A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system’s localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user’s instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers’ suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

/pdf/a-survey-of-the-performance-limiting-factors-of-a-2-bd00b17t.pdf

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives. The paper provides a comprehensive overview of positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of exisitng efforts and new trends in the field of positioning from both the academic and industrial communities would be a useful reference to researchers in the field.

pdf/positioning-using-wireless-networks-applications-recent-3oditnri8h.pdf

Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology. 

A Dual-Branch Convolutional Neural Network-Based Bluetooth Low Energy Indoor Positioning Algorithm by Fusing Received Signal Strength with Angle of Arrival

As an important branch of free-space optical (FSO) communication technology, ultraviolet (UV) communication is mainly applied to mobile communication platforms represented by unmanned aerial vehicle (UAV). With the development of LEDs and UV detector devices, UAV UV communication technology has shown great potential in related fields. But at the same time, it also faces some challenges. As the communication distance increases, the path loss of the UV communication system can reach 0.12dB/m, and the variation in bit error rate (BER) can rapidly deteriorate from the order of $10^{-8}$ to $10^{-1}$ . Additionally, the UV communication system mounted on a UAV platform can emit radiation into the environment, which may have negative effects on human health when the radiation intensity exceeds $0.5{\mu }$ W/cm2. These issues can be summarized as the availability, stability, and effectiveness of UAV-based UV communication technology. This paper aims to comprehensively address both UAVs and UV communication, providing a detailed introduction to the challenges and solutions facing UAV-based UV communication technology. Focusing on the specific aspects of these three issues, the paper first introduces the research background, value, and challenges of UAV-based UV communication technology, and investigates the current research status of UV communication channel models and positioning techniques. In order to solve the problem of UV environmental radiation, the article goes on to introduce beamforming and power control in UV optical communication technology. To solve the problem of reducing signal attenuation and increasing the communication range, the article introduces diversity technology and networking technology. In order to balance the communication quality and communication rate during UAV movement, the article introduces adaptive modulation and adaptive coding technology. Finally, the future development direction of UAV UV communication technology is summarised.

A Review of UV Communications for UAVs

Indoor wireless sensor networks (WSNs) are necessary for the provision of precise localization services for a variety of applications, including the tracking of assets, the monitoring of the environment, and navigation inside an interior space. Because it offers such high accuracy in distance measurements, ultra-wideband technology is an alternative worth considering for use in indoor localization applications. The vast majority of the activities that are now being done do not take into consideration the nodes in the network in a dynamic manner, and anchor nodes are put at random, which results in difficulties with low connection and poor connectivity. In addition, multi-path fading and incorrect identification of line-of-sight (LOS) and non-line-of-sight (NLOS) during localization, which may occur when the signals are scattered, are additional problems that lead to a poor connection in the current attempts. Numerous prior papers focused solely on sparse techniques like Time difference of arrival (TDoA) and Angle of Arrival (AoA), which are inadequate and have an influence on the precision of the localization process. Within the scope of this investigation, a novel strategy for enhancing UWB-based bi-directional localization in indoor WSNs has been presented. This technique makes use of a complementary pairing of Model-Contrastive federated learning (MCFL) and Dynamic Hexagonal Grid Construction (DHGC). For reducing complexity, we propose a 3D constructed map using hybrid of Tweak Capsule Network (TCapsNet) and Graphical Convolutional Network (GCN). Our proposed method demonstrates significant improvements in localization accuracy in contrast to existing techniques.

Model-contrastive federated learning entrenched UWB bi-direction localization through dynamic hexagonal grid construction in indoor WSN environment

In recent years, tremendous advances have been made in the design and applications of wireless networks and embedded sensors. The combination of sophisticated sensors with wireless communication has introduced new applications, which can simplify humans’ daily activities, increase independence, and improve quality of life. Although numerous positioning techniques and wireless technologies have been introduced over the last few decades, there is still a need for improvements, in terms of efficiency, accuracy, and performance for the various applications. Localization importance increased even more recently, due to the coronavirus pandemic, which made people spend more time indoors. Improvements can be achieved by integrating sensor fusion and combining various wireless technologies for taking advantage of their individual strengths. Integrated sensing is also envisaged in the coming technologies, such as 6G. The primary aim of this review article is to discuss and evaluate the different wireless positioning techniques and technologies available for both indoor and outdoor localization. This, in combination with the analysis of the various discussed methods, including active and passive positioning, SLAM, PDR, integrated sensing, and sensor fusion, will pave the way for designing the future wireless positioning systems.

/pdf/a-review-of-wireless-positioning-techniques-and-technologies-3str34jt.pdf

A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Constantina Isaia

Author Tools

Chat about Author

Papers

A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G