A Quantum Algorithm for RF-based Fingerprinting Localization Systems

Shokry, Ahmed; Youssef, Moustafa

doi:10.1109/lcn53696.2022.9843246

Cited by 9 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hoang et al proposed recurrent neural networks for WiFi fingerprinting indoor localization [ 38 ]. Ahmed Shokry et al proposed a quantum fingerprint-based localization algorithm to enable large-scale location tracking systems [ 39 ]. Fingerprint localization involves two stages: offline training and online positioning.…”

Section: Related Workmentioning

confidence: 99%

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Zheng

Pang

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore , we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.

show abstract

Section: Related Workmentioning

confidence: 99%

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Zheng

Pang

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, there have been successful proposals of quantum fingerprint matching algorithms in the literature [6,7,8,9,10,11,12]. These techniques leverage universal gatebased quantum machines to achieve exponential saving in matching time and space in terms of the number of APs and in terms of the number of fingerprint locations [13].…”

Section: Introductionmentioning

confidence: 99%

QLoc: A Realistic Quantum Fingerprint-based Algorithm for Large Scale Localization

Shokry

Youssef

2022

2022 IEEE International Conference on Quantum Computing and Engineering (QCE)

Self Cite

View full text Add to dashboard Cite

Effective access points (APs) selection is a crucial step in localization systems. It directly affects both localization accuracy and computational efficiency. Classical APs selection algorithms are usually computationally expensive, hindering the deployment of localization systems in a large worldwide scale.In this paper, we introduce a quantum APs selection algorithm for large-scale localization systems. The proposed algorithm leverages quantum annealing to eliminate redundant and noisy APs. We explain how to formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for quantum annealing, and how to select the minimum number of APs that maintain the same overall localization system accuracy as the complete APs set. Based on this, we further propose a logarithmic-complexity algorithm to select the optimal number of APs.We implement our quantum algorithm on a real D-Wave Systems quantum machine and assess its performance in a real test environment for a floor localization problem. Our findings reveal that by selecting fewer than 14% of the available APs in the environment, our quantum algorithm achieves the same floor localization accuracy as utilizing the entire set of APs and a superior accuracy over utilizing the reduced dataset by classical APs selection counterparts. Moreover, the proposed quantum algorithm achieves more than an order of magnitude speedup over the corresponding classical APs selection algorithms, emphasizing the efficiency of the proposed quantum algorithm for large-scale localization systems.

show abstract

An Efficient Quantum Binary-Neuron Algorithm for Accurate Multi-Story Floor Localization

Zook,

Shokry,

Youssef

2024

2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

A Quantum Algorithm for RF-based Fingerprinting Localization Systems

Cited by 9 publications

References 51 publications

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

QLoc: A Realistic Quantum Fingerprint-based Algorithm for Large Scale Localization

An Efficient Quantum Binary-Neuron Algorithm for Accurate Multi-Story Floor Localization

Contact Info

Product

Resources

About