Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

Turgut, Zeynep; Üstebay, Serpil; Aydın, Muhammed Ali; Aydın, Gülsüm Zeynep Gürkaş; Sertbaş, Ahmet

doi:10.1002/ett.3705

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…Turgut et al proposed a system [ 49 ] for indoor localization to offer a minimum cost of infrastructure and utilizing technologies of the present building. They generated the signal map by utilizing a fingerprinting approach, namely HALICDB.…”

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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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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“…Moreover, deep learning is used in IoT such as indoor localization 19 and data transmission. 20 Particularly, it is revealed in Reference 21 that deep learning can achieve better performance in user detection and channel estimation for IoT networks with a new structure of block-restrict neural network (BRNN). The work in Reference 21 reveals the fact that, through sufficient training, deep learning has advantages on both performance and computational complexity compared against OMP and other CS methods, and the computation time is reduced more than an order of magnitude.…”

Section: Related Workmentioning

confidence: 99%

User activity detection for massive Internet of things with an improved residual convolutional neural network

Zhang

2020

Trans Emerging Tel Tech

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Massive user activity detection is a challenging task for massive Internet of things (mIoT). In this paper, we propose a new deep neural network, named concentrated layers convolutional neural network (CLCNN), for user activity detection in mIoT. We firstly propose three basic rules in the design of residual network specifically for mIoT scenarios. Secondly, with the rules above we develop a new improved residual network block which includes integrated convolutional layers with activation functions, by which the residual convolutional network is constructed. Moreover, the regularization and its corresponding hyperparameter for the proposed network are also investigated against overfitting. Simulation results show that the proposed CLCNN network outperforms the existing deep learning algorithm and conventional compressive sensing solutions in user activity detection and corresponding channel estimation.

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“…Then, with the fusion of information technology and video communication technology, the form of online education has been greatly diversified, and the user group is growing continuously [16,17]. Taking online English learning system for example, since students of different grades and groups need to learn different knowledge, different knowledge databases need to be built on a same platform [18]. The popularization of mobile apps is bound to create fierce competition, and the design and development of an online app requires technical support such as cloud service, platform construction, course preparation, and system docking, etc.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Intelligent Online English Learning System Based on Mobile Internet Technology

Ying

Yan

2021

Int. J. Emerg. Technol. Learn.

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A predicament facing education is that the traditional teaching mode could no longer meet the requirements of modern society. The fast development of mobile Internet technology has brought online education platforms into existence, and these newborn things are very helpful to solve the mentioned contradiction. To deepen the research of the application of mobile Internet technology in modern education, this paper designed the functional modules of an intelligent online English teaching (IOET) system and implemented the proposed system. In our study, satisfying students’ learning requirements had been taken as the core principle when building the proposed system, and the database design had followed the principles of standardization, consistency, security, integrity, scalability, and ease of maintenance. The IOET system adopts a three-layer architecture, from top down, the system has a representation layer, a business logic layer, and a data service layer. The main management modules of the system include course notification module, visitor management module, and homework management module, etc. Through this paper, we hope to provide a piece of evidence for the basic research on the upgrade and improvement of intelligent online English teaching platforms.

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Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment

Cited by 10 publications

References 34 publications

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

User activity detection for massive Internet of things with an improved residual convolutional neural network

Design and Implementation of an Intelligent Online English Learning System Based on Mobile Internet Technology

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