Adaptive context-agnostic floor transition detection on smart mobile devices

The accuracy of pedestrian positioning is helpful to ensure the pedestrian safety in both indoor and outdoor environments. Improve the accuracy of pedestrian positioning is a key research issue. In order to solve the problem that the indoor and outdoor pedestrian navigation is not continuous and the accuracy is low, a pedestrian seamless navigation and positioning method based on BDS/GPS/IMU is proposed. In outdoor environment, in order to improve the availability of dynamic positioning when the single-system observation geometry is not ideal, the key techniques such as the differential coordinates and time benchmark in BDS/GPS positioning are studied, and a method of eliminating time difference by the independent combination difference in the system is proposed. This method simplifies the operation steps and overcomes the current compatible positioning difficulties without the time difference between BDS and GPS. It can be seen that the more the number of visible satellites, the better the space geometric distribution. In the combined positioning experiment results of BDS and GPS, the number of visual satellites are about 6–8 when GPS is used alone. When using the combined system, the number of visible satellites increased to about 16. The increase in the number of visible satellites greatly improves the observation geometry. For the Position Dilution of Precision (PDOP), the maximum PDOP of the dual system is 2.7, which is significantly lower than that of the single system, and the observation geometry performance is greatly improved. In the effective positioning time, for BDS/GPS, the positioning accuracy of elevation (U) direction is better than 4 cm, and the positioning accuracy of North (N) and East (E) direction is better than 2 cm. Based on the analysis of pedestrian gait characteristics, a multi-condition constrained zero-velocity detection algorithm is proposed. For the error of the inertial sensor error is accumulated over the time, the zero velocity update (ZUPT) algorithm is implemented to correct the cumulative errors by using to the designed extended Kalman filter (EKF) with the velocity and angular velocity information as the measurements. The results show that the accuracy of dual-mode positioning system of BDS compatible with GPS is better than the single-mode GPS positioning, the outdoor position accuracy can reach centimeter level, and under ZUPT compensation the indoor error ratio is 1%, which can achieve more accurate pedestrian seamless navigation.

This research study is an attempt to investigate and recommend the applications and usefulness of social media as a knowledge sharing platform using smart mobile devices and students’ academic performances. Research literature reveals that further examination of this idea is a must as it is increasingly becoming an integral part and a current mandate for digital-era learners. This study is exploratory based on learners’ data which includes students from foundation, junior and senior courses at a university. The objective was to cognize learners’ perceptions on collaborative learning while using applications of social media knowledge platform and smart mobile devices. Structural Model approach is followed to validate the data and undertake the required analysis. The resulting analysis shows that a significantly positive relation exists between the available platforms of knowledge applications using smart devices and its substantial impact on students’ overall performances. In addition, it could be observed that generally students have positive and valuable perception on the use of social media knowledge platform and smart mobile devices in collaborative learning settings. Moreover, such settings encourage students-students’ interactions, studentsteachers’ interactions, and the growing trends of students to online experts’ interactions. Furthermore, it was noticed that these interactions improve students’ engagement with such platforms using smart devices, which consequently has significant positive impact on students’ academic performances. The findings propose the inculcation in students the positive role of the use of social media knowledge platforms using smart devices so to encourage collaborative learning, to enhance active learning, to be more dynamic & engaging, to broaden their domain-knowledge, and to promote academic researchoriented learning, which are all crucial for graduate employability.

As smart mobile devices are widely used, mobile threats are more serious, so security in mobile becomes more and more important. However, the performance of these devices are not powerful enough to use the same security algorithms as PCs. Public key cryptosystems such as RSA need big primes to enhance the security, however, generating a big prime takes substantial time even on a PC. In this paper, we study two prime generation algorithms for smart mobile devices. First, we analyze a previous prime generation algorithm using a GCD test, named PGCD-MR, and show it sometimes performs inferior to the traditional TD-MR test. Second, we propose a new GCD test, named m-bit GCD-MR, for fast prime generation in both PCs and smart mobile devices. We compare the running times of PGCD-MR, m-bit GCD-MR, and TD-MR combinations on PCs and Samsung Galaxy Tab 10.1. The experimental results show our running time analysis is accurate (only 2% error) and m-bit GCD-MR test is the fastest among three prime generation algorithms. More exactly, m-bit GCD-MR test is about 20% faster than the TD-MR combination.

