The document discusses enhancing indoor localization using IoT techniques. It proposes a framework that uses a quaternion-based extended Kalman filter for heading estimation in pedestrian dead reckoning (PDR), along with low pass filtering and adaptive step length methodology. This approach achieved an average error of 0.16 meters, representing 0.07% of the total 210 meters traveled in experiments. The document also discusses using IoT devices to further improve indoor localization accuracy.
This document discusses indoor positioning using Wi-Fi signals. It examines the accuracy of location fingerprinting based indoor positioning using Wi-Fi. The study achieved a positioning accuracy of 2.0 to 2.5 meters. It found that using additional access points, including those in other buildings and in the 5GHz frequency band, improved accuracy. The document also provides background on Wi-Fi technology and discusses applications of indoor positioning like tracking goods, people and animals.
The document discusses building an indoor tracking system using Wi-Fi routers that can provide navigation for areas where GPS does not work. It aims to build a low battery consuming system that can locate users inside buildings. The proposed system would use Wi-Fi signal strengths from multiple routers to determine a user's location through trilateration and then provide navigation to destinations by matching the position to an indoor map. Key components discussed are positioning techniques, mapping, software requirements, and potential applications in malls, hospitals and industries.
Determining a person’s physical position in a multi-building indoor space using wifi fingerprinting on UJIIndoor Data Set to construct machine learning models.
WLAN and Bluetooth Indoor Positioning SystemProjectENhANCE
The document summarizes the development of an indoor positioning system using WLAN and Bluetooth technologies. It describes how a team of 9 members created a prototype system over the course of a year that uses signal fingerprinting and positioning algorithms running on an Android phone to pinpoint its location within a school campus. The system was improved throughout development to add Bluetooth, more advanced algorithms, usability enhancements, and scalability features. It provides technical details on the positioning engine, system implementation across servers and clients, and how to use the calibration and positioning applications.
Precision (Indoor) Real Time Location SystemsPeter Batty
This document summarizes an indoor real-time location system (RTLS) using ultrawideband (UWB) technology. It can track tags with 15cm accuracy. The system uses sensors to detect time-of-arrival of UWB signals from tags to determine precise 3D locations. Example applications discussed include tracking assets in manufacturing plants and ports with accuracy better than WiFi and RFID. The system has also been used to track soldiers in military training exercises, track vehicles and tools at BMW, and monitor dairy cows on farms.
This document describes an indoor navigation Android application that uses Wi-Fi fingerprinting for localization and a routing algorithm to navigate between nodes on a map. It discusses challenges with GPS indoors and explores localization techniques including Wi-Fi, Bluetooth, and sensors. The application utilizes a SQLite database of Wi-Fi fingerprints mapped to locations, calculates the user's position by comparing live readings to stored values, and determines displacement using accelerometer and gyroscope data. It draws the user's position on a map and calculates a path between nodes using numbering to navigate between points of interest selected on the interface.
Mobile IP is an Internet Engineering Task Force (IETF) standard designed to allow mobile device users to move between networks while maintaining a permanent IP address. It uses a home address for identification and a care-of address for routing. Key functions include foreign agent discovery, home agent registration using registration requests and replies, and tunneling via encapsulation to forward packets to the mobile node's care-of address. Route optimization enables direct communication between a correspondent node and the mobile node to improve efficiency.
The document discusses indoor positioning solutions (IPS). It provides background on the growing indoor location market with over 130 companies working on indoor mapping, tracking, and navigation technologies. IPS can be used for navigation, emergency response, tracking people and assets, and user applications like social networking and shopping. Technical approaches to IPS include terminal-based methods using the device itself for positioning, infrastructure-based methods using dedicated indoor infrastructure or existing WiFi networks, and hybrid methods. Baseline positioning methods discussed are connection-based positioning using cell/access point IDs, trilateration/multilateration using signal strength or timing to estimate distance, triangulation using angle of arrival, and fingerprinting using spatial radio environment maps. Google aggregates WiFi
This document discusses indoor positioning using Wi-Fi signals. It examines the accuracy of location fingerprinting based indoor positioning using Wi-Fi. The study achieved a positioning accuracy of 2.0 to 2.5 meters. It found that using additional access points, including those in other buildings and in the 5GHz frequency band, improved accuracy. The document also provides background on Wi-Fi technology and discusses applications of indoor positioning like tracking goods, people and animals.
The document discusses building an indoor tracking system using Wi-Fi routers that can provide navigation for areas where GPS does not work. It aims to build a low battery consuming system that can locate users inside buildings. The proposed system would use Wi-Fi signal strengths from multiple routers to determine a user's location through trilateration and then provide navigation to destinations by matching the position to an indoor map. Key components discussed are positioning techniques, mapping, software requirements, and potential applications in malls, hospitals and industries.
Determining a person’s physical position in a multi-building indoor space using wifi fingerprinting on UJIIndoor Data Set to construct machine learning models.
WLAN and Bluetooth Indoor Positioning SystemProjectENhANCE
The document summarizes the development of an indoor positioning system using WLAN and Bluetooth technologies. It describes how a team of 9 members created a prototype system over the course of a year that uses signal fingerprinting and positioning algorithms running on an Android phone to pinpoint its location within a school campus. The system was improved throughout development to add Bluetooth, more advanced algorithms, usability enhancements, and scalability features. It provides technical details on the positioning engine, system implementation across servers and clients, and how to use the calibration and positioning applications.
Precision (Indoor) Real Time Location SystemsPeter Batty
This document summarizes an indoor real-time location system (RTLS) using ultrawideband (UWB) technology. It can track tags with 15cm accuracy. The system uses sensors to detect time-of-arrival of UWB signals from tags to determine precise 3D locations. Example applications discussed include tracking assets in manufacturing plants and ports with accuracy better than WiFi and RFID. The system has also been used to track soldiers in military training exercises, track vehicles and tools at BMW, and monitor dairy cows on farms.
This document describes an indoor navigation Android application that uses Wi-Fi fingerprinting for localization and a routing algorithm to navigate between nodes on a map. It discusses challenges with GPS indoors and explores localization techniques including Wi-Fi, Bluetooth, and sensors. The application utilizes a SQLite database of Wi-Fi fingerprints mapped to locations, calculates the user's position by comparing live readings to stored values, and determines displacement using accelerometer and gyroscope data. It draws the user's position on a map and calculates a path between nodes using numbering to navigate between points of interest selected on the interface.
Mobile IP is an Internet Engineering Task Force (IETF) standard designed to allow mobile device users to move between networks while maintaining a permanent IP address. It uses a home address for identification and a care-of address for routing. Key functions include foreign agent discovery, home agent registration using registration requests and replies, and tunneling via encapsulation to forward packets to the mobile node's care-of address. Route optimization enables direct communication between a correspondent node and the mobile node to improve efficiency.
The document discusses indoor positioning solutions (IPS). It provides background on the growing indoor location market with over 130 companies working on indoor mapping, tracking, and navigation technologies. IPS can be used for navigation, emergency response, tracking people and assets, and user applications like social networking and shopping. Technical approaches to IPS include terminal-based methods using the device itself for positioning, infrastructure-based methods using dedicated indoor infrastructure or existing WiFi networks, and hybrid methods. Baseline positioning methods discussed are connection-based positioning using cell/access point IDs, trilateration/multilateration using signal strength or timing to estimate distance, triangulation using angle of arrival, and fingerprinting using spatial radio environment maps. Google aggregates WiFi
The document discusses an indoor navigation system that uses WiFi positioning to determine a user's location inside a building and provide routing directions. It covers several key aspects:
1. An overview of various indoor positioning technologies including GPS, cellular, infrared, UWB, Bluetooth, and WiFi. WiFi is identified as the preferred approach due to widespread availability and low cost.
2. Details on how WiFi positioning works, including techniques like triangulation, fingerprinting, and considerations of RSSI, MAC addresses, and network details.
