Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian, Reza; Macrina, Giusy; Scalzo, Edoardo; Guerriero, Francesca

doi:10.3390/s23073551

Cited by 9 publications

(3 citation statements)

References 79 publications

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“…Additionally, deep learning techniques allow for indoor navigation that is accurate and responsive to adjustments in the surrounding environment, such as the presence of obstacles or changes in lighting conditions (Shahbazian et al, 2023). These factors can affect the accuracy and reliability of the algorithm, especially if it has not been trained in a wide range of environments and scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.

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Section: Introductionmentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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“…GPS is recognized as one of the most widely utilized techniques, providing high-precision coordinates. IoT systems also utilize the ToA, difference time of arrival (DToA), and received signal strength (RSS) to estimate the locations of devices [7][8][9]. While these techniques contribute to improving the accuracy and reliability of IoT node detection and positioning, it is essential to acknowledge their vulnerability to location spoofing attacks and signal spoofing attacks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT Networks

Aldosari

2023

Sensors

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In the context of the Internet of Things (IoT), location-based applications have introduced new challenges in terms of location spoofing. With an open and shared wireless medium, a malicious spoofer can impersonate active devices, gain access to the wireless channel, as well as emit or inject signals to mislead IoT nodes and compromise the detection of their location. To address the threat posed by malicious location spoofing attacks, we develop a neural network-based model with single access point (AP) detection capability. In this study, we propose a method for spoofing signal detection and localization by leveraging a feature extraction technique based on a single AP. A neural network model is used to detect the presence of a spoofed unmanned aerial vehicle (UAV) and estimate its time of arrival (ToA). We also introduce a centralized approach to data collection and localization. To evaluate the effectiveness of detection and ToA prediction, multi-layer perceptron (MLP) and long short-term memory (LSTM) neural network models are compared.

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“…Moreover, significant growth in computing unit by using advanced machine learning methods (such as deep neural networks, generative adversarial networks, etc.) to improve privacy, reliability (minimizing false alarms), and computing time are evident [12] [13]. However, the efforts from the information technology domain (communication unit), particularly, in the direction of developing and designing interaction platforms adhering to the information communication (IC) needs and requirements of the informal caregivers (or other stakeholders) are lacking [14].…”

Section: Introductionmentioning

confidence: 99%

Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform: Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment (Preprint)

Sharma,

Grotenhuijs,

Gemert-Pijnen

et al. 2023

Preprint

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BACKGROUND Unobtrusive sensing technologies developed for monitoring deviant behaviors in older adult care requires its integration with an interaction platform to facilitate the flow of information between them and concerned caregivers. However, the continuous monitoring capabilities generate a considerable amount of data that must be interpreted, filtered, and personalized before being communicated to the informal caregivers based on their specific care needs and requirements. OBJECTIVE For the effective implementation of unobtrusive sensing solutions (USSs) in the care of older adults with cognitive impairment, this study aims to explore the expectation and pre-conditions for the implementation of USSs from the perspective of informal caregivers. Followed to that, a low-fidelity prototype of an interaction platform, incorporating persuasive system design (PSD) features based on the needs and requirements of informal caregivers, was designed and evaluated for its conceptual workflow and usability. METHODS Six informal caregivers of older adults with cognitive impairment living alone participated in this qualitative (interview) study. The expectation and pre-conditions for implementation were explored through open questions whereas formative evaluation (usability study with thinking aloud approach) was conducted to evaluate the conceptual workflow and used PSD features in the interaction platform. Overall, a mix of inductive and thematic analysis was used to analysis the interviews. RESULTS The result of this study presents both positive and negative outcome expectations regarding the implementation of a USSs, highlighting benefits such as objective decision-making and peace of mind, but also concerns about information overload and potential substitution of human contact. Strategic information communication agreements between informal and formal caregivers were deemed crucial for the successful implementation of USSs in care. Overall, informal caregivers had a positive experience with the lo-fi prototype of the interaction platform, particularly valuing the personalization feature. CONCLUSIONS In conclusion, to achieve successful implementation, a holistic design approach is necessary, and equal consideration should be given to the personalization-privacy paradox to balance user needs and privacy.

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Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Cited by 9 publications

References 79 publications

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Deep Learning-Based Location Spoofing Attack Detection and Time-of-Arrival Estimation through Power Received in IoT Networks

Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform: Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment (Preprint)

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