Wearable indoor localisation approach in Internet of Things

Huang, Haojun; Zhou, Jiangguo; Li, Wei; Zhang, Juanbao; Zhang, Xu; Hou, Guolin

doi:10.1049/iet-net.2016.0007

Cited by 20 publications

(7 citation statements)

References 26 publications

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“…For IoHT devices, privacy requirements are more stringent than for typical IoT infrastructures. IoHT healthcare systems have various privacy requirements, such as data privacy protection [43]. Data privacy protection is a way to protect personal data from unauthorized use and manipulation.…”

Section: Motivation For Using Privacy-preserving Fl In Smart Healthcarementioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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Section: Motivation For Using Privacy-preserving Fl In Smart Healthcarementioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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“…Security and privacy requirements for the IoMT healthcare systems are more rigorous than that of the typical IoT-based infrastructures. IoMT healthcare systems have many additional security requirements, such as device localization [37], which can also contribute to ensure the security and privacy of the systems. The functionalities of each level of the IoMT healthcare systems are different, which means each level has different security and privacy requirements.…”

Section: Security and Privacy Requirements For Iomt Healthcare Systemsmentioning

confidence: 99%

Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey

2019

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With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user's privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.

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“…In binary classification, we compute the score of a typical linear SVM based on (5) and then apply a threshold to convert the score into binary 1 or 0 [36]. In this equation, Ĥ is the fingerprint being tested while Ĥ k is one of the support vector fingerprints determined after training the SVM model.…”

Section: Positioning Performance Of Trrs and Svm Under Various Enviromentioning

confidence: 99%

“…However, such sensors must be embedded onto the device and require filtering methods since they are sensitive to drift and cumulative error. For instance, a system called Wearable Indoor Localisation Approach uses gyroscopes or magnetometers placed on the waist of the person to count the number of steps and to estimate the length of each step [5]. After testing at different levels of moving speed, the distance estimation error is around 0.5 to 1 m. Imperfect step counting primarily causes this significant error.…”

Section: Introduction To Indoor Positioningmentioning

confidence: 99%

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

Rocamora

Mak

et al. 2020

IET signal process.

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Techniques for indoor positioning systems (IPSs) can be categorised as range-based or range-free. Range-based methods rely on geometric mappings to approximate a location given the calculated distances or angles from multiple reference points. In contrast, range-free strategies utilise fingerprinting, wherein an acquired fingerprint data is compared to a precollected dataset to identify the best position estimate. Among these, fingerprinting of channel state information (CSI) is preferred over other information such as received signal strength indicator as the former can exploit the effect of multipath propagation and is robust against non-line-of-sight channels. CSI has the potential to achieve cm-level positioning accuracy with a single reference point only. In this study, the authors survey CSI fingerprinting-based indoor positioning and mobility tracking systems. The process of fingerprinting that includes site surveying and signal preprocessing is discussed in detail. They determine the potential challenges of such systems and propose remedies to improve positioning accuracy. In general, spatial diversity, such as multiple-input multiple-output antennas and wireless sensor networks, or frequency diversity (e.g. high subcarrier count, frequency hopping mechanism) are exploited to achieve high positioning resolution. Such IPS can also be enhanced via additional sensors or spatial graphs for motion detection and tracking.

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Wearable indoor localisation approach in Internet of Things

Cited by 20 publications

References 26 publications

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey

Survey of CSI fingerprinting‐based indoor positioning and mobility tracking systems

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