3. The system's objectives of indoor positioning, routing, and tracking. It also outlines implementation in models like universities and shopping malls.
4. Additional technical components
The document proposes an indoor navigation system for malls using Wi-Fi signal strength. It would determine a user's position by calculating the intersection point of signal strengths from three access points. The system would find the shortest path between a user's location and destination using Dijkstra's algorithm on a graph of the mall's floorplan. The project aims to help users navigate inside large indoor spaces like malls and offices.
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
This document discusses 3D passwords as a new authentication technique that combines existing methods like text passwords, graphical passwords, and biometrics into a single 3D virtual environment. The user interacts with various objects in the virtual world to create their unique 3D password. When logging in, they must recreate the same sequence of interactions. This makes 3D passwords more secure by increasing the number of possible passwords and making them difficult for attackers to guess. The document outlines how a 3D password system would work, including designing the virtual environment, recording the user's interactions as their password, and guidelines for the virtual world design like real-life similarity, unique distinguishable objects, and appropriate size.
The document provides an introduction to IoT including definitions, characteristics, genesis, applications and challenges. It describes the physical design of IoT including IoT devices, protocols, and the generic block diagram of an IoT device. It also describes the logical design including IoT functional blocks, communication models like publish-subscribe, request-response, levels of IoT deployment from level 1 to 6, and enabling technologies.
The document discusses middleware for indoor location-based services (LBSs). It provides an overview of key enabling technologies for indoor LBSs like indoor positioning, mapping, geocoding, geofencing, and routing. It also describes reference architectures and available open-source frameworks like the i-locate toolkit that can be used to build indoor LBS applications by reusing common functionalities and avoiding re-inventing solutions. Specifically, the i-locate toolkit enables self-navigation applications and asset tracking applications using indoor maps and positioning.
Bringing ArcGIS spatial analysis to bear on IoT dataEsri UK
IoT is changing the way we manage key systems and plan our future, generating ever more digital data. This session explores the new capabilities in ArcGIS that are making it easier to apply the power of spatial analysis to IoT data. Whether you need to ask questions of real time feeds or draw insight from ever larger big data sets, there are tools to help.
This document discusses location-based services (LBS) and evaluates different positioning techniques used in LBS. It begins by introducing common LBS applications and services. It then examines the components and architecture of LBS systems, including LBS middleware and location tracking. Privacy concerns with LBS are also addressed. The document evaluates and compares several positioning systems used in LBS, including satellite-based GPS, network-based methods like GSM, and indoor positioning techniques. It concludes by discussing limitations and opportunities for future work improving LBS positioning accuracy and privacy.
A mobile ad hoc network (MANET) consists of devices connected wirelessly without infrastructure. Each device is free to move and change connections, making the network topology highly dynamic. Devices must forward unrelated traffic, acting as routers. The primary challenge is maintaining routing information as the topology changes frequently. MANETs can connect to the internet or operate independently, using radio frequencies for short-range communication between peers. Examples include networks between vehicles or smartphones without cellular access.
This document summarizes a seminar presentation on Mobile Ad-Hoc Networks (MANETs). It introduces MANETs as networks without infrastructure where nodes can connect in dynamic and flexible topologies. It discusses routing challenges in MANETs due to the dynamic topology. It also summarizes several routing protocols used in MANETs like DSR, DSDV, CGSR, ABR and SSR, which aim to establish and maintain routes between nodes that are moving. Finally, it discusses security and performance issues in MANETs and proposes the dynamic virtual backbone approach to abstract node mobility.
This document provides an overview of optimizing IP for IoT networks. It discusses how IP can be adapted or adopted for devices. It also describes constraints of IoT nodes and networks and how IP is optimized through protocols like 6LoWPAN, 6TiSCH, and RPL. It covers adaptation layers, packet headers, forwarding methods, and scheduling in these protocols. Authentication, application protocols like MQTT and CoAP, and the work of IETF groups on standards for IoT are also summarized.
This document discusses indoor positioning technologies as an alternative to GPS which does not work well indoors. It outlines various positioning methods like lateration, angulation, and fingerprinting that can be used. It then surveys existing indoor positioning systems that use technologies like WiFi, Bluetooth, UWB, and inertial sensors. Specific solutions for indoor positioning on smartphones using only ambient WiFi signals and mobile sensors are also presented, such as WiFiSlam and Qualcomm's approach, which can achieve 2 to 2.5 meter accuracy.
PPT on Bluetooth Based Wireless Sensor NetworksSiya Agarwal
Bluetooth wireless sensor networks can be implemented using Bluetooth technology. Smart sensor nodes equipped with sensors, microprocessors and Bluetooth communication interface can collect data and transmit it to a gateway node. The network involves discovering Bluetooth devices, establishing connections and exchanging data. Algorithms are used for initialization, discovery, parameter setting and data transfer between nodes. While Bluetooth provides benefits like being wireless and inexpensive, it also has limitations such as average data rates and security risks.
HiperLAN was developed as a wireless local area network standard by ETSI to provide higher data rates than early 802.11 standards. HiperLAN Type 1 achieved data rates up to 2 Mbps for ad hoc networking. HiperLAN Type 2 was later developed to provide connection-oriented service up to 54 Mbps, with quality of service guarantees, security, and flexibility. It uses OFDM in the 5 GHz spectrum for robust transmission. While early products only achieved 25 Mbps, the standard provides a framework for higher speeds as technologies advance. HiperLAN is intended to complement wired networks by providing wireless connectivity in hotspot areas like offices, homes, and public places.
This document discusses routing issues in vehicular ad hoc networks (VANETs). It begins by introducing VANETs and their use for safety, comfort and entertainment applications. It then examines traditional mobile ad hoc network routing protocols and their problems when applied to VANETs due to high mobility. Several position-based routing protocols designed specifically for VANETs are described, including Greedy Perimeter Stateless Routing (GPSR) and Anchor-based Street and Traffic Aware Routing (A-STAR). The document concludes that position-based protocols show more promise than traditional ad hoc routing for VANETs and future work is still needed to provide reliable quality of service.
This document discusses the development of an Android application for physical activity recognition using the accelerometer sensor. It provides background on the Android operating system and its open development environment. It then summarizes relevant research papers on activity recognition using mobile sensors. The document outlines the process of collecting and labeling accelerometer data from smartphone sensors during different physical activities. Features are extracted from the sensor data and several machine learning classifiers are evaluated for activity recognition. The application will recognize activities and track metrics like calories burned, distance traveled, and implement fall detection and medical reminders.
The document provides an overview of an IoT reference architecture, describing its key views and functional groups. The functional view breaks the system into functional components including device and application functions, communication functions, IoT services, virtual entities, process management, service organization, security, and management. Each functional group contains functional components that address things like sensing, actuation, networking, service discovery, composition and orchestration, identity, authentication, authorization, and system administration. The views help address the concerns of different stakeholders and reduce complexity by focusing on specific areas.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
IRJET- Survey Paper on Human Following RobotIRJET Journal
The document summarizes research on developing an autonomous human following robot. It discusses using triangulation of radio signals from a tag worn by a human to calculate the tag's location using multiple antennas on the robot. The robot would use triangulation and received signal strength to determine the tag's position and direction to follow the human. It reviews several localization algorithms and navigation techniques used in other projects. The proposed method is to use triangulation of signals from three antennas to accurately calculate the tag's position and allow the robot to autonomously follow or be remotely controlled via Bluetooth.
The document discusses an indoor navigation system that uses WiFi positioning to determine a user's location inside a building and provide routing directions. It covers several key aspects:
1. An overview of various indoor positioning technologies including GPS, cellular, infrared, UWB, Bluetooth, and WiFi. WiFi is identified as the preferred approach due to widespread availability and low cost.
2. Details on how WiFi positioning works, including techniques like triangulation, fingerprinting, and considerations of RSSI, MAC addresses, and network details.
3. The system's objectives of indoor positioning, routing, and tracking. It also outlines implementation in models like universities and shopping malls.
