Radio map generation approaches for an RSSI-based indoor positioning system

Sulaiman, Batoul; Tarapiah, Saed; Atalla, Shadi; Mansoor, Wathiq; Himeur, Yassine

doi:10.1016/j.sasc.2023.200054

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…Based on previous research, a higher level of positioning accuracy can be achieved by reducing the interval between sampling points. To obtain a higher accuracy of positioning, a large number of collected data need to be obtained, thus increasing the time and cost of data collection to a certain extent [82]. Based on this, many researchers have Based on previous research, a higher level of positioning accuracy can be achieved by reducing the interval between sampling points.…”

Section: Real Data Acquisitionmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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Section: Real Data Acquisitionmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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“…The study in [23] focuses on optimizing resource efficiency in D2D communication by introducing a sharing optimization algorithm based on PSO. In [24], a PSO-based algorithm is proposed for data transmission between high-speed devices, such as Unmanned Aerial Vehicles (UAVs) [25], energy systems [26,27], indoor monitoring [28,29], smart cities [30,31], healthcare [32,33], indoor localization [34,35], etc., aiming to minimize energy consumption while maintaining minimum Quality-of-Service (QoS) requirements. Mehdi et al [36] applied an algorithm inspired by bee flight patterns to find optimal resource allocation solutions for D2D communication.…”

Section: Related Workmentioning

confidence: 99%

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Benbraika,

Kraa,

Himeur

et al. 2024

Computers

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Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.

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“…In this chapter, we cover relevant works on the design of indoor positioning implemented in the nursing facility. With the advent of the Internet of Things (IoT), devices that easily connect to the network such as beacons, tags, and mobile devices [2,[37][38][39][40] are preferred in system design. Signal measurements are generally based on time, angle, and received signal strength (RSS) [28,29,31,38,41].…”

Section: Related Literaturementioning

confidence: 99%

“…With the advent of the Internet of Things (IoT), devices that easily connect to the network such as beacons, tags, and mobile devices [2,[37][38][39][40] are preferred in system design. Signal measurements are generally based on time, angle, and received signal strength (RSS) [28,29,31,38,41].…”

Section: Related Literaturementioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

Sensors

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In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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Radio map generation approaches for an RSSI-based indoor positioning system

Cited by 15 publications

References 29 publications

Research Progress of Wireless Positioning Methods Based on RSSI

Research Progress of Wireless Positioning Methods Based on RSSI

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

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