Energy efficient data prediction model for the sensor cloud environment

Das, Kalyan; Das, Satyabrata; Mishra, Ananya; Mohapatra, Aurobindo

doi:10.1109/iciota.2017.8073619

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Xiong et al [30] considered the synchronizer for concurrent updates as a function that takes two original models and two updated models as inputs and generates two new models with synchronized updates, packing any bidirectional transformations into the synchronizer with the help of model differencing methods. Wang et al [31] designed a distributed framework in which each working node can perform locally synchronized model updates and periodically average the resulting model with an adaptive communication strategy that uses infrequent averaging to save communication latency and increase convergence speed, and then increases communication frequency to achieve a lower error rate. In addition, Wang et al [32] proposed a unified framework for collaborative stochastic gradient descent that incorporates existing communication efficient stochastic gradient descent algorithms, such as cycle averaging, elastic averaging, and decentralized stochastic gradient descent, to achieve an optimal balance between reducing communication overhead and achieving fast error convergence with a low error base (Table 2).…”

Section: Dutta Et Al [25]mentioning

confidence: 99%

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

Zhang

et al. 2021

Complex Intell. Syst.

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With the rapid development of information technology construction, increasing specialized data in the field of informatization have become a hot spot for research. Among them, meteorological data, as one of the foundations and core contents of meteorological informatization, is the key production factor of meteorology in the era of digital economy as well as the basis of meteorological services for people and decision-making services. However, the existing centralized cloud computing service model is unable to satisfy the performance demand of low latency, high reliability and high bandwidth for weather data quality control. In addition, strong convective weather is characterized by rapid development, small convective scale and short life cycle, making the complexity of real-time weather data quality control increased to provide timely strong convective weather monitoring services. In order to solve the above problems, this paper proposed the cloud–edge cooperation approach, whose core idea is to effectively combine the advantages of edge computing and cloud computing by taking full advantage of the computing resources distributed at the edge to provide service environment for users to satisfy the real-time demand. The powerful computing and storage resources of the cloud data center are utilized to provide users with massive computing services to fulfill the intensive computing demands.

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Section: Dutta Et Al [25]mentioning

confidence: 99%

Cloud–edge cooperation for meteorological radar big data: a review of data quality control

Zhang

et al. 2021

Complex Intell. Syst.

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“…The results obtained were quite improved compared to the earlier works and was at par with the existing prediction methods. Das et al [11,12] proposed the data prediction based energyefficient sensor cloud models in which the prediction method is used in the cloud system to save energy consumption for the sensors. Hamouda and Msallam [13] proposed the selection of variable sampling intervals for the monitoring of the parameters used in agriculture activities, which is energy efficient using WSNs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

Das¹,

Das²,

Mohapatra³

2020

Karbala International Journal of Modern Science

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An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the combination of prediction and forecasting methods in the cloud system, which results in less communication and more battery life for the sensor. In our model, we used the ANN model initially, which had predicted the temperature for a given day with an accuracy of 92%. The results of ANN, together with the earlier real temperatures, are given as input to the ARIMA forecasting model, which provides an accuracy of 96% for one day in advance. Our simulation shows that the proposed method saves more energy compared to the traditional approach.

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