4. Additional technical components
The document proposes an indoor navigation system for malls using Wi-Fi signal strength. It would determine a user's position by calculating the intersection point of signal strengths from three access points. The system would find the shortest path between a user's location and destination using Dijkstra's algorithm on a graph of the mall's floorplan. The project aims to help users navigate inside large indoor spaces like malls and offices.
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
AIoT(Artificial intelligence of things) is a relatively new term and has recently become a hot topic which combines two of the hottest acronyms AI( Artificial intelligence) and Internet of things (IoT)
This document discusses 3D passwords as a new authentication technique that combines existing methods like text passwords, graphical passwords, and biometrics into a single 3D virtual environment. The user interacts with various objects in the virtual world to create their unique 3D password. When logging in, they must recreate the same sequence of interactions. This makes 3D passwords more secure by increasing the number of possible passwords and making them difficult for attackers to guess. The document outlines how a 3D password system would work, including designing the virtual environment, recording the user's interactions as their password, and guidelines for the virtual world design like real-life similarity, unique distinguishable objects, and appropriate size.
The document provides an introduction to IoT including definitions, characteristics, genesis, applications and challenges. It describes the physical design of IoT including IoT devices, protocols, and the generic block diagram of an IoT device. It also describes the logical design including IoT functional blocks, communication models like publish-subscribe, request-response, levels of IoT deployment from level 1 to 6, and enabling technologies.
The document discusses middleware for indoor location-based services (LBSs). It provides an overview of key enabling technologies for indoor LBSs like indoor positioning, mapping, geocoding, geofencing, and routing. It also describes reference architectures and available open-source frameworks like the i-locate toolkit that can be used to build indoor LBS applications by reusing common functionalities and avoiding re-inventing solutions. Specifically, the i-locate toolkit enables self-navigation applications and asset tracking applications using indoor maps and positioning.
Bringing ArcGIS spatial analysis to bear on IoT dataEsri UK
IoT is changing the way we manage key systems and plan our future, generating ever more digital data. This session explores the new capabilities in ArcGIS that are making it easier to apply the power of spatial analysis to IoT data. Whether you need to ask questions of real time feeds or draw insight from ever larger big data sets, there are tools to help.
This document discusses location-based services (LBS) and evaluates different positioning techniques used in LBS. It begins by introducing common LBS applications and services. It then examines the components and architecture of LBS systems, including LBS middleware and location tracking. Privacy concerns with LBS are also addressed. The document evaluates and compares several positioning systems used in LBS, including satellite-based GPS, network-based methods like GSM, and indoor positioning techniques. It concludes by discussing limitations and opportunities for future work improving LBS positioning accuracy and privacy.
A mobile ad hoc network (MANET) consists of devices connected wirelessly without infrastructure. Each device is free to move and change connections, making the network topology highly dynamic. Devices must forward unrelated traffic, acting as routers. The primary challenge is maintaining routing information as the topology changes frequently. MANETs can connect to the internet or operate independently, using radio frequencies for short-range communication between peers. Examples include networks between vehicles or smartphones without cellular access.
This document summarizes a seminar presentation on Mobile Ad-Hoc Networks (MANETs). It introduces MANETs as networks without infrastructure where nodes can connect in dynamic and flexible topologies. It discusses routing challenges in MANETs due to the dynamic topology. It also summarizes several routing protocols used in MANETs like DSR, DSDV, CGSR, ABR and SSR, which aim to establish and maintain routes between nodes that are moving. Finally, it discusses security and performance issues in MANETs and proposes the dynamic virtual backbone approach to abstract node mobility.
This document provides an overview of optimizing IP for IoT networks. It discusses how IP can be adapted or adopted for devices. It also describes constraints of IoT nodes and networks and how IP is optimized through protocols like 6LoWPAN, 6TiSCH, and RPL. It covers adaptation layers, packet headers, forwarding methods, and scheduling in these protocols. Authentication, application protocols like MQTT and CoAP, and the work of IETF groups on standards for IoT are also summarized.
This document discusses indoor positioning technologies as an alternative to GPS which does not work well indoors. It outlines various positioning methods like lateration, angulation, and fingerprinting that can be used. It then surveys existing indoor positioning systems that use technologies like WiFi, Bluetooth, UWB, and inertial sensors. Specific solutions for indoor positioning on smartphones using only ambient WiFi signals and mobile sensors are also presented, such as WiFiSlam and Qualcomm's approach, which can achieve 2 to 2.5 meter accuracy.
PPT on Bluetooth Based Wireless Sensor NetworksSiya Agarwal
Bluetooth wireless sensor networks can be implemented using Bluetooth technology. Smart sensor nodes equipped with sensors, microprocessors and Bluetooth communication interface can collect data and transmit it to a gateway node. The network involves discovering Bluetooth devices, establishing connections and exchanging data. Algorithms are used for initialization, discovery, parameter setting and data transfer between nodes. While Bluetooth provides benefits like being wireless and inexpensive, it also has limitations such as average data rates and security risks.
HiperLAN was developed as a wireless local area network standard by ETSI to provide higher data rates than early 802.11 standards. HiperLAN Type 1 achieved data rates up to 2 Mbps for ad hoc networking. HiperLAN Type 2 was later developed to provide connection-oriented service up to 54 Mbps, with quality of service guarantees, security, and flexibility. It uses OFDM in the 5 GHz spectrum for robust transmission. While early products only achieved 25 Mbps, the standard provides a framework for higher speeds as technologies advance. HiperLAN is intended to complement wired networks by providing wireless connectivity in hotspot areas like offices, homes, and public places.
This document discusses routing issues in vehicular ad hoc networks (VANETs). It begins by introducing VANETs and their use for safety, comfort and entertainment applications. It then examines traditional mobile ad hoc network routing protocols and their problems when applied to VANETs due to high mobility. Several position-based routing protocols designed specifically for VANETs are described, including Greedy Perimeter Stateless Routing (GPSR) and Anchor-based Street and Traffic Aware Routing (A-STAR). The document concludes that position-based protocols show more promise than traditional ad hoc routing for VANETs and future work is still needed to provide reliable quality of service.
This document discusses the development of an Android application for physical activity recognition using the accelerometer sensor. It provides background on the Android operating system and its open development environment. It then summarizes relevant research papers on activity recognition using mobile sensors. The document outlines the process of collecting and labeling accelerometer data from smartphone sensors during different physical activities. Features are extracted from the sensor data and several machine learning classifiers are evaluated for activity recognition. The application will recognize activities and track metrics like calories burned, distance traveled, and implement fall detection and medical reminders.
The document provides an overview of an IoT reference architecture, describing its key views and functional groups. The functional view breaks the system into functional components including device and application functions, communication functions, IoT services, virtual entities, process management, service organization, security, and management. Each functional group contains functional components that address things like sensing, actuation, networking, service discovery, composition and orchestration, identity, authentication, authorization, and system administration. The views help address the concerns of different stakeholders and reduce complexity by focusing on specific areas.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
IRJET- Survey Paper on Human Following RobotIRJET Journal
The document summarizes research on developing an autonomous human following robot. It discusses using triangulation of radio signals from a tag worn by a human to calculate the tag's location using multiple antennas on the robot. The robot would use triangulation and received signal strength to determine the tag's position and direction to follow the human. It reviews several localization algorithms and navigation techniques used in other projects. The proposed method is to use triangulation of signals from three antennas to accurately calculate the tag's position and allow the robot to autonomously follow or be remotely controlled via Bluetooth.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The document describes the development of an autonomous drone integrated with artificial intelligence and object detection capabilities. The drone is designed to follow a person or vehicle and respond during emergencies. It uses a Raspberry Pi computer along with cameras, sensors and a flight controller. The methodology involves building the drone, programming AI algorithms for object detection and tracking, and testing the autonomous functions. The goal is to create a user-friendly drone that can assist during emergencies or be used for surveillance through features like face recognition and response to detected signals.