Indoor pedestrian navigation systems have become indispensable in modern times, facilitating efficient navigation through complex indoor environments such as malls and airports. Traditional GPS technology falls short in these settings due to signal blockage and reflections, leading to the emergence of indoor positioning systems (IPS). Leveraging technologies like Bluetooth beacons, Wi-Fi triangulation, and ultra-wideband signals, IPS provides precise real-time navigation within buildings. Augmented reality (AR) and virtual reality (VR) further enhance the indoor navigation experience, offering visual cues and pre-navigation simulations. Despite challenges such as precise mapping and signal interference, the adoption of indoor navigation technologies has increased, driven by the demand for better user experiences. This abstract explores the methods, technologies, and challenges associated with indoor pedestrian navigation, providing insights into current trends and future developments.

Many studies have revealed that homework (e.g., relaxation exercises) are crucial for remote therapeutic interventions. In this context, to monitor whether patients actually perform their homework and to check whether they perform it in the right way constitute complex tasks. So far, therapeutic interventions have not been properly supported by IT systems and, hence, the opportunities provided by mobile assistance have been neglected. For example, a smart mobile device may notify a patient about an assigned homework or motivate him to accomplish it in time. Moreover, the patient might be further assisted through a video providing detailed instructions. In turn, the smart mobile device could inform the therapist of the homework outcome. In practice, a proper support of the various types of homework is challenging, even when using modern IT systems. To remedy this drawback, we propose an approach integrating mobile services with process management technology in order to enable the complex coordination tasks that become necessary in connection with homework. For example, a process might enable remote monitoring of homework, giving therapists the opportunity of timely adjustments. In addition, the approach allows involving researchers by providing them with valuable data (e.g., heart rate) gathered during and after homework. This paper presents an approach for creating processes that run on smart mobile devices and enable flexible remote therapeutic intervention support. Such mobile approach significantly enhances therapy assistance on one hand and mobile homework-related scenarios on the other.

With the popularity of smart mobile devices, “context-aware” applications have attracted intense interest, for which location is one of the most essential contexts. Compared with outdoor localization, indoor localization has received much more attention from both academia and industry these days. Given the widespread use of WiFi hotspots, the received signal strength (RSS) fingerprint-based indoor localization technique is considered as a promising and practical solution because of its relatively high accuracy and low infrastructure cost. Inspired by our observation that sparsity is inherent to the WiFi signal, we present a new RSS fingerprint-based indoor localization approach, called SparseLoc . Through sparse representation of the fingerprints, SparseLoc can estimate a smart mobile device’s location with a small error most of the time. Although the correlation between neighboring fingerprints affects the localization accuracy, SparseLoc uses the similarity between principal components of fingerprints to alleviate this effect. Based on the empirical experiments, we demonstrate that SparseLoc improves the localization accuracy by over 25% compared with the existing WiFi signal-based localization methods.

This article is based on the degree work of researchers which is in development at the time of the creation of this document. It aims to disseminate research made on the development of electronic and control projects using smart mobile devices, by proposing a platform to make practices and testing of control systems. The platform is based on a microcontroller board Arduino ADK, together with a series of components commonly used in electronics projects. It also has a framework that facilitates the encoding of its programs. This platform is intended primarily to create projects in the areas: control systems, electronics, robotics, and domotics, among others, that require exchanging data with smart mobile devices. The platform is designed so that it can be easily replicated with low cost components.

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.

In large-scale indoor facilities such as airports, train stations, factories, and hospitals, the locations of mobile smart devices provide important information for grasping the activity state of people and the utilization state of things. Consequently, many indoor localization systems for people and things have been proposed. However, such systems require a large amount of localization equipment or advanced measurements of environmental physical characteristics, and thus they strongly depend on the infrastructure for localization. In this paper, we propose SmartFinder as a solution with extremely low dependence on the infrastructure. This localization system applies our previously proposed Cloud-based Self-Organizing Localization to mobile smart devices, such as smartphones carried by people and BLE devices attached to things. SmartFinder can track the location of many mobile smart devices with only three anchor nodes and in real time.

