Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network

Belay, Abebe; Lin, Hsin‐Piao; Wang, Li-Chun

doi:10.1109/jiot.2019.2948075

Cited by 46 publications

(22 citation statements)

References 26 publications

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“…For example, in [30], the authors found that using a sequence of 200 RSS samples collected at different timestamps gave better results than using a larger or smaller number of them. Similar findings were reported in [31]- [33].…”

Section: A Modeling Environmentsupporting

confidence: 91%

“…3) Dimensionality reduction: When the number of input parameters is increased disproportionately with respect to the underlying complexity of the problem, the computational performance of the network is compromised. For these cases, dimensionality reduction techniques can be helpful [31]. For example, in [23], the authors discretized the path between the…”

Section: A Modeling Environmentmentioning

confidence: 99%

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An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

Seretis,

Sarris

2021

Preprint

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We give an overview of recent developments in the modeling of radiowave propagation, based on machine learning algorithms. We identify the input and output specification and the architecture of the model as the main challenges associated with machine learning-driven propagation models. Relevant papers are discussed and categorized based on their approach to each of these challenges. Emphasis is given on presenting the prospects and open problems in this promising and rapidly evolving area.

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Section: A Modeling Environmentsupporting

confidence: 91%

Section: A Modeling Environmentmentioning

confidence: 99%

An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

Seretis,

Sarris

2021

Preprint

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“…The proposed model for trajectory prediction is based on regression-based machine learning methods. Common metrics to evaluate regression-based machine learning algorithms are mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) [47], [17]. We used these three metrics to evaluate and compare the performances of all the methods.…”

Section: Resultsmentioning

confidence: 99%

“…One popular example of its application is point-of-interest recommendation methods (e.g., restaurant, park, gas station, ...) [16], [17], [18]. Our work is a part of the first category.…”

Section: Related Workmentioning

confidence: 99%

A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Bahra

Pierre

2022

IEEE Access

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Future mobile networks are envisioned to have critical limitations in terms of latency, energy usage, capacity and network resources since these networks are expected to become extremely dense and complex. The rapid enormous advances in recent technologies such as Internet of Things (IoT) highlights the urgent need for network performance enhancement as well. To this end, self-organizing networks are a promising solution to push the network performance to the next level. These scalable networks can dynamically adapt to possible changes in the network. Smart mobility management, in particular mobility prediction, is a subsection of self-organizing functions which are mainly based on the machine learning techniques. In this paper, we propose to estimate user's future trajectory using machine learning approaches for a better network management. We propose a novel bidirectional trajectory prediction model called BTPM to model the user mobility behavior. The proposed method exploits the potential benefits of bidirectional gated recurrent unit (GRU) for having an accurate prediction. Moreover, we introduce a data preprocessing phase to obtain better results with significantly lower execution time. The proposed approach takes full advantage of data analysis in both directions (backward and forward) in order to provide a longterm prediction and model user's mobility even with complex patterns. Experimental results show that the proposed bidirectional approach significantly improves the performance of the mobility predictor in terms of model accuracy, robustness and execution time. It achieves a model error of 0.014 and decreases the execution time up to 97%.

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“…Outcomes of their system provided useful predictions of the location of the user. Some studies suggested [ 47 ] GRU as deep learning model to test on such optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Sun

Wei

et al. 2021

Sensors

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Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.

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Mobility Predictions for IoT Devices Using Gated Recurrent Unit Network

Cited by 46 publications

References 26 publications

An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

An Overview of Machine Learning Techniques for Radiowave Propagation Modeling

A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

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