IRJET- Smart Accident Detection and Emergency Notification System using IoT a...IRJET Journal
This document describes a smart accident detection and emergency notification system using IoT and mobile computing. The system uses MEMS sensors to detect vehicle vibrations during an accident and ultrasonic sensors to calculate distance. It then sends accident location data like coordinates, time, and angle of impact to emergency contacts via GSM module. This is intended to reduce response time and save lives by quickly notifying authorities and loved ones after a crash when emergency services are lacking. The system architecture incorporates sensors, microcontroller, GPS, GSM and modules for user registration, location updating, and accident notification.
Localization of wireless sensor networkIRJET Journal
This document summarizes a range-free localization algorithm for wireless sensor networks called TSBMCL (Temporary-Seed Based Monte Carlo Localization) that uses the Monte Carlo method. It discusses how TSBMCL works in two main parts: 1) voting for temporary anchor nodes from localized nodes, and 2) using the temporary anchors to aid localization of other nodes. The algorithm is shown to improve localization accuracy over the MCB algorithm. Simulation results demonstrate that TSBMCL reduces localization failure rates and requires fewer sampling particles than standard Monte Carlo localization methods. In conclusion, TSBMCL provides an accurate and efficient range-free localization scheme for mobile wireless sensor networks.
The document describes a blind assistance system called Sanjaya that uses object detection and depth estimation to help visually impaired individuals navigate environments. The system uses a SSD MobileNet model trained on the COCO dataset via TensorFlow's object detection API to identify objects in camera images in real-time. It then uses depth estimation to calculate distances and provides voice feedback alerts to users about detected objects and their proximity. The system aims to allow visually impaired people to have improved comprehension of their surroundings and navigation abilities.
A genetic based indoor positioning algorithm using Wi-Fi received signal stre...IAESIJAI
The recent trend in location-based services has led to a proliferation of studies in indoor positioning technology. Wi-Fi received signal strength indicator (RSSI) Fingerprinting and pedestrian dead reckoning (PDR) are the two best representatives from both approaches. This research proposed a genetic algorithm to combine Wi-Fi Fingerprinting and PDR. By taking advantage of PDR and genetic algorithm, we only need to collect a limited number of points for the fingerprint dataset with known coordinates, then target trajectories' position can be estimated with high accuracy. Results from our experiments and simulations have shown that even in the scenario of noisy inertial measurement unit (IMU) sensors data, using RSSI measurements and the coordinate of 8 points, our proposed method was able to achieve 1.589 meters of average distance error which is 34.4 percent lower than the conventional Fingerprinting method.
Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighte...CSITiaesprime
In practical applications, accurate floor determination in multi-building/floor environments is particularly useful and plays an increasingly crucial role in the performance of location-based services. An accurate and robust building and floor detection can reduce the location search space and ameliorate the positioning and wayfinding accuracy. As an efficient solution, this paper proposes a floor identification method that exploits statistical properties of wireless access point propagated signals to exponent received signal strength (RSS) in the radio map. Then, using single-layer extreme learning machine-weighted autoencoder (ELM-WAE) main feature extraction and dimensional reduction is implemented. Finally, ELM based classifier is trained over a new feature space to determine floor level. For the efficiency evaluation of our proposed model, we utilized three different datasets captured in the real scenarios. The evaluation result shows that the proposed model can achieve state-of-art performance and improve the accuracy of floor detection compared with multiple recent techniques. In this way, the floor level can be identified with 97.30%, 95.32%, and 96.39% on UJIIndoorLoc, Tampere, and UTSIndoorLoc datasets, respectively.
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document describes a method for indoor localization using a smartphone and Bluetooth Low Energy (BLE) sensor tag. The sensor tag contains an accelerometer, gyroscope, and other sensors to measure a user's motion and transmit the data via BLE to a smartphone app. The app uses dead reckoning algorithms integrating accelerometer and gyroscope data to calculate the user's distance and direction of movement over time without GPS or network connectivity. Challenges included increasing the sensor sampling rate and integrating data from multiple sensors. The described method provides indoor navigation when outdoor positioning systems like GPS are unavailable.
Indoor localisation and dead reckoning using Sensor Tag™ BLE.Abhishek Madav
The mobile application uses readings of the Accelerometer and Gyroscope from the Sensor Tag to describe details of motion in a planar mode. The project has been implemented as a part of the EECS 221 coursework at University of California, Irvine.
This document describes a method for indoor localization using a smartphone and Bluetooth Low Energy (BLE) sensor tag. The sensor tag contains an accelerometer, gyroscope, and other sensors to measure a user's motion and transmit the sensor data via BLE to a smartphone app. The app uses dead reckoning algorithms integrating accelerometer and gyroscope data to calculate the user's distance and direction of movement over time without GPS or network connectivity. Challenges addressed include increasing the sensor sampling rate and integrating accelerometer and gyroscope data to limit position error accumulation. The described system provides indoor navigation when outdoor positioning systems like GPS are unavailable.
With the rapid development of smartphone industry,
various positioning-enabled sensors such as GPS receivers,
accelerometers, gyroscopes, digital compasses, cameras, WiFi and Bluetooth have been built in smartphones for
communication, entertainment and location-based services.
Smartphone users can get their locations fixed according to
the function of GPS receiver.
Real-time human activity recognition from smart phone using linear support ve...TELKOMNIKA JOURNAL
The recognition of human activity (HAR) the use of cell devices embedded in its exten sively disbursed sensors affords guidance, instructions, and take care of citizens of smart cities. Consequently, it became essential to analyze human every day sports. To examine statistical models of human conduct, synthetic intelligence strategies such as machine studying can be used. Many studies have not studied type overall performance in real-time due to statistics series. To remedy this trouble, this paper proposes a structure primarily based on open supply technology and platforms consisting of Apache Kafka, for messages to flow over the internet, method them and provide shape for existing facts in real-time and formulates the trouble of identifying human pastime by using a smartphone tool as a type hassle using statistics collection by telephone sensors. The proposed version is skilled by some machine learning algorithms. The algorithm that has proven superior and quality results helps a linear vector machines.
F B ASED T ALKING S IGNAGE FOR B LIND N AVIGATIONIJCI JOURNAL
The major challenge of visually impaired person is
in mobility, object identification and identificati
on of
space around him/her. The proposed RF Based Talking
Signage for Blind Navigation aims to provide a
universal electronic travel guide for the visually
challenged people. This system incorporates a user
friendly and versatile method called “Talking Signa
ge” that is implemented using android devices. The
system uses an Android application in the mobile ph
one which could deliver voice messages about the us
er
environment via a heterogeneous network. It can be
deployed in any dense environment so that blind
persons can fulfill their needs. The primary advant
age of the system compared to other system in the a
rea is
low cost, ease of transport, less power consumption
, lightweight, and it could be utilized by those pe
oples
who are technically challenged. The architecture pr
oposed in this paper clearly shows communication
between a mobile phone and a heterogeneous network
enabled with RF devices. We have implemented the
system in our university environment and the propos
ed system found to be a great success
A CUSTOMIZED FLOCKING ALGORITHM FOR SWARMS OF SENSORS TRACKING A SWARM OF TAR...cscpconf
Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multimodal sensing capabilities that communicate over wireless networks. WMSNs have more
flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental
monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of
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higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more traightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in
a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a
customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency.
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In recent years, majority of researches are focused on localization system for wireless environment. These researches rely on localization using devices to track the entities. In this paper, we use, a recently proposed Device-free Passive (DfP) that uses Probabilistic techniques to track locations in large-scale real environment without the need of carrying devices. The proposed system uses the Access Points (APs) and Monitoring Point (MPs) that works by monitoring and processing the changes in the received physical signals at one or more monitoring points to detect changes in the environment. The system uses continuous space estimator to return multiple location while the mortal is in motion. Our results show that the system can achieve very high probability of detection and tracking with very few false positives.
