Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search

Zhang, Cong; Liu, Yuanan; Wu, Fan; Fan, Wenhao; Tang, Jielong; Liu, Haosong

doi:10.1109/access.2019.2927239

Cited by 14 publications

(5 citation statements)

References 25 publications

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“…It is to be noted that the IoT sensor data consists of complex time series data, and thus requires efficient data analysis mechanisms. The authors in [73,74] presented a data analytic framework that involved a multi-dimensional feature handling, selection and extraction model. The papers also discussed the dynamic data analytic model for IoT sensor data prediction.…”

Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

221

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

221

View full text Add to dashboard Cite

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“…This model is a famous model that predicts timeseries values. The model is trained and built using a set of historical timeseries values to predict a set of future values [9]. ARIMA aims to find and show the autocorrelations in the previously sensed data.…”

Section: Previous Studiesmentioning

confidence: 99%

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Sawalha

Al‐Naymat

2023

Preprint

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In business and industry, forecasting the future values of a time series is an age-old and widely utilized data analysis strategy. IoT temporal data, used in various applications, is one of the most well-known examples of such a time series. The main problem is to forecast a future window of the data using the provided IoT temporal data. Many forecasting models that deal with such data were proposed, such as Rolling Window, SVR-RBF, ARIMA, etc. However, in the training process, these models use all available data or a previous direct window of data. As a result, the training data may contain irrelevant data patterns to the current situation, lowering the total forecasting accuracy. This paper proposes a new adaptive forecasting model called AHW-SWA by creating two models based on the Exponential Smoothing method. The first model is trained using the previous direct window of data. The second model uses an extracted data window from the far historical data with a pattern most similar to the current situation. Then an adaptive process is used to balance the predicted values from both models to achieve higher accuracy. AHW-SWA model reduces the overall forecasting error by comparing the extracted and the latest data patterns. Our model outperforms well-known models, including Rolling Window, SVR-RBF, and ARIMA.

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“…For each subband of the wavelet, the deconstructed signal is estimated using a Gaussian process, allowing the processing of Gaussian to pick up a much simpler signal. Using difficult time series generated by several types of sensors, 13 built a multi‐dimensional attribute selection model and a prediction model of sensor data reactive. This methodology enhances the accuracy and consistency of IoT sensor data long‐term prediction results when compared with existing data prediction models.…”

Section: Related Workmentioning

confidence: 99%

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

Hussein

Idrees

Couturier

2022

Int J Communication

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Summary In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy.

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Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search

Cited by 14 publications

References 25 publications

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Adaptive Holt Winter Autoregressive Model for IoT Forecasting

Distributed energy‐efficient data reduction approach based on prediction and compression to reduce data transmission in IoT networks

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