An efficient indoor location tracking and navigation system using simple magnetic map matching

Binu, P K; Krishnan, Rahul; Kumar, A Pranav

doi:10.1109/iccic.2016.7919537

Cited by 23 publications

(9 citation statements)

References 5 publications

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“…So, each created image must be separated into two images, one of which contains the odd paths and the other contains the even paths for easy handling and clarification. Thus Figure 7 reported the comparison between the magnetic field images that were created in the test area, while Figure 7(a) the odd images of the center that contain paths (1,3,5) and their RGB histograms, Figure 7(b) the even image of the center that contain paths (2,4,6) and their RGB histograms, Figure 7(c) the odd image of the left that contain paths (7,9,11) and their RGB histograms, Figure 7(d) the even image of the left that contain paths (8,10,12) and their RGB histograms, Figure 7(e) the odd image of the right that contain paths (13,15) and their RGB histograms and Figure 7(f) the even image of the right that contain paths (14,16) and their RGB histograms.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the tracking algorithm is consisting of smartphone inertial sensors IMU given the possibility of obtaining magnetic field measurements at a much higher sample frequency with respect to Wi-Fi RSS on android phones and based on the pedestrian dead reckoning PDR approach integrated with external information, such as Wi-Fi and magnetic field fingerprinting, in order to offset the drift of assessment derived from the inertial sensors [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Indoor positioning system based on magnetic fingerprinting-images

et al. 2021

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In latest years, indoor positioning techniques have gained much attention, because of the absence GPS signal, so this paper shows a low priced mobile mapping system through using the advantages of integrating inertial navigation with smartphone sensors information concerned to a previous training phase with a magnetic map properly computed to more accurate positioning. Thus, areal online data sets were compiled through the use of ultra wide band to furnish an accurate positioning on the whole area of test and compute a trajectory used as a reference. Then, the use of the pedestrian dead reckoning based approach and IMU help to supply external information from the Wi-Fi signal that is used to exploit the received signal strength path loss, which is possibly used to assess the space between the device and access points. Furthermore, these real online data sets have been processed using Matlab to illustrate the different paths of the area of test. Also, using all RSS for every path line, different images were created. Finally, the positioning efficiency that is possible to be realized using information from IMU and UWB accelerated the fingerprinting training phase. So, the graphical analysis is used to summarize the results that match the closest path to the true path using mutual information.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor positioning system based on magnetic fingerprinting-images

et al. 2021

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“…Advances in GPS technology, Bluetooth, Wi-Fi, Radio Frequency Identification (RFID) enabled indoor employee tracking but many are considered costly. Android-based indoor tracking solution using magnetic map matching was implemented by [36] whilst [37] tested the magnet-based tracking on a robot. These solutions are computationally inexpensive with several limitations as they require predefined walking routes.…”

Section: B Piloting the Btla-framework For Anomaly Detectionmentioning

confidence: 99%

Federated Blockchain-Based Tracking and Liability Attribution Framework for Employees and Cyber-Physical Objects in a Smart Workplace

Ahmadi-Assalemi

Al-Khateeb

Epiphaniou

et al. 2019

2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3)

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The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Securityby-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations.

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“…There are several indoor localization strategies [31,32,33,34,35,36]. One method that deserves attention is Wi-Fi since indoor sites usually have it as part of a network infrastructure to provide Internet access.…”

Section: Prototype Applicationmentioning

confidence: 99%

A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications

Araújo

Santos

Lima

2019

Sensors

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The adaptability between different environments remains a challenge for Mobile Augmented Reality (MAR). If not done seamlessly, such transitions may cause discontinuities in navigation, consequently disorienting users and undermining the acceptance of this technology. The transition between environments is hard because there are currently no localization techniques that work well in any place: sensor-based applications can be harmed by obstacles that hamper sensor communication (e.g., GPS) and by infrastructure limitations (e.g., Wi-Fi), and image-based applications can be affected by lighting conditions that impair computer vision techniques. Hence, this paper presents an adaptive model to perform transitions between different types of environments for MAR applications. The model has a hybrid approach, choosing the best combination of long-range sensors, short-range sensors, and computer vision techniques to perform fluid transitions between environments that mitigate problems in location, orientation, and registration. To assess the model, we developed a MAR application and conducted a navigation test with volunteers to validate transitions between outdoor and indoor environments, followed by a short interview. The results show that the transitions were well succeeded, since the application self-adapted to the studied environments, seamlessly changing sensors when needed.

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An efficient indoor location tracking and navigation system using simple magnetic map matching

Cited by 23 publications

References 5 publications

Indoor positioning system based on magnetic fingerprinting-images

Indoor positioning system based on magnetic fingerprinting-images

Federated Blockchain-Based Tracking and Liability Attribution Framework for Employees and Cyber-Physical Objects in a Smart Workplace

A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications

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