Modular Multilayer Perceptron for WLAN Based Localization

Ahmad, Uzair; Gavrilov, A. V.; Lee, Sungyoung; Lee, Young-Koo

doi:10.1109/ijcnn.2006.247351

Cited by 20 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [20], scholars used an artificial synaptic network, an MLP network, and a generalized radial basis function network through ToA measurements to localize sensors rather than localizing Wi-Fi users. MLP was applied to effectively reduce the uncertainty in the location estimation system [21]. Authors suggest that MLP is a commonly applicable method for pattern recognition in deep learning technologies.…”

Section: Related Workmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

View full text Add to dashboard Cite

Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Since AoA was used as a means of data collection approach, the proposed technique has LoS problem. In [35], multilayer perceptron (MLP) was applied for indoor positioning. A limited number of APs were used in a building.…”

Section: Related Workmentioning

confidence: 99%

“…In [21], authors discussed that the use of numerous datasets at each grid helps to adapt to signal fluctuations easily. Hence, we collect 35 For this work, all reachable APs from unknown locations are used for data collection purposes to make the system inclusive and to train the machine deeply from various signal values. In complex and hierarchical environments, the AP locations may not be known.…”

Section: Working Environments and Experimental Data Collectionmentioning

confidence: 99%

An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

et al. 2018

View full text Add to dashboard Cite

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

show abstract

“…TOA and TDOA can use effectively for outdoor positioning system. Non line of site problem and multipath effect problem induced by building geometry, human body absorption, dynamic environments make these techniques ineffective or complex for indoor positioning systems (Sayed, Tarighat and Khajehnouri, 2005) and (Ahmad et al, 2006). Location fingerprinting is used by most WLAN positioning systems, as it can compute accurate location estimates.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

Pahlavani

Gholami

Azimi

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

show abstract

Modular Multilayer Perceptron for WLAN Based Localization

Cited by 20 publications

References 13 publications

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

Contact Info

Product

Resources

About