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The document proposes a framework that uses intelligent mobile devices to enable indoor wireless location tracking, navigation, and mobile augmented reality (AR). It discusses using mobile devices equipped with inertial measurement units (IMU) and multi-touch screens to provide user feedback to correct positioning errors. The framework also uses mobile AR through device cameras to help navigate users in complex 3D indoor environments and provide interactive location-based services. A prototype system was developed to demonstrate the feasibility of the proposed application framework.
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
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objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
3. Introduction
Smartphones contains a GPS unit, allow applications such as geo-fencing and
automotive navigation.
Lack of GPS signal reception in indoor environments.
Inertial Pedestrian Dead-Reckoning (PDR) used for indoor navigation on
smartphones.
Introducing an implementation for PDR algorithm using Quaternion-Based
Extended Kalman filter “EKF” for heading estimation with Low Pass filter and
adaptive step length methodology.
Our approach shows a remarkable decreases of the error, the recorded average
error is 0.16 meter with percentage 0.07% of the 210 meter total traveled.
4. Introduction - Positioning
Determine the position of an object.
Four different categories of positioning methods are typically used
for positioning systems.
◦ Radio-based Positioning Systems (GSM).
◦ Global Positioning System (GPS) (Most accurate).
◦ Wide-area / Local-area Systems (Wi-Fi).
◦ Near-field Systems (BLE).
These are based on the cell of origin, distances, angles, or pattern
recognition.
5. Introduction – Indoor Positioning
Techniques
Location Fingerprinting
◦ Record the occurrence of different
radio- signals in a specific location,
and measure the signal-strengths
using a mobile device, as physical
locations in the world have unique
radio signatures.
Triangulation/Trilateration
◦ Lengths between the wireless
device and each of the Access
Points (APs) the device is
connected to.
Proximity
◦ Simplest techniques, where the
location of a wireless device is
estimated to be the same as the
location of the AP it is currently
connected to.
6. Introduction – Other Indoor Positioning
Systems
Dead reckoning (DR) is the process of estimating the current
position of a user based upon a previously known position.
DR systems estimate relative displacement by step detection
and user heading estimation.
Used in Marine, Air, Automotive and Autonomous navigation
in robotics.
Pedestrian dead reckoning (PDR) using smartphone built-in
accelerometers can be used as a pedometer and built-in
magnetometer as a compass heading provider.
7. Kalman Filter
Kalman filter exists for the past 50 years.
It was first introduced by Rudolf Emil Kalman in 1960
and was implemented on the Apollo Project in 1961.
It's a method of predicting the future state of a
system based on the previous ones.
Consists of two distinct processes, the prediction
process and the measurement process.
Operated in a recursive manner to achieve optimal
Kalman filtering process.
8. Quaternion-based Extended Kalman
Filter
A complex number of the form 𝑤 + 𝑥𝑖 + 𝑦𝑗 + 𝑧𝑘, where w, x, y, z are real
numbers and i, j, k are imaginary units that satisfy certain conditions.
Quaternion-based EKF is used for determining orientation using 9-degree
of freedom “DOF”:
◦ 3-axis angular velocity
◦ 3-axis acceleration
◦ 3-axis magnetic field sensors.
10 states :
◦ 4-D quaternions.
◦ 3-D acceleration bias.
◦ 3-D magnetic field bias were modeled.
9. Quaternion-based EKF – Cont.
θ, ϕ, ψ represent pitch, roll and yaw angle respectively according to the Euler
angles definition.
Quaternion q is defined by:
𝑞 = 𝑞0 𝑞1 𝑞2 𝑞3 = cos
𝛼
2
𝑟𝑥 sin
𝛼
2
𝑟𝑦 sin
𝛼
2
𝑟𝑧 sin
𝛼
2
(1)
A 3-D vector can be rotated by a quaternion 𝑞 using the following equation:
𝜐 𝑛 = 𝑞 ⊕ 𝜐 𝑏 ⊕ 𝑞∗ (2)
where 𝜐 𝑛 and 𝜐 𝑏 are described in n frame and b frame vectors
𝑞∗ is the conjugate of 𝑞 and is given by:
𝑞∗
= 𝑞0 − 𝑞1 − 𝑞2 − 𝑞3 (3)
10. Quaternion-based EKF – Cont.
Rather than a complex rotation equation described by Equation (2), a simpler rotation
relationship can be expressed as the Direction Cosine Matrix (DCM) in terms of the quaternion
𝑞:
𝐶 𝑏
𝑛
=
𝑞0
2
+ 𝑞1
2
− 𝑞2
2
− 𝑞3
2
2 𝑞1 𝑞2 − 𝑞0 𝑞3 2 𝑞1 𝑞3 − 𝑞0 𝑞2
2 𝑞1 𝑞2 − 𝑞0 𝑞3 𝑞0
2
− 𝑞1
2
+ 𝑞2
2
− 𝑞3
2
2 𝑞2 𝑞3 − 𝑞0 𝑞1
2 𝑞1 𝑞3 − 𝑞0 𝑞2 2 𝑞2 𝑞3 − 𝑞0 𝑞1 𝑞0
2
− 𝑞1
2
− 𝑞2
2
+ 𝑞3
2
(4)
According to the Z-Y-X aerospace sequence, Euler angles 𝜃, ϕ and ψ can be written as the
following identity:
𝜃
∅
𝜓
=
𝑎𝑡𝑎𝑛2 2𝑞2 𝑞3 + 2𝑞0 𝑞1, 𝑞3
2
− 𝑞2
2
− 𝑞1
2
− 𝑞0
2
−asin (2𝑞2 𝑞3 − 2𝑞0 𝑞2)
𝑎𝑡𝑎𝑛2 2𝑞1 𝑞2 + 2𝑞0 𝑞3, 𝑞1
2
− 𝑞0
2
− 𝑞3
2
− 𝑞2
2
(5)
11. Quaternion-based EKF – Cont.
The relationship between the quaternion derivative 𝑞 and the angular velocity 𝜔
are described by the following well known equation:
𝑞 =
1
2
× 𝑞 ⊗ 𝜔 (6)
where ⊗ represents quaternion multiplication.
12. Internet of Things (IoT)
Things are objects of the physical world or
of the information world (virtual).
Things are capable of being identified and
integrated into communication layer.
Physical things: surrounding environment,
sensors, electrical equipment, etc.
Virtual things are capable of being stored,
processed and accessed: multimedia
content.
13. Internet of Things (IoT) – Cont.
Definition : A global infrastructure for the information society,
enabling advanced services, by interconnecting (physical and virtual)
things based on existing and evolving interoperable information and
communication technologies.
The basic concept of IoT is to connect things together, thus enabling
these “things” to communicate with each other and enabling people
to communicate with them.
14. IoT Fundamental characteristics
Interconnectivity
◦ IoT devices are integrated into the information network.
◦ can be dynamically discovered in the network
◦ Have the capability to describe themselves
Heterogeneity
◦ based on different hardware and networks
◦ They have to interact with other devices through different networks
Dynamic changes & self adapting
◦ Take actions based on operating conditions.
◦ Sleeping/waking up
◦ Connected/disconnected
Enormous scale
◦ IoT devices are much bigger than the number of devices on Internet
◦ big data
◦ semantics of data
◦ data handling (Cloud?)
15. IoT Applications
Smart cities: more digitalized and intelligent cities.
Smart factory: IoT will provide automatic procedures.
Smart home.
Wearables.
Smart grids.
Connected car.
Personal health devices.
Smart retail.
Smart supply chain.
Smart farming.
16. Motivation
The market of indoor positioning applications will
worth $4.4 Billion by 2019.
More than 5.6 billion IoT devices in Q3 2016, and the
number is expected to increase to 18.1 billion by 2022.
Cisco estimates that the Internet of Things has a
potential value of $19 trillion over the next decade.
Wearable tech market to be worth $34 Billion by 2020.
