Ein robustes Datenmonitoring-Verfahren für Sensornetzwerke (A Robust Data Monitoring Method for Sensor Networks)

2005 ◽  
Vol 47 (2) ◽  
Author(s):  
Volker Turau ◽  
Christoph Weyer ◽  
Matthias Witt
Keyword(s):  
Sensor Networks ◽  
Data Monitoring ◽  
Monitoring Method ◽  
Drahtlose Sensornetzwerke

ZusammenfassungEs wird ein neues Datenmonitoring-Verfahren für drahtlose Sensornetzwerke vorgestellt. Das Verfahren kombiniert geografische Hashtabellen mit Aggregation innerhalb des Netzwerks. Anfragen werden in Gebieten abgearbeitet, deren Sensorknoten ihre Werte in Knoten nahe einer festgelegten Lokation innerhalb des Gebietes sammeln, wo sie leicht von außen abgefragt werden können. Das Verfahren ist robust gegenüber Knotenausfällen und -bewegungen und erzeugt sehr geringen Netzwerkverkehr. Dies wird durch verschiedene Simulationen bestätigt.

Data Monitoring and Gathering in Sensor Networks

2011 ◽  
pp. 1113-1130
Author(s):  
Symeon Papavassiliou ◽  
Stella Kafetzoglou ◽  
Jin Zhu
Keyword(s):  
Sensor Networks ◽  
Data Monitoring

scholarly journals Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1533 ◽  
Author(s):  
Di Wang ◽  
Xi Zhang
Keyword(s):  
Sensor Networks ◽  
Missing Data ◽  
Control Chart ◽  
Effective Field ◽  
Field Monitoring ◽  
Multitask Learning ◽  
Sensor Data ◽  
Monitoring Method ◽  
Efficient Detection ◽  
Control Event

Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor data may not be gathered appropriately into a database for some unexpected reasons, such as sensor aging, wireless communication failures, and data reading errors, which leads to a large number of missing data as well as inaccurate or delayed detection, and poses a great challenge for field monitoring in sensor networks. This fact motivates us to develop a multitask-learning based field monitoring method in order to achieve an efficient detection when considerable missing data exist during data acquisition. Specifically, we adopt a log likelihood ratio (LR)-based multivariate cumulative sum (MCUSUM) control chart given spatial correlation among neighboring regions within the monitored field. To deal with the missing data problem, we integrate a multitask learning model into the LR-based MCUSUM control chart in the sensor network. Both simulation and real case studies are conducted to validate our proposed approach and the results show that our approach can achieve an accurate and timely detection for an out-of-control state when a large number of missing data exist in the sensor database. Our model provides an effective field monitoring strategy for engineering applications to accurately and timely detect the products with abnormal quality during production and reduce product losses.

Congestion Control Protocols for UWSNs

2021 ◽  
pp. 85-100
Author(s):  
Tanvika Garg ◽  
Manisha Bharti
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Congestion Control ◽  
Acoustic Communication ◽  
Acoustic Waves ◽  
Wireless Sensor ◽  
Data Monitoring ◽  
Radio Waves ◽  
Underwater Wireless Sensor Networks ◽  
Underwater Environment

UWSN is a grid of many purposes of self-operating nodes with various applications related to various disciplines such as hydrographic surveys, tactical surveillance, disaster prevention, and bathymetry. The process of transmission and reception of messages by propagating sound in an underwater environment is known as acoustic communication. Transmission of acoustic waves is the only method to communicate underwater, as radio waves get attenuated severely and there is severe scattering in optical transmission. Underwater wireless sensor networks (UWSN) have important applications in the exploration of underwater. UWSNs have various applications like in exploration of the sea, collection of data, monitoring of pollution, surveillance of tactics, prevention of disaster, in applications of ministry and surveying of mines.

scholarly journals Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Wei Zhang ◽  
Guozhen Tan ◽  
Nan Ding ◽  
Guangyuan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Traffic Congestion ◽  
Wireless Sensor ◽  
Data Monitoring ◽  
Subcritical State ◽  
Traffic Data ◽  
Light Control ◽  
Traffic Light ◽  
Traffic Light Control

This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on theMobile Centurydataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

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