scholarly journals A Formal Approach to the Selection by Minimum Error and Pattern Method for Sensor Data Loss Reduction in Unstable Wireless Sensor Network Communications

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 1092 ◽  
Author(s):  
◽  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Loss Reduction ◽  
Data Loss ◽  
Formal Approach ◽  
Minimum Error ◽  
Network Communications

scholarly journals A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhipeng Gao ◽  
Weijing Cheng ◽  
Xuesong Qiu ◽  
Luoming Meng
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Spatial Correlation ◽  
Two Dimensions ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Data Loss ◽  
Temporal And Spatial ◽  
Data Estimation

In wireless sensor network, data loss is inevitable due to its inherent characteristics. This phenomenon is even serious in some situation which brings a big challenge to the applications of sensor data. However, the traditional data estimation methods can not be directly used in wireless sensor network and existing estimation algorithms fail to provide a satisfactory accuracy or have high complexity. To address this problem,Temporal and Spatial Correlation Algorithm(TSCA) is proposed to estimate missing data as accurately as possible in this paper. Firstly, it saves all the data sensed at the same time as a time series, and the most relevant series are selected as the analysis sample, which improves efficiency and accuracy of the algorithm significantly. Secondly, it estimates missing values from temporal and spatial dimensions. Different weights are assigned to these two dimensions. Thirdly, there are two strategies to deal with severe data loss, which improves the applicability of the algorithm. Simulation results on different sensor datasets verify that the proposed approach outperforms existing solutions in terms of estimation accuracy.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

scholarly journals Design and Simulation of a Wireless Sensor Network Greenhouse-Monitoring System Based on 3G Network Communication

2016 ◽  
Vol 12 (05) ◽  
pp. 48 ◽  
Author(s):  
Y. H. Zhou ◽  
J. G. Duan
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Communication ◽  
Target Area ◽  
Wireless Communication Network ◽  
Environmental Monitoring System

A greenhouse provides a stable and suitable environment for the growth of plants. Temperature and humidity are closely related to plant growth. These factors directly affect the water content of plants and the quality of fruits. To solve the problems in the current monitoring system of greenhouse cultivation, such as complicated wiring, large node power consumption, and so on, this study proposes a wireless sensor network greenhouse-monitoring system based on third-generation network communication for the real-time monitoring of the temperature, humidity, light, and CO<sub>2</sub> concentration in a greenhouse. GS1011M is regarded as the core in developing wireless terminal nodes. PC software is used to build a real-time observation platform. Sensor data are received in real time through a wireless communication network to complete the monitoring of the target area. A simulation research is also conducted. Results show that the power dissipation of the greenhouse environmental monitoring system is low, its data accuracy is high, and its operation is stable.

scholarly journals Quality based drip drag match data collection in wireless sensor network

2017 ◽  
Vol 7 (1.1) ◽  
pp. 426
Author(s):  
V Jayaraj ◽  
S Alonshia
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Loss ◽  
Quality Of Data ◽  
Deterministic Models ◽  
Multiple Data

Although data collection has received much attention by effectively minimizing delay, computational complexity and increasing the total data transmitted, the transience of sensor nodes for multiple data collection of sensed node in wireless sensor network (WSN) renders quality of service a great challenge. To circumvent transience of sensor nodes for multiple data collection, Quality based Drip-Drag-Match Data Collection (QDDM-DC) scheme have been proposed. In Drip-Drag-Match data collection scheme, initially dripping of data is done on the sink by applying Equidistant-based Optimum Communication Path from the sensor nodes which reduces the data loss. Next the drag operation pulls out the required sensed data using Neighbourhood-based model from multiple locations to reduce the delay for storage. Finally, the matching operation, compares the sensed data received by the dragging operation to that of the corresponding sender sensor node (drip stage) and stores the sensed data accurately which in turn improves the throughput and quality of data collection. Simulation is carried for the QDDM-DC scheme with multiple scenarios (size of data, number of sinks, storage capacity) in WSN with both random and deterministic models. Simulation results show that QDDM-DC provides better performance than other data collection schemes, especially with high throughput, ensuring minimum delay and data loss for effective multiple data collection of sensed data in WSN.

A Routing Method Based on Wireless Sensor Network for Smart Meter Reading System

2013 ◽  
Vol 380-384 ◽  
pp. 2930-2933
Author(s):  
Zhong Feng Wang ◽  
Wei Chun Ge ◽  
Ying Nan Wang ◽  
Jian Long Huang ◽  
Li Gang Li
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Collision Probability ◽  
Wireless Sensor ◽  
Engineering Practice ◽  
Smart Meter ◽  
Important Means ◽  
Network Communications ◽  
Reading System ◽  
Advanced Metering

Smart meter reading system is the core of advanced metering infrastructure for Smart Grid. Wireless sensor network is an important means to realize the power information collection. Based on hierarchical strategy, this article proposed a method to establish routing in wireless smart meter reading system. This method provides reliable two-way communication routing, but also greatly reduces the collision probability of network communications. In order to verify the feasibility of the approach, a large number of simulation experiments were carried out. This idea has been applied in actual engineering practice.

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.

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