RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Rathnayake, R. M. M. R.; Maduranga, Madduma Wellalage Pasan; Tilwari, Valmik; Dissanayake, Maheshi B.

doi:10.3390/eng4020085

Cited by 15 publications

(2 citation statements)

References 57 publications

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“…Several researchers have addressed the question of indoor localisation in 5G networks using KNN [23,24,[49][50][51][52][53]. Despite this, the KNN method alone failed to deal with a highly dense 3D radiomap, as studied in [13,54,55].…”

Section: Deterministic Approachmentioning

confidence: 99%

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

El Boudani,

Dagiuklas,

Kanaris

et al. 2023

Electronics

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Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.

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Section: Deterministic Approachmentioning

confidence: 99%

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

El Boudani,

Dagiuklas,

Kanaris

et al. 2023

Electronics

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show abstract

“…Location systems: IoT-based location systems can help track and guide human explorers, as well as locate astronauts, equipment, or materials. These devices can be attached to astronauts or equipment and located using radio triangulation techniques [64], with connectivity protocols like LoRa [65], Bluetooth Low Energy (BLE), or Wi-Fi [66]. • Aerospace medicine: This refers to integrating sensors into astronauts' clothing, monitoring their vital signs during missions to Mars [67].…”

mentioning

confidence: 99%

Architectural Framework and Feasibility of Internet of Things-Driven Mars Exploration via Satellite Constellations

Ledesma,

Lamo,

Fraire

et al. 2024

Electronics

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This study outlines a technical framework for Internet of Things (IoT) communications on Mars, leveraging Long Range (LoRa) technology to connect Martian surface sensors and orbiting satellites. The designed architecture adapts terrestrial satellite constellation models to Martian environments and the specific needs of interplanetary communication with Earth. It incorporates multiple layers, including Martian IoT nodes, satellite linkage, constellation configuration, and Earth communication, emphasizing potential Martian IoT applications. The analysis covers four critical feasibility aspects: the maximum communication range between surface IoT nodes and orbiting satellites, the satellite constellation’s message processing capacity to determine IoT node volume support, the communication frequency and visibility of IoT nodes based on the satellite constellation arrangement, and the interplanetary data transmission capabilities of LoRa-based IoT devices. The findings affirm LoRa’s suitability for Martian IoT communication, demonstrating extensive coverage, sufficient satellite processing capacity for anticipated IoT node volumes, and effective data transmission in challenging interplanetary conditions. This establishes the framework’s viability for advancing Mars exploration and IoT in space exploration contexts.

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Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID

Mathavan,

Siva Ranjani,

Suresh

et al. 2024

Int J Communication

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SummaryIn an indoor setting, the radio frequency communication is utilized to locate and track mobile objects utilizing Radio Frequency Identification (RFID) technology. Measurements from the Received Signal Strength Indicator (RSSI) are typically the foundation of the localization technique. In an RFID‐based interior setting, lowering tracking mistakes and increasing the precision of tracking remain difficult tasks. In order to address these issues, we developed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) framework that consists of four procedures: deep reinforced learning‐based tracking, quantum‐based localization, optimization‐based virtual reference tag allocation, and signal enhancement. In order to increase the signal's effectiveness, we initially suggested using the Extended Gradient Filter (EGF) technique to eliminate RSSI oscillations. In the second step, we suggested using the Emperor Penguin Colony (EPC) optimization technique to allocate the virtual reference tag while taking the number of tags, SNR, and temperature and humidity of the surroundings into account. In the third phase, we use a quantum neural network (QNN) for localization in order to estimate the position of the moving target. We introduced the SignRank approach to select the best virtual reference tag for localization, which lowers tracking mistakes. In conclusion, we presented the Twin Delayed Deep Deterministic Policy Gradient (TD3) method that boosts the tracking precision by tracking the moving target tag efficiently and taking into account stage, the orientation, distance, and valuable coordinates. The NS3.26 network simulator is used to run the simulation, and tracking precision, tracking error, and accumulated probabilities are used to assess effectiveness.

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RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Cited by 15 publications

References 57 publications

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Architectural Framework and Feasibility of Internet of Things-Driven Mars Exploration via Satellite Constellations

Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID

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