Massive Collaborative Wireless Sensor Network Structure Based on Cloud Computing

To explore the wireless sensor network (WSN) structure, the cooperative WSN architecture of mass data processing based on cloud computing is studied. The technology of WSN and cloud computing is deeply discussed. The system and node structure of WSN are studied by theoretical analysis method, and the performance of the WSN is studied by using the numerical simulation method. The mass data processing technology based on Map Reduce and its application in WSN are discussed. The numerical simulation method is used to experiment on the architecture of SVC4WSN and MD4LWSN. The relationship between the optimal network number and the node communication radius at different node density is verified. Moreover, the energy and time delay Reduce path is compared with three protocols of LEACH, PEGASIS and PEDAP. The results show that the two Reduce paths have better performance in both network survival time and the total time slot of data acquisition.

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Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring

Sensors ◽

10.3390/s19030691 ◽

2019 ◽

Vol 19 (3) ◽

pp. 691 ◽

Cited By ~ 24

Author(s):

Patricia Arroyo ◽

José Herrero ◽

José Suárez ◽

Jesús Lozano

Keyword(s):

Cloud Computing ◽

Wireless Sensor Network ◽

Air Quality ◽

Volatile Organic Compounds ◽

Data Processing ◽

Sensor Network ◽

Organic Compounds ◽

Low Cost ◽

Wireless Sensor ◽

Volatile Organic

Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

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A Novel Wireless Sensor Network Architecture Based on Cloud Computing and Big Data

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v13i12.7890 ◽

2017 ◽

Vol 13 (12) ◽

pp. 18 ◽

Cited By ~ 2

Author(s):

Changtong Song

Keyword(s):

Big Data ◽

Wireless Sensor Network ◽

Data Processing ◽

Sensor Network ◽

Network Architecture ◽

Wireless Sensor ◽

Big Data Processing ◽

Optimal Network ◽

Proposed Model ◽

The Relationship

To explore big data processing and its application in wireless sensor network (WSN), this paper studies structural construction of the WSN based on big data processing, and numerically simulates SVC4WSN and MDF4LWSN architectures. Moreover, the relationship between the optimal network layer and node communication radius was verified at different node densities. The results indicate that the proposed model achieved better lifecycle and loading balancing effect than the other network.

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Integrating IoT and Cloud Computing for Wireless Sensor Network Applications

Cloud and IoT‐Based Vehicular Ad Hoc Networks ◽

10.1002/9781119761846.ch7 ◽

2021 ◽

pp. 125-143

Author(s):

M. Julie Therese ◽

P. Dharanyadevi ◽

K. Harshithaa

Keyword(s):

Cloud Computing ◽

Wireless Sensor Network ◽

Sensor Network ◽

Wireless Sensor ◽

Network Applications

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An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Soft Computing ◽

10.1007/s00500-020-04694-1 ◽

2020 ◽

Vol 24 (16) ◽

pp. 12541-12552 ◽

Cited By ~ 1

Author(s):

M. Shobana ◽

R. Sabitha ◽

S. Karthik

Keyword(s):

Wireless Sensor Network ◽

Data Processing ◽

Sensor Network ◽

Soft Computing ◽

Data Aggregation ◽

Large Scale ◽

Wireless Sensor ◽

Secure Data ◽

Efficient Data ◽

Secure Data Aggregation

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An Efficient Data Processing Scheme for Wireless Sensor Network Monitoring Using a Machine Learning Model

2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU) ◽

10.23919/icmu.2018.8653586 ◽

2018 ◽

Author(s):

Zhishu Shen ◽

Atsushi Tagami ◽

Teruo Higashino

Keyword(s):

Machine Learning ◽

Wireless Sensor Network ◽

Data Processing ◽

Sensor Network ◽

Network Monitoring ◽

Learning Model ◽

Wireless Sensor ◽

Processing Scheme ◽

Machine Learning Model ◽

Efficient Data

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Exploration of Wireless Sensor Network Based on Cloud Computing

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i11.9501 ◽

2018 ◽

Vol 14 (11) ◽

pp. 16

Author(s):

Qing Wan ◽

Ying Wang

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Wireless Sensor Network ◽

Sensor Network ◽

Energy State ◽

Wireless Sensor ◽

Cloud Environment ◽

Application Service ◽

Operation And Maintenance ◽

Starting Point

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.

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