In the realm of the smart city concept, every aspect of urban life is set to become intelligent, spanning across buildings, manufacturing, healthcare, agriculture, and transportation. A crucial component of the transportation system within the smart city framework is the implementation of smart parking systems. This particular aspect has garnered considerable attention from researchers,industries,and all stakeholders involved in shaping and developing the smart city vision. Currently, a diverse array of smart parking solutions is being introduced to the market by solution providers, employing various innovative approaches.Guidance is a key component in smart parking solution, we distinguish two guidance systems: External guidance and Internal guidance. External guidance help car drivers to reach a parking zone while Internal guidance gives instructions for drivers to find parking spot inside the parking space. The primary objective of this paper is to explore the smart parking domain from three distinct perspectives: i) an overview of the latest existing approaches to parking management, ii) external positioning and guidance methods for smart parking, and iii) an in-depth survey of the most widely utilized internal positioning technologies. The state of the art related to guidance in smart parking address internal and external guidance separately. we introduce in this paper the basics of our future work in which we will combine external and internal parking guidance to provide an end-to-end guidance service for smart parking users.

Smart Parking System Approaches and Positioning Technologies: A Survey

Developing indoor positioning systems (IPS) using Received Signal Strength (RSS) received from Wi-Fi sensors based on the Fingerprinting technique has become an interesting topic to many researchers in the positioning and navigation field. These systems, however, are barely tested in real environments such as complex buildings and in real situations where the change of RSS over time occurs frequently. Moreover, the cases tested in an environment close to a real scenario could not provide proper positioning accuracy. In this paper, some methods are proposed to improve the IPS with the KNN algorithm which changes the procedure of selecting the chosen train points. These methods are, also, implemented and tested in a real building with different complexities and an area of 324 m 2 . The results show an improvement of 14% in average positioning accuracy from 2.11 m to 1.82 m.

Evaluation of Improved K-Nearest Neighbors for Indoor Positioning System in Real Complex Buildings

Developing indoor positioning systems (IPS) using Received Signal Strength (RSS) received from Wi-Fi sensors based on the Fingerprinting technique has become an interesting topic to many researchers in the positioning and navigation field. These systems, however, are barely tested in real environments such as complex buildings and in real situations where the change of RSS over time occurs frequently. Moreover, the cases tested in an environment close to a real scenario could not provide proper positioning accuracy. In this paper, some methods are proposed to improve the IPS with the KNN algorithm which changes the procedure of selecting the chosen train points. These methods are, also, implemented and tested in a real building with different complexities and an area of 324 m2. The results show an improvement of 14% in average positioning accuracy from 2.11 m to 1.82 m.

In this study, we present an infrastructure-independent multi-floor indoor localization scheme that uses a deep learning (DL)-based floor detection method and a particle filter with clustering. To implement localization with limited measurement data, we incorporate the user’s vertical motion information to initialize and optimize the system. This method assumes two prerequisites: the capability for rapid floor detection and extraction of vertical motion features. These ensure a correlation between vertical movement and two-dimensional location and can be efficiently integrated with map information. The proposed scheme has several notable features. First, we utilize the strong feature extraction capability of the sequence-to-sequence (Seq2Seq) model for sequential data to implement real-time step action prediction. We also develop a floor decision algorithm to extract vertical movement information from the step action sequence. The proposed floor detection method can track the floor regardless of the user’s activities. Second, we configure calibration nodes (CN) on the map based on prior knowledge from the environmental information. By combining CNs with DL-based floor detection, we not only extend the particle filter to three-dimensional applications but also achieve calibration and repair of the localization. Third, we introduce a clustering method to improve localization accuracy and reduce computational complexity in uncertain measurements. The experimental results show that the Seq2Seq model has good robustness to noisy data, the proposed DL-based floor detection achieved an average floor number accuracy of 93.42% without restricting user behavior, and all the floor transitions were successfully recognized. Moreover, under the long path multi-floor scenario, our scheme achieved a localization accuracy of over 96% within a 2m error boundary.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10167659.pdf

Multi-Floor Indoor Localization Scheme Using a Seq2Seq-Based Floor Detection and Particle Filter With Clustering

Online RSSI selection strategy for indoor positioning in low-effort training scenarios

RFID localization methods have been widely used in indoor positioning systems (IPS). Most localization techniques involve the use of multiple antennas and the placement of antennas and readers in order to ensure accurate positioning results. However, most localization techniques are complex and require high overhead costs in terms of needing multiple antennas and RFID readers. In this paper, we proposed a method to use a single antenna to perform all the reads and rely rather on the antenna beams to acquire multiple positioning data. A single array of antennas is configured to have multiple angles of operation and rely on different power levels as compared to regular antennas. By manipulating the beam pattern, direction and power, multiple sub-antennas can be conceived and the method utilizes antenna beams and relies mainly on one antenna to realize two-dimensional localization.

/pdf/sing-ant-rfid-indoor-positioning-system-using-single-antenna-3f5wo2h4.pdf

Sing-Ant: RFID Indoor Positioning System Using Single Antenna with Multiple Beams Based on LANDMARC Algorithm

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 {limit} columns in the table. Remove at least 1 column to add or create another one.

Zhen Xu

Author Tools

Chat about Author

Papers

Sing-Ant: RFID Indoor Positioning System Using Single Antenna with Multiple Beams Based on LANDMARC Algorithm