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Low cost and high reproducible is a key issue for sustainable location-based services. Currently, Wi-Fi fingerprinting based indoor positioning technology has been widely used in various applications due to the advantage of existing wireless network infrastructures and high positioning accuracy. However, the collection and construction of signal ra...
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... landmarks are defined as visually salient structures in indoor space that anchors special locations, such as intersections, entrance, corners, etc. As shown in Figure 2, 8 different types of structure landmark have been defined, including FT (T-junction at front angle), LT (T-junction at left angle), RT (T-junction at right angle), LL (L-junction at left angle), RL (L-junction at right angle), EC (end of corridor), CW (corridor to wide area) and WC (wide area to corridor). Each type of structure landmark has a special structural and visual characteristic, which is a basis for landmark recognition. ...
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Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms...
Citations
... A sustainable indoor localization method is presented by Liu, T. et al. in 2021 [15] and uses structure cues like doors, walls, and corners as reference points for mapping radio signals inside an indoor environment. They suggest a three-step radio signal mapping process that includes data gathering, identifying structures as landmarks, and radio signal mapping. ...
For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an effective and privacy-protective federated architecture-based framework for location classification via Wi-Fi fingerprinting. The federated indoor localization classification (f-ILC) system that was suggested had distributed client–server architecture with data privacy for any and all related edge devices or clients. To try and evaluate the proposed f-ILC framework, different data from different sources on the Internet were collected and given in a format that had already been processed. Experiments were conducted with standard learning, federated learning with a single client, and federated learning with several clients to make sure that federated deep learning models worked correctly. The success of the f-ILC framework was computed using a number of factors, such as validation of accuracy and loss. The results showed that the suggested f-ILC framework performed better than traditional distributed deep learning-based classifiers in terms of accuracy and loss while keeping data secure. Due to its innovative design and superior performance over existing classifier tools, edge devices’ data privacy makes this proposed architecture the ideal solution.
... The WIFI fingerprint technique [9,13,14] has been more widely used than any other sustainable indoor positioning method due to its advantages. The fingerprint-based indoor positioning system achieves an accuracy in the range of 2-5 m [13] for the wireless local area network (WLAN) RSS using the KNN comparison/probabilistic method with the recorded offline data. ...
... It requires the maintenance of a correct radio map fingerprint dataset [24] to periodically estimate the precise position [16,34]. The location-aware map [9] uses the conscious position, camera image, and fusion of the [35] inertial sensor to solve this venture in a practical world. ...
Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.
Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.
Applying digital technologies to preserve cultural heritage is hardly a new concept. In 1990s, with the launch of the Memory of the World project by UNESCO, scholars all over the world began to pay attention to the digital protection of cultural heritage, both tangible and intangible [1]. Through the Memory of the World research network that could be forged globally, UNESCO aims for concerted efforts in mediating, safeguarding, and even promoting cultural heritage by new means of information communicating technologies [2]. Nonetheless the efforts, although commendable, have been more focused on the digital documentation instead of promotion of cultural heritage. Concurrently, as China attaches great importance to “entrepreneurship and entrepreneurship” education, college students’ innovation and entrepreneurship education has become an important field of research in China. This study defines intangible cultural heritage, explains digitization of intangible cultural heritage, points out the limitations of the efforts on digital preservation, overviews entrepreneurship education for college students in China, and proposes a pedagogical model targeted at new media art in higher education which seeks to propagate the efforts to digitally preserve and promote intangible cultural heritage under the values of innovation and entrepreneurship.
With the increasing user demands for the ubiquitous availability of location-based services, and the acknowledgement of their substantial business prospects, researchers have extensively studied indoor navigation techniques that do not require extra infrastructures to provide accurate indoor navigation. Recently, Landmark, which is available in many scenarios without additional deployment cost, are integrated in plenty of works. However, existing feature extraction and selection methods to detect landmark involve manual feature engineering, which is time-consuming, laborious, and prone to error and additional Wi-Fi fingerprint is used to identify landmark. Therefore, this paper proposes an indoor navigation based on landmark, which is recognized by an unsupervised feature learning method to automatically extracts and selects the features, without extra deployment cost. The proposed method jointly trains denoising autoencoder implemented by convolutional neural network and LSTM neural networks produces a compact feature representation of the data to identify landmark. Besides, the relative distance between different landmarks is estimated by PDR to generate the indoor landmark map with the help of multidimensional scaling technique. The effectiveness of the proposed framework is verified through the experiments in the context of practical buildings. Experimental results show that the proposed method, which learns useful features automatically outperforms conventional classifiers that require the hand-engineering of features. We also show that the proposed method can build mostly correct indoor maps and provides efficient directions to users without extra infrastructure.
