireless sensor network data detection. Distributed wireless sensor network data has strong time-frequency coupling, so it is difficult to realize frequency domain spatial parameter clustering in frequency domain, and the detection performance is not good. A distributed wireless sensor network data detection algorithm based on non-stationary filtering and high-order statistical feature peak retrieval is proposed. The data model of distributed wireless sensor network is constructed under the interference of color noise. The weak vibration signal is subjected to time-frequency analysis and noise separation by non-stationary filtering, and the spectral peak of distributed wireless sensor network data is searched by the fourth-order cumulant slice post-operator to realize the optimal detection of signals. The simulation results show that the algorithm has a high probability of accurate detection, and has a good ability of suppressing noise and noise sidelobe information interference, which improves the probability of accurate detection of distributed wireless sensor network data under low signal-to-noise ratio.

scholarly journals Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7256
Author(s):  
Marcin Lewandowski ◽  
Bartłomiej Płaczek
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Event Detection ◽  
Detection Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Essential Requirement ◽  
Detection Systems ◽  
Transmission Reduction ◽  
Long Lifetime

Wireless sensor networks have found many applications in detecting events such as security threats, natural hazards, or technical malfunctions. An essential requirement for event detection systems is the long lifetime of battery-powered sensor nodes. This paper introduces a new method for prolonging the wireless sensor network’s lifetime by reducing data transmissions between neighboring sensor nodes that cooperate in event detection. The proposed method allows sensor nodes to decide whether they need to exchange sensor readings for correctly detecting events. The sensor node takes into account the detection algorithm and verifies whether its current sensor readings can impact the event detection performed by another node. The data are transmitted only when they are found to be necessary for event detection. The proposed method was implemented in a wireless sensor network to detect the instability of cargo boxes during transportation. Experimental evaluation confirmed that the proposed method significantly extends the network lifetime and ensures the accurate detection of events. It was also shown that the introduced method is more effective in reducing data transmissions than the state-of-the-art event-triggered transmission and dual prediction algorithms.

Improving the Mechanism of Detecting and Measuring Holes in Ad hoc Wireless Sensor Network

2019 ◽  
Author(s):  
Mohamed A Bayoumi ◽  
Tarek M Salem ◽  
Samir M Koriem
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ad Hoc ◽  
Average Energy ◽  
Detection Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Small Individual ◽  
Coverage Hole ◽  
Hole Detection

Abstract Area detection and measuring is one of the most important problems in wireless sensor network because it mainly relates to the continuity and functionality of most routing protocols applied to the region of interest (ROI). Electronics failure, random deployment of nodes, software errors or some phenomena such as fire spreading or water flood could lead to wide death of sensor nodes. The damage on ROI can be controlled by detecting and calculating the area of the holes, resulting from the damaged sensor networks. In this paper, a new mathematical algorithm, wireless sensor hole detection algorithm (WHD), is developed to detect and calculate the holes area in ROI where the sensor nodes are spread randomly. WHD is developed for achieving quality of service in terms of power consumption and average hole detection time. The dynamic behavior of the proposed WHD depends on executing the following steps. Firstly, WHD algorithm divides down the ROI into many cells using the advantage of the grid construction to physically partition the ROI into many small individual cells. Secondly, WHD algorithm works on each cell individually by allocating the nearest three sensor nodes to each of the cell’s coordinates by comparing their positions, WHD connects each cell’s coordinate points with the selected sensor nodes by lines that construct a group of triangles, then WHD calculates the area of upcoming triangles. Repeating the previous step on all the cells, WHD can calculate and locate each hole in the ROI. The performance evaluation depends on the NS-2 simulator as a simulation technique to study and analyze the performance of WHD algorithm. Results show that WHD outperforms, in terms of average energy consumption and average hole discovery time, path density algorithm, novel coverage hole discovery algorithm and distriputed coverage hole Detection.

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