DC backup power supply monitoring in substation based on wireless sensor network

2016 ◽  
Author(s):  
Firdaus ◽  
Dies Okky Aditya ◽  
Wahyudi Budi Pramono
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Supply ◽  
Wireless Sensor

scholarly journals Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

2011 ◽  
pp. 129-135
Author(s):  
Lambodar Jena ◽  
Ramakrushna Swain ◽  
N.K. kamila
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Dimensionality Reduction ◽  
Sensor Network ◽  
Power Supply ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Limited Power ◽  
Limited Power Supply

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each.

scholarly journals Power Supply for a Wireless Sensor Network: Airliner Flight Test Case Study

2014 ◽  
Vol 8 (12) ◽  
Author(s):  
Paul Durand Estebe ◽  
Vincent Boitier ◽  
Marise Bafleur ◽  
Jean-Marie Dilhac ◽  
Sébastien Berhouet
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Supply ◽  
Flight Test ◽  
Wireless Sensor ◽  
Test Case

scholarly journals Enhancement in the Lifetime of Wireless Sensor Network

2019 ◽  
pp. 185\-190
Author(s):  
Bhumika Yadav ◽  
Ram Neresh Sharma
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Power Supply ◽  
Wireless Sensors ◽  
Remote Area ◽  
Wireless Sensor ◽  
Remote Areas ◽  
Technology Changes ◽  
Atmosphere Temperature ◽  
Temperature And Pressure

Technology changes every day and everyone wants to add more comfort in our life but some technologies increase the safety. Wireless Sensors are one of the technologies which increase the safety in our life in some manner. Wireless sensors are used in remote areas where human cannot survive to collect results of atmosphere, temperature and pressure. All the technical devices are work on electricity/battery and in remote area it is impossible to change batteries or provide continues power supply to sensors that is why we want to increase the lifetime of wireless sensor network in this paper. In this paper we are explaining our approach which is developed by us to increase the lifetime of wireless sensor network as well as we are comparing its results to some other technologies which are already present now a days.

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