Key Technology for Wireless Sensor Data Fusion

2014 ◽  
Vol 701-702 ◽  
pp. 505-509
Author(s):  
Zhi Qiang Kang ◽  
Rui Feng Cheng ◽  
Wei Chu
Keyword(s):  
Data Fusion ◽  
Low Power ◽  
Coal Mine ◽  
Collection Efficiency ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Support Pressure ◽  
Single Chip ◽  
Large Power ◽  
Redundant Data

Using wireless sensors network (WSN) to collect coal mine hydraulic support pressure data , the method to solve the drawbacks of wired sensors . But the lack of WSN transmission performance in the coal mine : the transmission distance is short , large power consumption , short life cycle . Improve working methods for WSN : DIGI low-power sensor nodes ; using low-power single-chip Wake and Sleep MPS430149 at checking time ; core is a data fusion process , in the process of collecting data from all nodes , the use of local computing and storage nodes the ability to process the data , removing redundant data and minimize the amount of data transmitted within the network , improve data collection efficiency , achieve energy saving role in enhancing the network of life . The results show that the method is effective in improving the life and efficiency of WSN .

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

scholarly journals Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xu Xia ◽  
Zhigang Chen ◽  
Wei Wei
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Coal Mine ◽  
Data Aggregation ◽  
Delay Time ◽  
Assessment Model ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Working Face ◽  
State Of The Environment

More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

scholarly journals Low-Power GPS-Disciplined Oscillator Module for Distributed Wireless Sensor Nodes

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 716
Author(s):  
Tyler Boehmer ◽  
Sven Bilén
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Average Power ◽  
Sensor Arrays ◽  
Extended Period ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Operational Environment ◽  
Large Power ◽  
Crystal Oscillators

Many sensor systems, such as distributed wireless sensor arrays, require high-accuracy timing while maintaining low power consumption. Although the capabilities of chip-scale atomic clocks have advanced significantly, their cost continues to be prohibitive for many applications. GPS signals are commonly used to discipline local oscillators in order to inherit the long-term stability of GPS timing; however, commercially available GPS-disciplined oscillators typically use temperature-controlled oscillators and take an extended period of time to reach their stated accuracy, resulting in a large power consumption, usually over a watt. This has subsequently limited their adoption in low-power applications. Modern temperature-compensated crystal oscillators now have stabilities that enable the possibility of duty cycling a GPS receiver and intermittently correcting the oscillator for drift. Based on this principle, a design for a GPS-disciplined oscillator is presented that achieves an accuracy of 5 μs rms in its operational environment, while consuming only 45 mW of average power. The circuit is implemented in a system called geoPebble, which uses a large grid of wireless sensors to perform glacial reflectometry.

scholarly journals BATS: Adaptive Ultra Low Power Sensor Network for Animal Tracking

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3343 ◽  
Author(s):  
Niklas Duda ◽  
Thorsten Nowak ◽  
Markus Hartmann ◽  
Michael Schadhauser ◽  
Björn  Cassens ◽  
...  
Keyword(s):  
Low Power ◽  
Sensor Network ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Software Infrastructure ◽  
Multiple Features ◽  
Ultra Low Power ◽  
Measurement Results ◽  
Node Network

In this paper, the BATS project is presented, which aims to track the behavior of bats via an ultra-low power wireless sensor network. An overview about the whole project and its parts like sensor node design, tracking grid and software infrastructure is given and the evaluation of the project is shown. The BATS project includes a lightweight sensor node that is attached to bats and combines multiple features. Communication among sensor nodes allows tracking of bat encounters. Flight trajectories of individual tagged bats can be recorded at high spatial and temporal resolution by a ground node grid. To increase the communication range, the BATS project implemented a long-range telemetry system to still receive sensor data outside the standard ground node network. The whole system is designed with the common goal of ultra-low energy consumption while still maintaining optimal measurement results. To this end, the system is designed in a flexible way and is able to adapt its functionality according to the current situation. In this way, it uses the energy available on the sensor node as efficient as possible.

Intelligent Water Quality Monitoring System Based on Multi-Sensor Data Fusion Technology

2021 ◽  
Vol 12 (4) ◽  
pp. 43-63
Author(s):  
Qiuxia Liu
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Sensor Data ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Fusion Algorithm ◽  
Sensor Module

The intelligent water quality monitoring system takes the single chip microcomputer STM32F103C8T6 as the control core to collect signals of each sensor module and converts the collected parameters into effective digital signals by using the internal analog-to-digital converter. The data gathered by the acquisition center is sent to the analysis and processing center through the ZigBee module E18. In the analysis and processing center, data is fused and processed by the single chip microcomputer STC12C5A60S2. The data after fusion is sent to the monitoring management center through the GPRS module SIM800C. For improving the monitoring precision of the system, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision.

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