Selective AP probing for indoor positioning in a large and AP-dense environment

Jeon, Seokseong; Jeong, Jae-Pil; Suh, Young-Joo; Yu, Chansu; Han, Dongsoo

doi:10.1016/j.jnca.2017.09.010

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…It can be seen from the figure that TU1 is the target user, and the system needs to recommend movies for the target user. User U2 likes movies M1, M2, user U1 likes movies M1, M2, M3, target user TU1 likes movie M1 [7]. Movie M1 is similar to movie M2, so movie M2 can be recommended to target user TU1.…”

Section: ) Item-based Collaborative Filtering Algorithmmentioning

confidence: 99%

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.

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Section: ) Item-based Collaborative Filtering Algorithmmentioning

confidence: 99%

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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“…However, it is unrealistic to represent all missed APs with similar values when there will be more than one missed AP. In [28], the authors used selective APs for localization. However, in this type of approach, it is very difficult to implement whenever larger numbers of APs are missed.…”

Section: Proposed Systemmentioning

confidence: 99%

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

et al. 2018

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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.

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“…Nevertheless, replacing the missed AP values with a similar value is impractical to get accurate positioning in dynamic environments. In [42], the authors used selective APs for localization. This type of approach is very difficult to implement whenever they miss the larger numbers of APs.…”

Section: Proposed Systemmentioning

confidence: 99%

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

et al. 2018

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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.

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Selective AP probing for indoor positioning in a large and AP-dense environment

Cited by 7 publications

References 32 publications

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

Research on intelligent design algorithm of indoor space based on hybrid recommendation model

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

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