People sometimes loose directions inside unfamiliar
and large buildings.
A quaternion-based extended Kalman filter (EKF)
algorithm has been shows a massive improve in
heading estimation with handheld IMUs in experiment
and theory.
17. Motivation – Cont.
Current systems rely on a wireless network which is not always available and
some buildings topologies are hard to get covers and very expensive ($349 /
50 square meter ).
Indoor Location Systems is tremendously necessary for many fields:
• The Military.
• Emergency Services (e.g. earthquakes, firefighting).
• Disaster Rescue.
• Tracking Of Doctors In Hospital.
• Tourism.
• Navigate passengers in airports.
• Help visually impaired people to navigate.
In December 2015, British government building a project to probe the
viability of using low power Bluetooth Beacon technology for indoor
navigation.
18. Problem Statement
Locating user or object in an indoor environment is a challenge task since the lack of GPS signal.
Smart phones and wearables devices today come with a vast types of sensors which can be used and
combined to locate the user in indoor environment.
Massive number of IoT device which we can use to enhance the accuracy for locating human in
indoor environment.
Reading of these sensors can be affected by a number of errors.
The output of sensors readings must go through some filtering algorithm.
The filtered output can be used in the user location estimation, to achieve this by applying a type of
Dead Reckoning Algorithms.
19. Problem Statement – Cont.
The firefighters are equipped with small
lightweight sensors that do not interfere
with their ability to do their job.
The positioning system works in real
time, positioning each user with meter
level accuracy and broadcasting the
information.
The information is presented to the
operational manager overlaid on an
informative map.
20. Related Work
Three groups of Indoor Positioning Systems (IPS) have been
introduced.
1. Fingerprint: signature of environment features and strongly dependent on
the physical location.
2. Beacons : installing infrastructures beacons in a building.
3. Smartphones and wearables with inertial sensors built-in: that make it
possible to deploy PDR systems in daily life tasks.
21. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/ Wei Chen, Ruizhi Chen,
Yuwei Chen, Heidi Kuusniemi,
Jianyu Wang
An Effective Pedestrian Dead
Reckoning Algorithm Using a
Unified Heading Error Model
Integrate GPS with self-
contained dead rocking sensors
to overcome the GPS signal
corruption or unavailability.
Below than 1.5% of the traveled
distance.
• Solution not compatible with
mobile phones.
• Highly dependent on GPS as
an input sensor.
2011/ Kamil Kloch, Paul
Lukowicz, Carl Fischer
Collaborative PDR Localization
with Mobile Phones
Ad-hoc collaboration between
devices to improve the PDR
algorithm.
Improve the indoor position in
78% of the cases
• It is mainly used to cover large
events.
• Depends on the collaboration
between devices, so all
devices in the same closed
area must have the same
system.
• Use GPS in the calculations.
22. Related Work Cont.
Year / Author Title Description Accuracy Challenges
Jochen Seitz, Jasper Jahn, Javier
Guti_errez Boronat, Thorsten
Vaupel, Steffen Meyer, Jorn
Thielecke
A Hidden Markov Model for
Urban Navigation Based on
Fingerprinting and Pedestrian
Dead Reckoning
Using PDR algorithm with WiFi
fingerprint to overcome the
unviability or WiFi outage.
Achieve high accuracy by
combining WiFi fingerprint with
Hidden Markov Model(HMM)
• Depend on Wi-Fi fingerprint
2012/ Azkario Rizky Pratama,
Widyawan, Risanuri Hidayat
Smartphone-based Pedestrian
Dead Reckoning as an Indoor
Positioning System
Using the PDR algorithm to
recognize the pattern of human
steps and apply high/low pass
filters
Average error of 2.925% or 1.39
meter of the traveled distance
• It is difficult to detect
displacement.
• Not using any algorithm to
enhance the results.
2015/ Chengxuan Liu, Ling Pei,
Jiuchao Qian, Lin Wang, Peilin Liu,
Wenxian Yu
Sequence-Based Motion
Recognition Assisted Pedestrian
Dead Reckoning Using a
Smartphone
Using a sequence-based motion
recognition method which
estimates the motion states from
a sequence of data, and deploys a
HMM (Hidden Markov Model) to
infer the state labels of sequence
motion.
0.30 m Mean positioning error. • Solution not compatible with
mobile phones.
23. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2011/Andre M. Cavalcante,
Edgar B. Souza, Juliano J.
Bazzo, Nibia Bezerra, Allan
Pontes and Robson D. Vieira
A Pedometer-Based System
for Real-time Indoor Tracking
on Mobile Devices
Using a simple pedometer
approach that uses data
collected from sensors
(accelerometers and
compass) that are built-in on
mobile devices to provide
indoor tracking.
Low to moderate accuracy Low accuracy due to
using low cost sensors
and mobiles (N97)
which presenting low
tolerance to noise and
electromagnetic
interference.
Did not use any filtering
and interference
reduction strategies.
24. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/Dominik Gusenbauer,
Carsten Isert and Jens
Krösche
Self-Contained Indoor
Positioning on Off-The-Shelf
Mobile Devices.
Using a combination of GPS,
where available, with
Pedestrian Dead Reckoning
(PDR) utilizing inertial
measurements and context-
aware activity based map
matching.
Approximately a single
parking spot (5 M).
Low accuracy due to
using of low cost
sensors and mobiles
(N97) which present low
tolerance to noise and
electromagnetic
interference.
Did not use any filtering
and interference
reduction strategies.
Used GPS as input
method to increase the
accuracy which is not
available all the time.
25. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2010/ Masakatsu Kourogi,
Tomoya Ishikawa, and Takeshi
Kurata
A Method of Pedestrian Dead
Reckoning Using Action
Recognition
Using a single low-cost Inertial
Measurement Unit (IMU)
mounted at the waist of the
user and using an action
recognition process to detect
the current action taken by the
user such as standing up
from/sitting down on a chair,
and bending over to slip
through obstacles.
Achieved 95% by cross
validation test on the training
data set, and error rate of the
PDR localization is 2% of the
walking distance.
Solution not compatible
with mobile phones.
Depends on many sensors
to collect data
(Accelerometers,
Gyroscope, Magnetometers,
Thermometers, Barometer)
which not all available in
mobile phones.
They achieve results by
assuming that the specified
actions (going up stairs,
going down stairs, take the
elevator … etc.) are likely to
take place at the fixed
positions.
26. Related Work Cont.
Year / Author Title Description Accuracy Challenges
2015/ Qinglin Tian, Zoran
Salcic, Kevin I-Kai Wang, Yun
Pan
An Enhanced Pedestrian Dead
Reckoning Approach for
Pedestrian Tracking using
Smartphones
Building a pedestrian tracking
using dead reckoning on a
standard smartphone.
Assuming the device is carried
in a defined way within the
tracking period, by identifying
three typical modes of
carrying the device during
walking and using that fact to
enhance tracking accuracy.
4.5% of the total traveled
distance.
Not using any map
matching techniques.
A very low accuracy.
27. Related Work – Cont.
A. R. Pratama, proposed a system with error rate of 2.925% for a 30 meters long
experiment which is about 0.6 meter error.
Y. Jin, proposed a system with error rate of 26.3% for a 90 meter long
experiment.
T. Qinglin, proposed a new approach of PDR with an error rate of 4.69% of the
total travel distance of 96.33 meter.
29. Proposed Framework Cont.
We used the quaternion-based EKF model.
Low Pass Filter “LPF” and mean filter used to smooth sensors
readings.
Inputs of the algorithm are accelerometer, magnetometer and
gyroscope readings.
Output is the current user heading in radians.
Mean filter is applied on magnetometer and gyroscope readings.
LPF is only applied on accelerometer readings.
