scholarly journals Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880225 ◽  
Author(s):  
Zhou-zhou Liu ◽  
Shi-ning Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Data Processing ◽  
Data Acquisition ◽  
Large Scale ◽  
Fog Computing ◽  
Wireless Sensor ◽  
Cloud Data ◽  
Sensor Cloud

The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

scholarly journals Fault Detection based Connected Dominating Set (FDCDS) in Fog Computing

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 599-606
Author(s):  
Nagarjuna Valeti ◽  
V. Ceronmani Sharmila
Keyword(s):  
Wireless Sensor Networks ◽  
Cloud Computing ◽  
Sensor Networks ◽  
Fault Detection ◽  
Cloud Storage ◽  
Dominating Set ◽  
Fog Computing ◽  
Connected Dominating Set ◽  
Wireless Sensor ◽  
Cloud Data

The meaning of cloud computing is providing services by using the internet. From the Cloud Data Centres (CDC) the services are utilized by the cloud users. Presently (Internet of things) IOT playing the key role to improve the performance of the fog computing enabled applications. Migrating the wireless sensor networks with IOT becomes the most powerful and error free application based on the availability of the services, cloud storage, computation and these are transferred efficiently between server and cloud. Health domain is most widely affecting system in cloud computing as well as by using fog computing with IOT. The system causes various failures for providing the service continuously. Enabling the fog computing with the integration of cloud for the medical devices to transmit the patient information to the cloud storage has become the complicated for the IOT sensors continuously. This may cause the data loss and also reduce the performance of the medical device. To improve the continuous services within the cloud server. In this paper, the Fault detection based Connected Dominating Set (FDCDS) which provides the continuous services with the integration of fog computing and IOT devices with wireless sensor networks. Simulation shows the performance of the proposed system.

Sensor Cloud

2018 ◽  
pp. 226-246
Author(s):  
Seyed Amin Hosseini Seno ◽  
Fatemeh Banaie
Keyword(s):  
Wireless Sensor Networks ◽  
Cloud Computing ◽  
Sensor Networks ◽  
Large Scale ◽  
Wireless Sensor ◽  
Sensory Data ◽  
Large Numbers ◽  
Comprehensive Survey ◽  
Sensor Cloud ◽  
Architecture And Design

With the advancement of wireless sensor networks (WSN) and the increasing use of sensors in various industrial, environmental and commercial fields, it is difficult to store and process the volume of generated data on local platforms. Cloud computing provides scalable resources to perform analysis of online as well as offline data streams generated by sensor networks. This can help to overcome the weakness of WSN in combining and analyzing heterogeneous and large numbers of sensory data. This chapter presents a comprehensive survey on state-of-the-art results in the context of cloud –enabled large-scale sensor networks. The chapter also discusses the objectives, architecture and design issues of the generic sensor-cloud platform.

scholarly journals Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2654 ◽  
Author(s):  
Yuan Rao ◽  
Gang Zhao ◽  
Wen Wang ◽  
Jingyao Zhang ◽  
Zhaohui Jiang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Data Acquisition ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Improve Energy Efficiency ◽  
Switching Mechanism ◽  
Acquisition Scheme

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

The Design of Vehicle-Carried Data Acquisition and Monitoring System for Large-Scale Railway Maintenance Equipment Based on Embedded Linux and 3G Technology

2014 ◽  
Vol 644-650 ◽  
pp. 1257-1260
Author(s):  
Jun Fu Yu ◽  
Jia Lan Yang ◽  
Rui Li ◽  
Hai Rui Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Acquisition ◽  
Monitoring System ◽  
Large Scale ◽  
Can Bus ◽  
Embedded Linux ◽  
Wireless Sensor ◽  
Fault Diagnose ◽  
Railway Maintenance

To meet the requirement of the data acquisition for the remote fault diagnose system of the large-scale railway maintenance equipment, this paper proposed a vehicle-carried data acquisition and monitoring system based on embedded Linux and 3G technology. In the proposed system, the hardware design for realizing the data acquisition function is based on CAN bus, and monitoring function is based on wireless sensor networks.

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