Machine Learning Applied to LoRaWAN Network for Improving Fingerprint Localization Accuracy in Dense Urban Areas

Piroddi, Andrea; Torregiani, Maurizio

doi:10.3390/network3010010

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…It localizes using the RSSI data and the trilateration approach. Perkovic et al [16] found the localization and the precise RSSI values published to the Message Queuing Telemetry Transport (MQTT) server of the remote location using an ML method. We assess and explain the localization inaccuracy between the predicted and actual.…”

Section: Related Workmentioning

confidence: 99%

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Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

Swathika,

Dilip Kumar,

Srinidhi

et al. 2024

IEEE Access

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The learning rate is one of the most crucial hyper-parameters to regulate during the training of the Deep Learning (DL) models and optimizers. Adaptive learning rate algorithms try to automate the time-consuming process of manually setting a suitable learning rate, which is still exhausting. This research uses the learn rate schedule mechanism for training DL models. The learn rate schedule mechanism updates the learning rate for each step or iteration in DL models and optimizers for problem-solving. This paper implements a learn rate schedule mechanism and hybrid learn rate schedule mechanism like piecewise, exponential decay, polynomial time, reciprocal time and cosine annealing decay as adaptive learning rate mechanisms for DL models and optimizers like Adadelta, Adam, RMSprop and Stochastic Gradient Descent with Momentum (SGDM) to improve the accuracy of Received Signal Strength Indicator (RSSI)-based localization in LoRaWAN (Long Range Wide Area Networks) based Internet of Things (IoT) networks.These techniques aim to automate the process of determining suitable learning rates that dynamically update the learning rate for each step or iteration for optimizers and deep learning models. This technique improves the model's performance by introducing adaptability into the learning process and departing from conventional set learning rates. The mathematical model of the learning rate schedule is derived and formulated with adaptive deep learning rate models to map with the LoRaWAN RSSI-based localization datasets for accessing the performance parameters. The learn rate schedule for different types of localization datasets is also analyzed. The results were compared for all the learning rate schedule mechanisms with the default parameter settings of DL models, and it gives a better accuracy of 98.98%, which is higher than the existing models.

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Section: Related Workmentioning

confidence: 99%

“…schedule is 93.2584 and error (m) of 674 16. for SOMs in the linear RSS model and MLP in the power RSS model.…”

mentioning

confidence: 99%

Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

Swathika,

Dilip Kumar,

Srinidhi

et al. 2024

IEEE Access

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“…In addition, LoRaWAN offers features such as adaptive data rate (ADR) for efficient resource management, bi-directional communication, and strong security, making it a robust and scalable solution for IoT deployments [ 40 ]. With their long range, ultra-low power consumption, and efficient network management, LoRa and LoRaWAN are revolutionizing the IoT landscape, empowering businesses and industries with seamless connectivity and enabling innovative IoT applications across various sectors [ 41 ].…”

Section: Introductionmentioning

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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RSS-Based Localization using Deep Learning Models with Optimizer in LoRaWAN-IoT Networks

Kumar

2024

2024 IEEE International Conference for Women in Innovation, Technology &Amp;amp; Entrepreneurship (ICWITE)

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Machine Learning Applied to LoRaWAN Network for Improving Fingerprint Localization Accuracy in Dense Urban Areas

Cited by 3 publications

References 25 publications

Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

Harnessing Learn Rate Schedule for Adaptive Deep Learning in LoRaWAN-IoT Localization

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

RSS-Based Localization using Deep Learning Models with Optimizer in LoRaWAN-IoT Networks

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