The rise of the Internet of Things (IoT) and mobile internet applications has
spurred interest in location-based services (LBS) for commercial, military, and
social applications. While the global positioning system (GPS) dominates
outdoor localization, its efficacy wanes indoors due to signal challenges.
Indoor localization systems leverage wireless technologies like Wi-Fi, ZigBee,
Bluetooth, UWB, selecting based on context. Received signal strength indicator
(RSSI) technology, known for its accuracy and simplicity, is widely adopted.
This study employs machine learning algorithms in three phases: supervised
regressors, supervised classifiers, and ensemble methods for RSSI-based indoor
localization. Additionally, it introduces a weighted least squares technique
and pseudo-linear solution approach to address non-linear RSSI measurement
equations by approximating them with linear equations. An experimental testbed,
utilizing diverse wireless technologies and anchor nodes, is designed for data
collection, employing IoT cloud architectures. Pre-processing involves
investigating filters for data refinement before algorithm training. The study
employs machine learning models like linear regression, polynomial regression,
support vector regression, random forest regression, and decision tree
regressor across various wireless technologies. These models estimate the
geographical coordinates of a moving target node, and their performance is
evaluated using metrics such as accuracy, root mean square errors, precision,
recall, sensitivity, coefficient of determinant, and the f1-score. The
experiment's outcomes provide insights into the effectiveness of different
supervised machine learning techniques in terms of localization accuracy and
robustness in indoor environments.

利用机器学习算法和无线技术实现基于接收信号强度指示器 (RSSI) 的室内定位，通过测试不同的机器学习模型和无线技术，评估室内环境下的定位精度和稳定性。

利用机器学习技术改进的物联网应用的室内定位

Improved Indoor Localization with Machine Learning Techniques for IoT  applications

We study the problem of interference source identification, through the lens
of recognizing one of 15 different channels that belong to 3 different wireless
technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms
trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band.
We obtain a classification accuracy of around 89.5% using any of four different
deep neural network architectures: CNN, ResNet, CLDNN, and LSTM, which
demonstrate the generality of the effectiveness of deep learning at the
considered task. Interestingly, our proposed CNN architecture requires
approximately 60% of the training time required by the state of the art while
achieving slightly larger classification accuracy. We then focus on the CNN
architecture and further optimize its training time while incurring minimal
loss in classification accuracy using three different approaches: 1- Band
Selection, where we only use samples belonging to the lower and uppermost 2 MHz
bands, 2- SNR Selection, where we only use training samples belonging to a
single SNR value, and 3- Sample Selection, where we try various sub-Nyquist
sampling methods to select the subset of samples most relevant to the
classification task. Our results confirm the feasibility of fast deep learning
for wireless interference identification, by showing that the training time can
be reduced by as much as 30x with minimal loss in accuracy.

研究了干扰源识别问题，应用深度学习算法识别 Bluetooth，Zigbee 和 WiFi 三种无线技术的 15 个信道，通过优化 CNN 网络的训练，可在保证分类准确率的前提下将训练时间缩短至原先的大约 1/30。

深度学习用于干扰识别：波段，训练信噪比和样本选择

Deep Learning for Interference Identification: Band, Training SNR, and  Sample Selection

Indoor localization has recently witnessed an increase in interest, due to
the potential wide range of services it can provide by leveraging Internet of
Things (IoT), and ubiquitous connectivity. Different techniques, wireless
technologies and mechanisms have been proposed in the literature to provide
indoor localization services in order to improve the services provided to the
users. However, there is a lack of an up-to-date survey paper that incorporates
some of the recently proposed accurate and reliable localization systems. In
this paper, we aim to provide a detailed survey of different indoor
localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF),
Return Time of Flight (RTOF), Received Signal Strength (RSS); based on
technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra
Wideband (UWB), Bluetooth and systems that have been proposed in the
literature. The paper primarily discusses localization and positioning of human
users and their devices. We highlight the strengths of the existing systems
proposed in the literature. In contrast with the existing surveys, we also
evaluate different systems from the perspective of energy efficiency,
availability, cost, reception range, latency, scalability and tracking
accuracy. Rather than comparing the technologies or techniques, we compare the
localization systems and summarize their working principle. We also discuss
remaining challenges to accurate indoor localization.

本文从 WiFi、RFID、UWB 和蓝牙等技术出发，评估和总结当今室内定位技术的优点和挑战，并强调了该技术在 IoT 中的重要性。