LoRaWAN: Lost for Localization?

Svertoka, Ekaterina; Rusu-Casandra, Alexandru; Bürget, Radim; Marghescu, Ion; Hošek, Jiří; Ometov, Aleksandr

doi:10.1109/jsen.2022.3212319

Cited by 11 publications

(5 citation statements)

References 43 publications

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“…The authors of [ 224 ] collected a fingerprinting dataset by conducting two studies on indoor and outdoor environments. The first study was conducted at the Brno University of Technology in Brno, Czech Republic, and the second was conducted at the University Politechnica of Bucharest in Bucharest, Romania.…”

Section: Lorawan Datasetsmentioning

confidence: 99%

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Farhad

Pyun

2023

Sensors

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The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

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Section: Lorawan Datasetsmentioning

confidence: 99%

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Farhad

Pyun

2023

Sensors

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“…Since the working frequency is around 900 MHz, the test is in the far field region (as that zone starts below 0.5 meters); therefore, the RF signal characteristics are maintained until its disappearance. This approach of studying LoRa performance in small outdoor areas has already been used in other works like [15], [25], [26].…”

Section: Lora Performance With Human Shadowing a Experimental Setupmentioning

confidence: 99%

Effects of Body Shadowing in LoRa Localization Systems

Marquez¹,

Bahillo

Calle

et al. 2023

IEEE Access

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Applications where a person carries environmental sensors benefit from knowing the position of the individual. Researchers can locate the device by analyzing the radio signals used for sending information. However, it is necessary to know the impact of body obstruction on communication links and, therefore, on the localization system. The paper studies this impact by using field tests employing one LoRa node with an omnidirectional antenna in an outdoor parking lot with vegetation. The tests use four LoRa gateways located in the corners of the area to measure the signals with and without body obstruction. The node was located on the subject's chest to maximize signal obstruction. Results show that signal strength decreases by an average of 3 dB on links with body shadowing. Additionally, the study uses these signals with machine learning and Kalman filter localization algorithms. Finally, the results show that body shadowing affects location accuracy in most methods. These results are useful for IoT researchers that need to locate people carrying LoRaWAN devices but cannot use GPS/GNSS due to energy constraints.

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“…As a result, researchers and industrial communities must devise alternative means of localizing targets in these scenarios. These alternative means include techniques such as triangulation [10], trilateration [11,12] and fingerprinting [13][14][15][16][17] and as well as wireless technologies such as WiFi [1,6,[18][19][20], Bluetooth Low Energy (BLE) [10,11], Ultra-wideband (UWB) [21], Radio Frequency Identification (RFID) [22], Zigbee [23], and Long Range (LoRa) [24,25]. Indoor localization based on WiFi technology can be divided further into two main categories: Channel State Information (CSI) and Received Signal Strength Indicator (RSSI).…”

Section: Introductionmentioning

confidence: 99%

SALLoc: An Accurate Target Localization in WiFi-Enabled Indoor Environments via SAE-ALSTM

Ayinla,

Aziz,

Drieberg

2024

IEEE Access

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Developing a reliable and accurate indoor localization system is a crucial step for creating a seamless and interactive user-device experience in nearly all intelligent internet of things (IIoTs) and smart applications. Indoor localization systems based on WiFi fingerprinting have been considered as a promising alternative to model-based approaches owing to their accuracy, low cost, availability, and ease of configuration. However, recent studies have revealed that in complex environments, WiFi fingerprinting techniques are faced with a lot of challenges as the coverage area increases. These challenges include fingerprint spatial uncertainty, instability in the received signal strength indicator (RSSI) and discrepancy in fingerprint distribution. Furthermore, there is frequent need for database upgrades or even recreation whenever there is a change in the architecture of the location. These challenges have questioned the robustness and efficiency of most of the existing schemes. In this paper, we present an indoor localization architecture for complex multi-building multi-floor location prediction and subsequently propose SALLoc (SAE-ALSTM Localization), a WiFi fingerprinting indoor localization scheme based on Stacked Autoencoder (SAE) and Attention-based Long Short-Time Memory (ALSTM) framework. Firstly, stratified sampling technique is used to separate validation set from the entire uneven RSSI training set which ensures that the same proportion of RSSI samples are present in both sets. Secondly, SAE is utilized to select core features and decrease the dimensions of the RSSI samples. Finally, ALSTM is trained to focus on these features to achieve robust location prediction. Extensive investigations were conducted using UJIIndoorLoc, Tampere and UTSIndoorLoc datasets, and the results obtained demonstrated the superiority of the proposed scheme in terms of prediction accuracy, robustness, and generalizations when compared to state-of-the-art methods. The mean localization error (MLE) on UJIIndoorLoc, Tampere and UTSIndoorLoc datasets are 8.28 m, 9.52 m, and 6.48 m respectively. Consequently, it can be concluded that the proposed scheme is accurate and well-suited for large-scale indoor environment location prediction.

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LoRaWAN: Lost for Localization?

Cited by 11 publications

References 43 publications

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

Effects of Body Shadowing in LoRa Localization Systems

SALLoc: An Accurate Target Localization in WiFi-Enabled Indoor Environments via SAE-ALSTM

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