Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Wu, Mou; Tan, Liansheng; Xiong, Naixue

doi:10.1016/j.ins.2015.10.004

Cited by 213 publications

(142 citation statements)

References 49 publications

(49 reference statements)

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“…Enabling the sensor node to transmit a reading only when the prediction does not respect the error tolerance predefined the user. The authors in [6] proposed a Hierarchical Least Mean Squares (HLMS) adaptive filter as a prediction model, which is one of the many adaptive filter based approaches [7], [8], [24]. This filter consists of multiple layers of regular Least Mean Square (LMS) filters, each layer takes feedback from the previous layer in the hierarchy aiming to minimize the prediction error of the model.…”

Section: Related Workmentioning

confidence: 99%

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

et al. 2019

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In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the "nonsampled" parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle nonstationary data more effectively.

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Section: Related Workmentioning

confidence: 99%

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

et al. 2019

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“…Some methods [23][24][25] make use of statistical models to estimate the readings of nodes. Owing to require few readings to respond of queries, statistical models can drastically reduce the amount of data sent by nodes [26].…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks

Wan

Xiong

Lộc

2018

Hum. Cent. Comput. Inf. Sci.

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110

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In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.

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“…In the case of single prediction approaches the system performs prediction in only one location whereas in the case of dual prediction approaches the system performs prediction at a local node along with the central server. Some notable prediction schemes applicable for both the categories mentioned above are adaptive filtering scheme [16], Autoregressive filter, Autoregressive Integrated Moving Average filter (ARIMA) [15], Kalman filtering and machine learning techniques [17]. Although some of the prior approaches can provide better accuracy for the model generation at the IoT device however given the severe computational constraints of the IoMT devices these approaches are not practical for local processing.…”

Section: B Prediction Based Iot Systemsmentioning

confidence: 99%

A Federated Filtering Framework for Internet of Medical Things

Sanyal

Wang

Nour

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (T olF ) and the local filtering parameter (δ); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95% of the communication cost while maintaining reasonable data privacy and low latency.

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Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Cited by 213 publications

References 49 publications

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks

A Federated Filtering Framework for Internet of Medical Things

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