Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Kotrotsios, Konstantinos; Fanariotis, Anastasios; Leligou, Helen C.; Orphanoudakis, Theofanis

doi:10.3390/s22020570

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…In addition, parameter optimization of the neural network occurs. In [66], an optimization method for indoor localization parameters for portable small embedded systems is described in more detail. The method is given a set of machine learning models in advance and searches for optimal parameters by gradually adjusting the number of layers, neurons, and the training number of the models for improvement in positioning accuracy.…”

Section: Integration With Image-based Methodsmentioning

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: Integration With Image-based Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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“…For the results to have any meaningful impact and for our measurements to provide real-world results, the selection of models had to follow a group of criteria as follows: Each model is required to belong to a group of well-defined and well-known ML models. Specifically, all selected models should be popular models whose behavior is well known (their features and parameters are understood) or models that have been used in other scientific research in microcontroller-based systems [ 13 , 14 , 15 ]. This criterion will help to make our results meaningful and easier to understand.…”

Section: System Setup and Methodologymentioning

confidence: 99%

Power Efficient Machine Learning Models Deployment on Edge IoT Devices

Fanariotis

Orphanoudakis

Kotrotsios

et al. 2023

Sensors

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Computing has undergone a significant transformation over the past two decades, shifting from a machine-based approach to a human-centric, virtually invisible service known as ubiquitous or pervasive computing. This change has been achieved by incorporating small embedded devices into a larger computational system, connected through networking and referred to as edge devices. When these devices are also connected to the Internet, they are generally named Internet-of-Thing (IoT) devices. Developing Machine Learning (ML) algorithms on these types of devices allows them to provide Artificial Intelligence (AI) inference functions such as computer vision, pattern recognition, etc. However, this capability is severely limited by the device’s resource scarcity. Embedded devices have limited computational and power resources available while they must maintain a high degree of autonomy. While there are several published studies that address the computational weakness of these small systems-mostly through optimization and compression of neural networks- they often neglect the power consumption and efficiency implications of these techniques. This study presents power efficiency experimental results from the application of well-known and proven optimization methods using a set of well-known ML models. The results are presented in a meaningful manner considering the “real world” functionality of devices and the provided results are compared with the basic “idle” power consumption of each of the selected systems. Two different systems with completely different architectures and capabilities were used providing us with results that led to interesting conclusions related to the power efficiency of each architecture.

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“…In contrast to fingerprinting approaches, deep neural networks may generalize better when the measurement environment undergoes changes with the introduction of additional or different multipath propagation components. Most of the existing approaches that employ neural networks (NNs) for indoor localization, either estimate the position directly [ 20 , 21 ] or via a combination of distance estimation and trilateration [ 22 ]. It has to be noted that AoA-based neural network localization approaches exist [ 23 ] but have so far been scarce.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal

Koutris

Siozos

Kopsinis

et al. 2022

Sensors

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In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.

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Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Cited by 6 publications

References 33 publications

Research Progress of Wireless Positioning Methods Based on RSSI

Research Progress of Wireless Positioning Methods Based on RSSI

Power Efficient Machine Learning Models Deployment on Edge IoT Devices

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal

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