Machine Learning Algorithm for Wireless Indoor Localization

Machine Learning Algorithm for Wireless Indoor Localization
Author: Osamah Ali Abdullah
Publisher:
Total Pages:
Release: 2018
Genre: Computers
ISBN:
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Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.

Machine Learning for Indoor Localization and Navigation

Machine Learning for Indoor Localization and Navigation
Author: Saideep Tiku
Publisher: Springer Nature
Total Pages: 563
Release: 2023-06-29
Genre: Technology & Engineering
ISBN: 3031267125
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While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve the accuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation. In particular, the book: Provides comprehensive coverage of the application of machine learning to the domain of indoor localization; Presents techniques to adapt and optimize machine learning models for fast, energy-efficient indoor localization; Covers design and deployment of indoor localization frameworks on mobile, IoT, and embedded devices in real conditions.

Wireless Indoor Localization

Wireless Indoor Localization
Author: Chenshu Wu
Publisher: Springer
Total Pages: 220
Release: 2018-08-22
Genre: Computers
ISBN: 9811303568
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This book provides a comprehensive and in-depth understanding of wireless indoor localization for ubiquitous applications. The past decade has witnessed a flourishing of WiFi-based indoor localization, which has become one of the most popular localization solutions and has attracted considerable attention from both the academic and industrial communities. Specifically focusing on WiFi fingerprint based localization via crowdsourcing, the book follows a top-down approach and explores the three most important aspects of wireless indoor localization: deployment, maintenance, and service accuracy. After extensively reviewing the state-of-the-art literature, it highlights the latest advances in crowdsourcing-enabled WiFi localization. It elaborated the ideas, methods and systems for implementing the crowdsourcing approach for fingerprint-based localization. By tackling the problems such as: deployment costs of fingerprint database construction, maintenance overhead of fingerprint database updating, floor plan generation, and location errors, the book offers a valuable reference guide for technicians and practitioners in the field of location-based services. As the first of its kind, introducing readers to WiFi-based localization from a crowdsourcing perspective, it will greatly benefit and appeal to scientists and researchers in mobile and ubiquitous computing and related areas.

Learning Indoor Localization Using Radio Received Signal Strength

Learning Indoor Localization Using Radio Received Signal Strength
Author: Gauri Kulkarni
Publisher:
Total Pages: 54
Release: 2016
Genre:
ISBN:
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With this research we will investigate a novel machine learning approach to the prediction of location from received signal strength indicators (RSSI) values obtained from these transmitting access points. Indoor localization has been a long- standing problem in recent times and gaining popularity among researchers. In this research we aim to solve this problem in an indoor environment like office buildings using radio received signals strengths. The most popular approach for positioning has been GPS (Global Positioning System). But we all know that it is inadequate when we consider indoor environments. Hence to solve this issue; we make use of the radio received signal strengths. The most common technology used for indoor positioning is Wi-Fi, which uses radio signals as its signal propagation medium. In this research we are proposing to create an indoor localization system radio signal strengths from as low- energy BLE t echnology from Bluetooth as access points that were easily available, where the locations of the se access points will be unknown. The RSSI obtained from these beacons will be used to predict the locations using machine-learning algorithms. For evaluating our theory we are using the classic fingerprinting method as our baseline for the evaluations. To evaluate this we considered the classic algorithm of nearest neighbor, which is used as a classic method for implementing fingerprinting.

Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking

Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking
Author: Mao, Guoqiang
Publisher: IGI Global
Total Pages: 526
Release: 2009-05-31
Genre: Computers
ISBN: 1605663972
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Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.

Machine Learning

Machine Learning
Author: Hamed Farhadi
Publisher: BoD – Books on Demand
Total Pages: 231
Release: 2018-09-19
Genre: Computers
ISBN: 1789237521
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The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

Recent Advances in Indoor Localization Systems and Technologies

Recent Advances in Indoor Localization Systems and Technologies
Author: Gyula Simon
Publisher: MDPI
Total Pages: 502
Release: 2021-08-30
Genre: Technology & Engineering
ISBN: 303651483X
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Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods.

Computer Networks and Inventive Communication Technologies

Computer Networks and Inventive Communication Technologies
Author: S. Smys
Publisher: Springer Nature
Total Pages: 1212
Release: 2021-06-02
Genre: Technology & Engineering
ISBN: 9811596476
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This book is a collection of peer-reviewed best selected research papers presented at 3rd International Conference on Computer Networks and Inventive Communication Technologies (ICCNCT 2020). The book covers new results in theory, methodology, and applications of computer networks and data communications. It includes original papers on computer networks, network protocols and wireless networks, data communication technologies, and network security. The proceedings of this conference is a valuable resource, dealing with both the important core and the specialized issues in the areas of next generation wireless network design, control, and management, as well as in the areas of protection, assurance, and trust in information security practice. It is a reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners for advance work in the area.

Analysis and Evaluation of Wi-Fi Indoor Positioning Systems Using Smartphones

Analysis and Evaluation of Wi-Fi Indoor Positioning Systems Using Smartphones
Author: David Hinojosa Muñoz
Publisher:
Total Pages:
Release: 2016
Genre:
ISBN:
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This paper attempts to analyze the main algorithms used in Machine Learning applied to the indoor location. New technologies are facing new challenges. Satellite positioning has become a typical application of mobile phones, but stops working satisfactorily in enclosed spaces. Currently there is a problem in positioning which is unresolved. This circumstance motivates the research of new methods. After the introduction, the first chapter presents current methods of positioning and the problem of positioning indoors. This part of the work shows globally the current state of the art. It mentions a taxonomy that helps classify the different types of indoor positioning and a selection of current commercial solutions. The second chapter is more focused on the algorithms that will be analyzed. It explains how the most widely used of Machine Learning algorithms work. The aim of this section is to present mathematical algorithms theoretically. These algorithms were not designed for indoor location but can be used for countless solutions. In the third chapter, we learn gives tools work: Weka and Python. the results obtained after thousands of executions with different algorithms and parameters showing main problems of Machine Learning shown. In the fourth chapter the results are collected and the conclusions drawn are shown.