This article is part of a PhD research and investigates the impact of rapid technological evolution, focusing on the adoption of smart mobile devices (SMD), such as smartphones, tablets and laptops, and applications (app) in the personal lives of higher education professors in the area of Information Science. In addition, gaps are identified in existing research, emphasizing the need to understand how SMD is used in personal activities for personal knowledge management (PKM) practices and infocommunication. Specific objectives include identifying the most commonly used SMDs and apps, the frequency of such use, and how they are used for those practices. The methodology is qualitative, triangulating the integrative review with a questionnaire (N-10). The results point to interest in device ecologies for PKM and Info-communication, with communication apps (text and voice) and e-mail being the most important for teachers. It is hoped that the results of this study can contribute to the advancement of knowledge about the use of SMD by teachers and to the development of a generic descriptive model of infocommunicational behaviour and PKM that can be applied in other contexts.

Mobile cloud computing (MCC) allows smart mobile devices (SMD) to access the cloud resources in order to offload data from smartphones and to acquire computational services for application processing. A distinctive factor in accessing cloud resources is the communication link. However, the communication links between SMD and cloud resources are weak, which allows intruders to perform malicious activities by exploiting their vulnerabilities. This makes security a key challenge in the MCC environment. Several intrusion detection and response systems (IDRSs) are adapted to address the exploitation of vulnerabilities that affect smartphones, communication links between cloud resources and smartphones, as well as cloud resources. In this article, we discuss the cloud-based IDRS in the context of SMD and cloud resources in the MCC infrastructure. The stringent security requirements are provided as open issues along with possible solutions. The article aims at providing motivations for researchers, academicians, security administrators, and cloud service providers to discover mechanisms, frameworks, standards, and protocols to address the challenges faced by cloud-based IDRS for SMD.

The various services using smart mobile devices including smartphones have increased and personal smart mobile devices have utilized for the business use in many cases. There are the technologies for using smart mobile devices safely by separating the personal area and the business area such as TEE. This paper describes a MSS(Mobile Security Solution) system which is designed based on the TEE system architecture of Global Platform. The Secure Domain of the MSS System allows the secure preservation of sensitive data and the secure execution of sensitive functions. In this paper, we describe the architecture of the MSS System and the procedure for providing a secure message service by using the Secure Domain of the MSS system.

<p>The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.</p>

With the technical advances of wireless sensor networking and smart mobile device, the demand for position information of pedestrians (especially in the indoor environment) has increased remarkably. In this paper, we proposed an indoor localization approach based on received signal strength (RSS) and Bayesian filter. In the following sections, we describe our virtual modeling method of environment and the way we take object's movement sequences as history conditions in Bayesian filter. The experiment results show that our solution provides accurate tracking results (within 80 centimeters for moving object). The contribution of this research is that it provides a general implementation utilizing Bayesian filter which is able to estimate location precisely with off-the-shelf hardware. And Bluetooth Low Energy (BLE) is employed which reduces power consumption considerably. Meanwhile the accuracy is sufficient for pedestrian tracking in real application scenarios where BLE devices can be easily deployed. Simply connecting static points will distort the real trajectory. One solution for this problem is filter technique. Filter technique is not only can be used in signal measurements, but also on general position estimations. In this paper we propose a Bluetooth indoor pedestrian tracking system which employs RSS as scene profiling basis. During the tracking process, our system keeps iterating a recursion of prediction and update in which Bayes filter (4) is the mathematical fundamental. Other than the classic procedure of filtering the measurement data only, our filter integrates context information and history record as conditions for the estimation. Our evaluation shows that our combination of current measurements with history records increases both the accuracy and efficiency. The rest of this paper is organized as follows: Section II overviews fundamental and essential related works. Then Section III describes detailed implementation. After that in Section IV we present an evaluation of the proposed algorithm by comparing it with existing RSS-based fingerprint approaches. Finally, Section VI summarizes the main conclusions and gives directions for future work.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.