보행자 항법에서 계단 보행 시 진행 방향 신호의 형상 분석을 통한 층 결정 알고리즘

학위논문(석사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2022.2. 박찬국.This master’s thesis presents a new algorithm for determining floors in pedestrian navigation. In the proposed algorithm, the types of stairs are classified by shape analysis, and the floors are determined based on the stair type. In order to implement our algorithm, the walking direction estimated through the Pedestrian Dead Reckoning (PDR) system is used. The walking direction signal has different shapes depending on the stair types. Then, shape analysis is applied to the signal shapes of the walking direction to identify the types of stairs and determine the floor change. The proposed algorithm is verified through simulations and experiments, and it is confirmed that it works well even when moving through multiple floors with several different types of stairs. It is also verified that the performance is superior to the conventional floor determination algorithm.본 논문에서는 관성 측정 장치(IMU: Inertial Measurement Unit)를 이용한 실내 보행자 항법에서 계단을 통한 층 이동 시 계단의 종류를 파악하여 층을 결정하는 새로운 알고리즘을 제안한다. 이를 위해 보행자항법(PDR: Pedestrian Dead Reckoning) 시스템에서 추정된 고도, 걸음 검출 시간, 그리고 방향각을 사용한다. 이때 추정된 고도는 계단 보행을 시작하거나 마칠 때 평지 보행과 구분될 정도의 정확도만 필요하다. 따라서 본 알고리즘에서는 기존의 층 구분 알고리즘에서 필요로 하는 고도 추정치에 대한 의존성을 최소화할 수 있다. 제안한 알고리즘에서는 계단 보행 시에 나타나는 방향각의 신호에 통계적 형상 분석(statistical shape analysis) 기법을 적용하여 계단의 종류를 파악한 후 층을 구분하게 된다. 시뮬레이션과 실험을 통해 제안한 알고리즘의 정확도를 검증하며 여러 종류의 계단을 여러 층 오르내리는 경우에도 알고리즘이 잘 동작함을 확인한다. 그리고 기존의 층 구분 알고리즘에서 발생하는 시간 지연 문제를 해결하고 층 구분 정확도가 높아진 것을 확인한다. 또한 본 연구는 관성 측정장치 이외의 다른 센서나 무선통신 장치를 사용하지 않으며 층 높이와 같은 건물에 대한 기본 정보를 가정하지 않고도 층을 잘 결정할 수 있다는 유효성을 가진다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objectives and Contributions 2 Chapter 2 Pedestrian Dead Reckoning System 4 2.1 Overview of Pedestrian Dead Reckoning 4 2.2 Integration Approach 5 2.2.1 Strapdown inertial navigation system 5 2.2.2 Extended Kalman filter 6 2.2.3 INS-EKF-ZUPT 7 Chapter 3 Shape Analysis 10 3.1 Euclidean Similarity Transformation 11 3.2 Full Procrustes Distance 12 Chapter 4 Floor Determination 14 4.1 Stair Types 15 4.2 Stair Type Classification Algorithm 17 4.3 Floor Determination Algorithm 18 Chapter 5 Simulation and Experimental Results 22 5.1 Simulation Results 22 5.2 Experimental Results Single Floor Change 30 5.3 Experimental Results Multiple Floor Changes 32 5.3.1 Scenario 1 32 5.3.2 Scenario 2 37 Chapter 6 Conclusion 40 6.1 Conclusion and Summary 40 6.2 Future Work 41 Bibliography 42 국문초록 46석

Efficient Context-aware Service Discovery in Multi-Protocol Pervasive Environments

International audienceService discovery is a critical functionality of emerging pervasive computing environments. In such environments, service discovery mechanisms need to (i) overcome the heterogeneity of hardware devices, software platforms, and networking infrastructures; and (ii) provide users with an accurate selection of services that meet their current requirements. To address these issues, we have developed the Multi- Protocol Service Discovery and Access (MSDA) middleware platform2, which provides context-aware service discovery and access in pervasive environments. This paper primarily focuses on the design and implementation of the context-awareness support of MSDA. Context-awareness not only provides a more accurate service selection, but also enables a more efficient dissemination of service requests across heterogeneous pervasive environments. We present the design and prototype implementation of MSDA, along with experimental results that demonstrate the advantages derived by introducing context awareness

Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

Identifying specific locations of items such as containers, warehouse pellets, and returnable packages in a large environment, for instance, in a warehouse, requires an extensive tracking system that could identify the location through data visualization. This is the similar case for radio-frequency identification (RFID) pallet level signal as the accuracy of determining the position for specific location either on the level or stacked in the same direction are read uniformly. However, there is no single study focusing on pallet-level classification, in particular on distance measurement of pallet height. Hence, a methodological approach that could provide the solution is essential to reduce the misplaced issues and thus reduce the problem in searching the products in a large-scale setting. The objective of this work attempts to define the pallet level of the stacked RFID tags through the machine learning techniques framework. The methodology started with the pallet-level which firstly determined by manual clustering according to the product code number of the tags that were manufactured for defining the actual level. An additional study of the radio frequency of the tagged pallet box in static condition was carried out by determining the feature of the time series. Various sample sizes of 1 Hz, 5 Hz and 10 Hz combined with the received signal strength of maximum, minimum, mode, median, mean, variance, maximum and minimum difference, kurtosis and skewness are evaluated. The statistical features of the received signal strength reading are analyzed by the selection of the univariate features, feature importance technique, and principal component analysis. The received signal strength of the maximum, median, and mean of all statistical features has been shown to be significant specifically for the 10Hz sample size. Different machine learning classifiers were tested based on the significant features, namely the Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes Support Vector Machine, and k-Nearest Neighbors. It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. In results, it was found that the Random Forest provided 92.44% of the test sets with the highest accuracy. In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. The best position that could achieve a classification accuracy of 93.30% through the validation process for position five (5) in the systematic model that is the centre of the pallet box. In conclusion, it can be inferred from the analysis that the Random Forest model has better predictive performance compared to the rest of the pallet level partition model with a height of 12 cm used in this research. Based on the train, validation, and test sets in Random Forest, the RFID capability to determine the position of the pallet can be detected precisely

Access Controls for Cooperatively Enabled Smart Spaces

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 75-79).Traditionally, access control mechanisms have been static in nature, requiring explicit intervention in order to change their behavior. However, since security requirements can be expected to change frequently and rapidly in ubiquitous computing environments, access control mechanisms that can react to the context in which they are applied are desirable. With this in mind, we have created ACCESS (Access Controls for Cooperatively Enabled Smart Spaces); a framework for enabling dynamic, role-based and context-aware access control mechanisms in ubiquitous computing applications.by Buddhika Kottahachchi.M.Eng

Human Behavior Cognition Using Smartphone Sensors

Peer reviewe

Runtime reconfiguration of physical and virtual pervasive systems

Today, almost everyone comes in contact with smart environments during their everyday’s life. Environments such as smart homes, smart offices, or pervasive classrooms contain a plethora of heterogeneous connected devices and provide diverse services to users. The main goal of such smart environments is to support users during their daily chores and simplify the interaction with the technology. Pervasive Middlewares can be used for a seamless communication between all available devices and by integrating them directly into the environment. Only a few years ago, a user entering a meeting room had to set up, for example, the projector and connect a computer manually or teachers had to distribute files via mail. With the rise of smart environments these tasks can be automated by the system, e.g., upon entering a room, the smartphone automatically connects to a display and the presentation starts. Besides all the advantages of smart environments, they also bring up two major problems. First, while the built-in automatic adaptation of many smart environments is often able to adjust the system in a helpful way, there are situations where the user has something different in mind. In such cases, it can be challenging for unexperienced users to configure the system to their needs. Second, while users are getting increasingly mobile, they still want to use the systems they are accustomed to. As an example, an employee on a business trip wants to join a meeting taking place in a smart meeting room. Thus, smart environments need to be accessible remotely and should provide all users with the same functionalities and user experience. For these reasons, this thesis presents the PerFlow system consisting of three parts. First, the PerFlow Middleware which allows the reconfiguration of a pervasive system during runtime. Second, with the PerFlow Tool unexperi- enced end users are able to create new configurations without having previous knowledge in programming distributed systems. Therefore, a specialized visual scripting language is designed, which allows the creation of rules for the commu- nication between different devices. Third, to offer remote participants the same user experience, the PerFlow Virtual Extension allows the implementation of pervasive applications for virtual environments. After introducing the design for the PerFlow system, the implementation details and an evaluation of the developed prototype is outlined. The evaluation discusses the usability of the system in a real world scenario and the performance implications of the middle- ware evaluated in our own pervasive learning environment, the PerLE testbed. Further, a two stage user study is introduced to analyze the ease of use and the usefulness of the visual scripting tool

Novel techniques for location-cloaked applications

Location cloaking has been shown to be cost-effective in mitigating location privacy and safety risks. This strategy, however, has significant impact on the applications that rely on location information. They may suffer efficiency loss; some may not even work with reduced location resolution. This research investigates two problems. 1) How to process location-cloaked queries. Processing such queries incurs significant more workload for both server and client. While the server needs to retrieve more query results and transmit them to the client, the client downloading these results wastes its battery power because most of them are useless. To address these problems, we propose a suite of novel techniques including query decomposition, scheduling, and personalized air indexing. These techniques are integrated into a single unified platform that is capable of handling various types of queries. 2) How a node V can verify whether or not another node P indeed locates in a cloaking region it claims. This problem is challenging due to the fact that the process of location verification may allow V to refine P\u27s location within the region. We identify two types of attacks, transmission coverage attack and distance bounding attack. In the former, V refines a cloaking region by adjusting its transmission range to partially overlap with the region, whereas in the latter, by measuring the round trip time of its communication with P. We present two corresponding counter strategies, and built on top of them, propose a novel technique that allows P to participate in location verification while providing a certain level of guarantee that its cloaking region will not be refined during the process