Machine learning and deep learning methods for wireless network applications

Chen, Abel C. H.; Jia, Wen-Kang; Hwang, Feng-Jang; Liu, Genggeng; Song, Fangying; Pu, Lianrong

doi:10.1186/s13638-022-02196-2

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…In practical terms, there exists a non-zero probability that the sensitivity of certain statistically calibrated sensors may fall outside the specified limits. However, the emergence of cutting-edge machine learning and deep learning methods [23] might offer a possible means to mitigate this issue by assessing the congruence of the physical signals detected by sensors within the same network.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

Prato,

Pennecchi,

Genta

et al. 2023

Metrologia

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Low-cost sensors and in particular Micro-Electro-Mechanical Systems (MEMS) devices are widely used in many applications, including consumer electronics, healthcare, automotive, and industrial automation. Their large-scale production (typically in the order of millions per week in a single factory) would require the calibration of a huge number of devices that would be costly and time-consuming. A solution can be found in the use of statistical methods in order to (at least partially) substitute for the typical calibration procedures. In this work, we propose a Bayesian method to statistically calibrate large batches of sensors using probabilistic models and prior knowledge. The method involves experimentally calibrating only a small sample of sensors, then infer the number of reliable sensors in the entire batch and assign an appropriate uncertainty to all the sensors. Therefore, it can be considered as a statistical calibration of the batch. The Bayesian nature of this approach allows reducing the number of experimental calibrations by incorporating the prior knowledge coming from the previous calibration of a “benchmark” batch, which is performed “once and for all” and is representative of the whole production process. The application and validation of the method are performed through the calibration of 100 digital MEMS accelerometers. Validation results showed an acceptable agreement between experimental-based bootstrap and theoretical values, with relative differences within ± 7 %.

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Section: Discussionmentioning

confidence: 99%

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

Prato,

Pennecchi,

Genta

et al. 2023

Metrologia

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“…ANNs have been used in several areas, such as engineering applications and WSN applications [ 14 , 19 ]. Several types of neural networks are described in [ 13 ]. Generally, an ANN can be defined as a system or mathematical model that consists of many nonlinear artificial neurons running in parallel and may be generated as one layered or multilayered.…”

Section: Dnn-based Estimated Distance Correctionmentioning

confidence: 99%

“…Several ML techniques have been investigated in this context such as, namely, support vector machine (SVM), artificial neural network (ANN), etc. [ 11 , 12 , 13 , 14 ]. The key hurdle these techniques face in common is the requirement of large training data sets.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Esheh,

Affes

2024

Sensors

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Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

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“…In recent years, the use of ML to mine the laws hidden in the dataset in order to find out the localization model has become a development trend in wireless sensor network localization research. For example, Statistical Regression (SR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and DL [15][16][17][18][19]. In this paper, we utilize deep neural networks in deep learning to train a model using the original DV-Hop estimated distance and the actual distance as a dataset to correct the estimated distance to reduce the localization error.…”

Section: Related Workmentioning

confidence: 99%

Localization algorithm based on deep neural network and improved COA optimization

yu,

ye,

liu

et al. 2024

Preprint

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To improve the positioning accuracy of range-independent positioning algorithms in wireless sensor networks, a DV-Hop localization algorithm (DDCO) based on deep learning and improved crayfish optimization was proposed. Firstly, the dual communication radius subdivision of the minimum number of hops is introduced to reduce the error due to the number of hops; then the trained deep neural network model is used to correct the estimated distance to reduce the distance estimation error; finally, the random center of gravity inverse learning and the improved crayfish algorithm with nonlinear function are introduced to calculate the coordinates of the unknown nodes, and the global optimization capability of the intelligent algorithm is used to reduce the error generated by the DNN. The simulation results show that the positioning error of the DDCO algorithm is reduced by 54.4%, 23.4%, and 10.5%, respectively, compared with DV-Hop and other comparison algorithms under different communication radii. Under different beacon node densities, the error decreases by 46.2%, 24.2%, and 10.6%, respectively. Under different node densities, the error decreases by 49.6%, 30.3%, and 17.3%, respectively.

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Machine learning and deep learning methods for wireless network applications

Cited by 5 publications

References 30 publications

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Localization algorithm based on deep neural network and improved COA optimization

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