30. Step-based Pedestrian Dead-reckoning
According to the PDR algorithm, the accelerometer, the magnetic sensor and the
gyroscope will be combined to locate the position of the smartphone based on
the following formula:
𝑋𝑖+1 = 𝑋𝑖 + 𝑆𝐿𝑖 × sin 𝛼𝑖
𝑌𝑖+1 = 𝑌𝑖 + 𝑆𝐿𝑖 × cos 𝛼𝑖
(7)
where (𝑋,𝑌) indicates the coordinates of the position, 𝑆𝐿𝑖is the step length and 𝛼
represents the heading angle
31. Step Detection
To detect a step, we employ a relative
threshold detection scheme .
This scheme detects a step when valid
maximum peak (as maxima) and valid
minimum peak (as minima) are detected in
sequence of a certain interval.
To ensure a valid step, an interval time
difference between maxima and minima is
also determined experimentally, must be
between 400ms – 1000ms.
9.2
9.4
9.6
9.8
10
10.2
10.4
10.6
41 42 43 44 45 46 47 48 49
PROJECTEDVERTICALACCELERATION(M/S2)
TIME (S)
32. Orientation Estimation
We used 6-D vectors (3-axis acceleration and 3-axis magnetic field), Quaternion-
Based EKF.
We used 7-D (4-D quaternions and 3-D gyroscope bias drift) vectors as state vectors.
In practice, the gyroscope bias drift specified by temperature is relatively small.
Accelerometer and magnetometer are usually utilized to eliminate the gyroscope
bias drift.
Therefore, 4-D state vectors are:
𝑥1
𝑥2
𝑥3
𝑥4
=
𝑞0
𝑞1
𝑞2
𝑞3
(8)
33. Orientation Estimation – Cont.
According to equation 6 , state equations can be expressed as:
𝑥1
𝑥2
𝑥3
𝑥4
=
1
2
𝑥1
𝑥2
𝑥3
𝑥4
⨂
0
𝜔 𝑥
𝜔 𝑦
𝜔𝑧
(9)
6-D measurement vectors can be directly obtained by the accelerometer and magnetometer
output:
𝑧1
𝑧2
𝑧3
𝑧4
𝑧5
𝑧6
=
𝑎 𝑥
𝑎 𝑦
𝑎 𝑧
𝑚 𝑥
𝑚 𝑦
𝑚 𝑧
(10)
34. Step Length Estimation
Static and dynamic technique.
Static technique assumes that any valid step that has the same length, which can be specified through the
following equation:
𝑠𝑡𝑒𝑝_𝑠𝑖𝑧𝑒 = ℎ𝑖𝑔ℎ𝑡 × 𝑘 (11)
with k equal to 0.415 for male and 0.413 for female.
We use the dynamic technique to estimate the step length ρk from acceleration measurements for the kth
step as:
𝜌 𝑘 = 𝐾 ×
4
𝑎 𝑘
𝑣−𝑚𝑎𝑥
− 𝑎 𝑘
𝑣−𝑚𝑖𝑛
(12)
where 𝑎 𝑘
𝑣−𝑚𝑎𝑥
and 𝑎 𝑘
𝑣−𝑚𝑖𝑛
are the maximum and minimum values of the projected vertical acceleration
during the kth step, respectively. The constant K is dependent on each pedestrian.
K=0.45 experimentally
35. Floor detection
Using:
𝐻 = −
𝑅.𝑇
𝑀 𝑎.𝑔
ln
𝑝 𝐻
𝑝 0
◦ where 𝑝 0 is pressure at Mean Sea Level (MSL), 𝑀 𝑎 is the molecular
weight of air, 𝑔 is the gravitational constant, 𝑅 is the universal gas
constant, and 𝑇 is the temperature in Kelvin
to determine the current user height
related to the sea level.
The first height reading is the first floor.
Every floor will have a fixed height of 2.7 to
3.25 meters.
36. Experiment Methods
compute the error rate by calculating the difference between the start and end point which is
GPS coordinates by Haversine formula as following:
𝑎 = sin
∆𝜑
2
2
+ cos 𝜑1 ∗ cos 𝜑2 ∗ sin
∆𝜆
2
2
𝑐 = 2 ∗ 𝑎 tan 𝑎, 1 − 𝑎
2
𝑑 = 𝑅 ∗ 𝑐
where 𝜑 is latitude, 𝜆 is longitude, R is earth’s radius (mean radius = 6,371km)
37. Experiment Equipment
One plus one
◦ LIS3DH Accelerometer, AK8963 Magnetometer
and L3GD20 Gyroscope.
◦ The device used in this experiment has Android
6.0.
◦ Compatible with any Android phone.
◦ with Accelerometer, Magnetometer and
Gyroscope sensors.
Samsung Gear S2
◦ Accelerometer, gyro, heart rate, barometer
sensors. It is running Tizen OS 2.4 for wearable.
38. Experiment Setup
Start point and end point with a
180°difference between them.
Total test distance is 10 meters.
Test subjects take varies number of steps,
between 15 to 20 steps.
The axis of the acceleration have a 90-degree
difference between the smartphone and the
gear S2.
Repeated two times, first without wearing a
smart watch and second try with a smart
watch.
39. Results : Orientation Estimation
Gyroscope and Magnetometer are fused together to provide a better estimation.
Applied Kalman filter, mean filter and low pass filter to smoothing the readings and remove any
noises
-1
-0.5
0
0.5
1
1.5
2
2.5
0
7
14
21
28
35
42
49
56
63
70
77
84
91
98
105
112
119
126
133
140
147
154
161
168
175
182
189
196
203
Heading Axis Without Filters
Azimuth Pitch Roll
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0
7
14
21
28
35
42
49
56
63
70
77
84
91
98
105
112
119
126
133
140
147
154
161
168
175
182
189
196
203
Heading Axis With Filters
Azimuth Pitch Roll
40. Results : Step Detection and Length
Estimation
To evaluate the step detection and it is length a distance of approximately 10 meters was walked
21 times.
Step detection Mean error 2.1 step with variance of 1.66 step.
Step length mean error 0.168 meter with variance 0.002203 meter.
0
5
10
15
20
25
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
Actual Vs. Detected Steps
Actual steps Detected steps
0
0.2
0.4
0.6
0.8
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Actual Vs. Recorded Stesp Length
actual steps length recorded steps length
41. RESULTS : Watch to Smartphone
connection
Using HTML5 WebSocket (peer to peer) since the Samsung Gear S2
have a full HTML5 API implementation.
data structure as the following:
◦ < accelerometer data>; <gyroscope data>; <barometer data>; <time
stamp>
A sample of data sent from the watch to smartphone:
◦ -0.8757730722427368;6.183053970336914;7.331608772277832;-
1.8200000524520874;1.190000057220459;0.9800000190734863;1012.
55;1477159557796
Compared to the Samsung SDK to communicate the Gear S2 with
Smartphone :
◦ Samsung rate is 24 readings per second
◦ The proposed solution is 41 readings per second
42. Pedestrian Tracking Results – First
Scenario
7 subjects each one repeated the test 3 times,
different in gender and height.
Total travel distance per subject is 30 meter.
Total distance traveled by all subjects is 210
meters.
Without wearing the smartwatch.
The average error is 0.3 meter with percentage
0.14% of the total traveled distance.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AverageErrorinMeter
Subject
Average error over all test subjects
43. Pedestrian Tracking Results – Second
Scenario
Same test conditions on the same subjects.
Wearing the smartwatch.
The average error is 0.16 meter with percentage
0.07% of the total traveled distance.
An improvement of 46% by wearing the
smartwatch.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
AverageErrorinMeter
Subject
Average error over all test subjects
44. EXPERIMENT AND RESULTS
Compared to the other techniques, our proposed algorithm results
an improve by :
◦ 40.16% compared to Y. Jin.
◦ 30.6% compared to A. R. Pratam.
◦ 14.2% compared to T. Qinglin.
Shows that our technique is a promising one.
45. FUTURE WORK
Test the proposed algorithm in much more
wearable devices:
◦ Samsung Gear S3 which include a GPS sensor by
default, this will helps in the heading estimation.
