Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

Sadowski, Sebastian; Spachos, Petros; Plataniotis, Konstantinos N.

doi:10.1109/jiot.2020.2992651

Cited by 92 publications

(62 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, many mobile terminals such as smartphones are popular, and the number of cell phone holdings is increasing dramatically, so some indoor positioning technologies for cell phones are also being studied more often. Location techniques based on smartphone terminals often do not require many other positioning aids, and today's smartphones also integrate a variety of sensors, such as temperature sensors, acceleration sensors, and geomagnetic sensors [3]. Moreover, we can save more positioning costs and use the sensors carried by the locator's cell phone itself to be able to locate the location, so using the cell phone as the main tool to achieve indoor positioning has considerable practical value and practical significance.…”

Section: Introductionmentioning

confidence: 99%

“…However, a single indoor positioning technology has some technical defects in the positioning process to meet the positioning needs, so it also needs to be integrated with other positioning technologies for positioning. erefore, this article proposes an optimized indoor positioning scheme based on the fusion of iBeacon positioning and PDR positioning, using the acceleration sensors and geomagnetic sensors carried by smartphones themselves [3], combined with the low-cost, simple deployment, and higher accuracy iBeacon positioning technology for indoor fusion positioning, which improves the accuracy of indoor positioning to a great extent. e development of IoT, big data, cloud computing, and other technologies as the core technologies of smart classrooms has promoted the development of smart classrooms, and the data sources of education big data mainly include four aspects such as IoT sensing, video recording, image recognition, and platform acquisition, among which the data collected by IoT sensing mainly include time, content, and location information [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

View full text Add to dashboard Cite

In this article, we use a low-power iBeacon network to conduct an in-depth analysis and research on the principle of indoor positioning and adopt an efficient and fast positioning algorithm. Secondly, based on the obtained analysis, an iBeacon-based indoor positioning system is proposed to analyze how to use iBeacon for accurate positioning and whether it can effectively compensate for the current mainstream positioning system. We analyze the requirements of the iBeacon-based indoor positioning system and propose the design of this positioning system. We analyze the platform and environment for software development, design the general framework of the positioning system, and analyze the logical structure of the whole system, the structure of data flow, and the communication protocols between each module of the positioning system. Then, we analyze the functions of the server module and the client module of the system, implement the functions of each module separately, and debug the functions of each module separately after each module is implemented. The feasibility of the algorithm and the performance improvement are confirmed by the experimental data. Our results show that the communication distance is improved by approximately 20.25% and the accuracy is improved by 5.62% compared to other existing results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Regarding indoor localization, however, the state of the art techniques are designed based on the use of Bluetooth Low Energy (BLE) (Ng et al, 2020 ; Sadowski et al, 2020 ; Spachos and Plataniotis, 2020a , b ), Ultra High-Frequency RFID (Li et al, 2019 ), WiFi (Wang et al, 2017 ), and hybrid systems (Guo et al, 2019 ; Monica and Bergenti, 2019 ). Our perspective is that using these technologies; a wearable IoMT device can be equipped with cloud-based signal triangulation techniques and advanced filtering, data fusion, and estimation approaches (such as Kalman-based sensor fusion and machine learning techniques) to locate the wearer with respect to the known locations of signal transmitters installed in an indoor infrastructure.…”

Section: Iomt Wearable Technologiesmentioning

confidence: 99%

“…Please see more details in Ng et al ( 2020 ), Servick ( 2020 ), and Zastrow ( 2020 ) for an explanation of the use of BLE technology on smartphone applications for tracking COVID-19 infection and some recent efforts such as those by Google and Apple for releasing an application for doing this. Also please see Wang et al ( 2017 ), Guo et al ( 2019 ), Li et al ( 2019 ), Monica and Bergenti ( 2019 ), Mackey et al ( 2020 ), Sadowski et al ( 2020 ), Spachos and Plataniotis ( 2020a ), and Spachos and Plataniotis ( 2020b ) for more details on technologies that can be used for indoor tracking. However, there is a wide range of challenges to be addressed for the use of such technology.…”

Section: Iomt Wearable Technologiesmentioning

confidence: 99%

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19

2021

View full text Add to dashboard Cite

The COVID-19 pandemic has highly impacted the communities globally by reprioritizing the means through which various societal sectors operate. Among these sectors, healthcare providers and medical workers have been impacted prominently due to the massive increase in demand for medical services under unprecedented circumstances. Hence, any tool that can help the compliance with social guidelines for COVID-19 spread prevention will have a positive impact on managing and controlling the virus outbreak and reducing the excessive burden on the healthcare system. This perspective article disseminates the perspectives of the authors regarding the use of novel biosensors and intelligent algorithms embodied in wearable IoMT frameworks for tackling this issue. We discuss how with the use of smart IoMT wearables certain biomarkers can be tracked for detection of COVID-19 in exposed individuals. We enumerate several machine learning algorithms which can be used to process a wide range of collected biomarkers for detecting (a) multiple symptoms of SARS-CoV-2 infection and (b) the dynamical likelihood of contracting the virus through interpersonal interaction. Eventually, we enunciate how a systematic use of smart wearable IoMT devices in various social sectors can intelligently help controlling the spread of COVID-19 in communities as they enter the reopening phase. We explain how this framework can benefit individuals and their medical correspondents by introducing Systems for Symptom Decoding (SSD), and how the use of this technology can be generalized on a societal level for the control of spread by introducing Systems for Spread Tracing (SST).

show abstract

“…It is expected that in near future Smart Cities (SCs) [1] flourish across the globe aiming at efficient management of global urbanization and energy consumption [2] to assist in having more environmental friendly societies [3]. The concept of Internet of Things (IoT) [4][5][6] plays a vital role in development of SCs by presenting an interactive infrastructure that can provide a large amount of sensory data in a real time fashion to monitor, manage, and control the SC. On one hand, the large volume of data provided by IoT devices can assist in implementation of predictive analytic in SCs for policy design and city-wide decision making.…”

Section: Introductionmentioning

confidence: 99%

Makf-Sr: Multi-Agent Adaptive Kalman Filtering-Based Successor Representations

Salimibeni

Malekzadeh

Mohammadi

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization and energy consumption. Multi-agent Reinforcement Learning (RL) is an efficient solution to utilize large amount of sensory data provided by the Internet of Things (IoT) infrastructure of the SCs for city-wide decision making and managing demand response. Conventional Model-Free (MF) and Model-Based (MB) RL algorithms, however, use a fixed reward model to learn the value function rendering their application challenging for ever changing SC environments. Successor Representations (SR)-based techniques are attractive alternatives that address this issue by learning the expected discounted future state occupancy, referred to as the SR, and the immediate reward of each state. SR-based approaches are, however, mainly developed for single agent scenarios and have not yet been extended to multi-agent settings. The paper addresses this gap and proposes the Multi-Agent Adaptive Kalman Filtering-based Successor Representation (MAKF-SR) framework. The proposed framework can adapt quickly to the changes in a multi-agent environment faster than the MF methods and with a lower computational cost compared to MB algorithms. The proposed MAKF-SR is evaluated through a comprehensive set of experiments illustrating superior performance compared to its counterparts.

show abstract

Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

Cited by 92 publications

References 39 publications

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19

Makf-Sr: Multi-Agent Adaptive Kalman Filtering-Based Successor Representations

Contact Info

Product

Resources

About