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The report offers methods for developing an indoor navigation system. For this purpose, popular navigation applications have been analysed. We have been strongly motivated by the fact that no universal methods have been established that are applicable with this kind of projects. Very often the case is that new methods are formed in view of a specif...
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... are allowed to add their own map of a desired building, the information wherewith is included in the database. Guests, in turn, can make use of the services of the application by having only access to the current data -available maps, scanning a QR codes, and entering a hall number. The diagram of the mobile application developed is shown in Fig. ...
Citations
... Unfortunately, that is not the case for indoor scenarios. Localizing an object or route indoors using GPS is usually not feasible due to the loss of the signal emitted by its satellites [13]. Te complexity of indoor environments with walls and various objects contributes to this phenomenon. ...
This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.
... With the advancement and development of the architecture of intelligent systems, indoor navigation systems are becoming more important [6]. In any modern society, to help people reach their desired destination or achieve their required goals hassle-free and in a timely manner, there is a need for a navigational system. ...
Modern technologies such as the Internet of Things (IoT) and physical systems used as navigation systems play an important role in locating a specific location in an unfamiliar environment. Due to recent technological developments, users can now incorporate these systems into mobile devices, which has a positive impact on the acceptance of navigational systems and the number of users who use them. The system that is used to find a specific location within a building is known as an indoor navigation system. In this study, we present a novel approach to adaptable and changeable multistory navigation systems that can be implemented in different environments such as libraries, grocery stores, shopping malls, and official buildings using facial and speech recognition with the help of voice broadcasting. We chose a library building for the experiment to help registered users find a specific book on different building floors. In the proposed system, to help the users, robots are placed on each floor of the building, communicating with each other, and with the person who needs navigational help. The proposed system uses an Android platform that consists of two separate applications: one for administration to add or remove settings and data, which in turn builds an environment map, while the second application is deployed on robots that interact with the users. The developed system was tested using two methods, namely system evaluation, and user evaluation. The evaluation of the system is based on the results of voice and face recognition by the user, and the model’s performance relies on accuracy values obtained by testing out various values for the neural network parameters. The evaluation method adopted by the proposed system achieved an accuracy of 97.92% and 97.88% for both of the tasks. The user evaluation method using the developed Android applications was tested on multi-story libraries, and the results were obtained by gathering responses from users who interacted with the applications for navigation, such as to find a specific book. Almost all the users find it useful to have robots placed on each floor of the building for giving specific directions with automatic recognition and recall of what a person is searching for. The evaluation results show that the proposed system can be implemented in different environments, which shows its effectiveness.
This is an introductory text to a collection of papers from the ICSF 2021: Second International Conference on Sustainable Futures: Environmental, Technological, Social, and Economic Matters, which held at Kryvyi Rih National University, Kryvyi Rih, Ukraine, on May 19-21, 2021. It consists of an introduction, conference topics review, and some observations about the event and its future.