◦ Fitbit surge which includes GPS, 3-axis
accelerometers, 3-axis gyroscope, digital compass
and altimeter.
◦ GARMIN fēnix® 5 which include GPS, GLONASS,
Barometric altimeter, Compass, Gyroscope,
Accelerometer and Thermometer sensors.
46. FUTURE WORK – Cont.
Combining the proposed algorithm with other indoor navigation techniques :
◦ Wi-Fi RSSI fingerprinting
◦ ZigBee
◦ RFID
◦ Camera technique which is very promising (Google Visual Positioning
Service “VPS”)
Testing the proposed algorithm on the autonomous drone to facilitate and
add the capability for indoor navigation to theses drones.
Testing the proposed algorithm on self-driving cars to allow these cars
navigate in indoor environments when the there is no GPS signal.
Tuning of algorithm parameters.
Sensitivity analysis.
More experiments.
47. Published Papers
Title A new Kalman filter-based algorithm to
improve the indoor positioning
Authors Mohamed Nabil, M. B. Abdelhalim, Ashraf
AbdelRaouf
Publisher IEEE
Conference Multimedia Computing and Systems (ICMCS),
2016 5th International Conference on
Date of
Conference
29 Sept - 1 Oct 2016
Conference
Location
Marrakech, Morocco
DOI 10.1109/ICMCS.2016.7905588
Link http://ieeexplore.ieee.org/document/7905588/
Title Enhancing indoor localization using IoT techniques
(Submitted)
Authors Mohamed Nabil, M. B. Abdelhalim, Ashraf
AbdelRaouf
Publisher Springer
Conference The 3rd International conference on Advanced
Intelligent systems and Informatics 2017
Date of
Conference
15 Sept. 2017
Conference
Location
Cairo, Egypt
Inertial Pedestrian Dead-Reckoning (PDR) methods which depend on the inertial sensors and not depend on any network or any infrastructure such as GPS or fixed indoor location systems to achieving indoor navigation on smartphones.
With the increased sensor offering in smartphones, built-in accelerometers can be used as a pedometer and built-in magnetometer as a compass heading provider. Pedestrian dead reckoning (PDR).
If all noise is Gaussian, the Kalman filter minimizes the mean square error of the estimated parameters.
Was implemented on the Apollo Project in 1961 to solve the space navigation problem (especially in spacecraft attitude estimation and estimate the engine heat).
Why is Kalman Filtering so popular?
· Good results in practice due to optimality and structure.
· Convenient form for online real time processing.
· Easy to formulate and implement given a basic understanding.
· Measurement equations need not be inverted.
Why use the word “Filter”?
The process of finding the “best estimate” from noisy data amounts to “filtering out” the noise.
However a Kalman filter also doesn’t just clean up the data measurements, but also projects these measurements onto the state estimate.
The Figure
if we were given the data in blue, it may be reasonable to predict that the green dot should follow, by simply extrapolating the linear trend from the few previous samples. However, how confident would you be predicting the dark red point on the right using that method? how confident would you about predicting the green point, if you were given the red series instead of the blue?
Orientation can be defined as a set of parameters that relates the angular position of a frame to another reference frame.
For better estimating the gyroscope bias drifts without increasing the dimensions of state vectors.
The measurement model was linearized by calculating the Jacobian matrix.
Extended Kalman Filter is used for non linear system but it estimates actual state not error in the system
Extended Kalman Filter (EKF), as a kind of famous optimal estimation methods, have been applied in many fields, especially in spacecraft attitude estimation . Sabatini, proposed a standard quaternion-based EKF for determining orientation using 9-degree of freedom “DOF” (3-axis angular velocity, 3-axis acceleration, and 3-axis magnetic field) sensors. 10 states (4-D quaternions, 3-D acceleration bias, and 3-D magnetic field bias) were modeled. The measurement model was linearized by computing the Jacobian matrix. For better estimating the gyro bias drifts without increasing the dimensions of state vectors, the gyro was corrected by cubic polynomial temperature curve and a quaternion-based EKF was presented for AHRS. Recently, some papers focus on dealing with the external acceleration or magnetic disturbance, which would disturb the orientation estimation significantly. However, it is noted that the standard EKF must linearize the process models and/or measurement models, which would inevitably induce linearization errors into Kalman filter. Moreover, it is a large computational load for microcontrollers to compute Jacobian matrix.
Navigation frame (n frame) is organized by East-North-Up (ENU) definition
Let quadrotor right side as body frame (b frame) xb positive direction, forward as body frame yb positive direction and straight up as body frame zb positive direction
It is seen that the quaternion q can be obtained by integrating the quaternion derivative 𝑞 with fixed sampling time.
But the quaternion 𝑞 generated by the integrator may not be a unit quaternion, thus it is necessary to normalize the after effect quaternion 𝑞 in the last step of attitude update procedure.
IoT devices are integrated into the information network. They :
• can be dynamically discovered in the network
• Have the capability to describe themselves
Take actions based on operating conditions. The state of the device change dynamically:
• Sleeping/waking up
• Connected/disconnected
• Surveillance cameras
• Normal or infra red
• High or low resolution (in case of motion)
• Alert nearby cameras to do the same
During the past few years, more improvements have been achieved in outdoor Location Based Systems “LBS” than in indoor.
Indoor Location Based Systems “LBSs” becomes as popular as outdoor LBSs.
Indoor Location Market worth $4,424.1 Million by 2019
http://www.marketsandmarkets.com/PressReleases/indoor-location.asp
Internet of Things forecast
https://www.ericsson.com/mobility-report/internet-of-things-forecast
The Internet of Everything—A $19 Trillion Opportunity
https://www.cisco.com/c/dam/en_us/services/portfolio/consulting-services/documents/consulting-services-capturing-ioe-value-aag.pdf
Wearable Tech Market To Be Worth $34 Billion By 2020
https://www.forbes.com/sites/paullamkin/2016/02/17/wearable-tech-market-to-be-worth-34-billion-by-2020/
http://www.robotshop.com/en/indoor-navigation-positioning-system-433mhz-kit.html
Cos to cover City Stars = 150000 / 50 = 300 * 349 = $104700
Fingerprint: which means a signature of environment features and strongly dependent on the physical location.
Examples include Wi-Fi Received Signal Strength Indicator (RSSI) from all Access Points (AP) in the building.
Beacons : installing extra infrastructures beacons in a building, users carry receivers of the signals sent by beacons to form fingerprints.
Smartphones with inertial sensors built-in: that make it possible to deploy PDR systems in daily life tasks.
The step vector consists of each person step with its length. The trajectory is then created incrementally by adding each new Step Vector to the previous one.
The form of the step vectors is
𝑠𝑡𝑒𝑝 𝑙𝑒𝑛𝑔𝑡ℎ, 𝑠𝑡𝑒𝑝 ℎ𝑒𝑎𝑑𝑖𝑛𝑔
The PDR takes the step vector as an input and the initial position or a previous position and update the location of smartphone by defining a new (X, Y) coordinates.
Maxima is a maximum peak that exceeds upper threshold, while minima is a minimum peak that is below the lower threshold.
Error quaternions were used in reduced order Gauss-Newton method in to reduce the dimensions of state vectors
0 𝜔 𝑥 𝜔 𝑦 𝜔 𝑧 are the angular rate measurements from a 3-axis gyroscope and q is the quaternion of representation of quaternion.
Generally, there are two techniques for estimating step length: static method and dynamic technique.
where 𝑎 𝑘 𝑣−𝑚𝑎𝑥 and 𝑎 𝑘 𝑣−𝑚𝑖𝑛 are the maximum and minimum values of the projected vertical acceleration during the kth step, respectively. The constant K is dependent on each pedestrian, which can be determined through calibrations.
Our experiment shows a low error value by using the dynamic technique with K=0.45, this parameter is obtained by empirical experiments of all subjects for all cases.