118 research outputs found
A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning
Wireless Sensor Network (WSN) applications reshape the trend of warehouse
monitoring systems allowing them to track and locate massive numbers of
logistic entities in real-time. To support the tasks, classic Radio Frequency
(RF)-based localization approaches (e.g. triangulation and trilateration)
confront challenges due to multi-path fading and signal loss in noisy warehouse
environment. In this paper, we investigate machine learning methods using a new
grid-based WSN platform called Sensor Floor that can overcome the issues.
Sensor Floor consists of 345 nodes installed across the floor of our logistic
research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors.
Our goal is to localize all logistic entities, for this study we use a mobile
robot. We record distributed sensing measurements of Received Signal Strength
Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon
system as the ground truth. The asynchronous collected data is pre-processed
and trained using Random Forest and Convolutional Neural Network (CNN). The CNN
model with regularization outperforms the Random Forest in terms of
localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can
be configured flexibly depending on the scenario in the warehouse. The
hardware, software and the CNN architecture of the Sensor Floor are open-source
under https://github.com/FLW-TUDO/sensorfloor.Comment: This is a preprint version for IEEE I2MTC 202
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
A Machine Learning Approach to Indoor Localization Data Mining
Indoor positioning systems are increasingly commonplace in various environments and
produce large quantities of data. They are used in industrial applications, robotics,
asset and employee tracking just to name a few use cases. The growing amount of data
and the accelerating progress of machine learning opens up many new possibilities for
analyzing this data in ways that were not conceivable or relevant before. This paper
introduces connected concepts and implementations to answer question how this data
can be utilized. Data gathered in this thesis originates from an indoor positioning system
deployed in retail environment, but the discussed methods can be applied generally.
The issue will be approached by first introducing the concept of machine learning
and more generally, artificial intelligence, and how they work on a general level. A
deeper dive is done to subfields and algorithms that are relevant to the data mining task
at hand. Indoor positioning system basics are also shortly discussed to create a base understanding
on the realistic capabilities and constraints that these kinds of systems encase.
These methods and previous knowledge from literature are put to test with the
freshly gathered data. An algorithm based on existing example from literature was tested
and improved upon with the new data. A novel method to cluster and classify movement
patterns was introduced, utilizing deep learning to create embedded representations of the
trajectories in a more complex learning pipeline. This type of learning is often referred
to as deep clustering.
The results are promising and both of the methods produce useful high level representations
of the complex dataset that can help a human operator to discern the
relevant patterns from raw data and to be used as an input for subsequent supervised and
unsupervised learning steps. Several factors related to optimizing the learning pipeline,
such as regularization were also researched and the results presented as visualizations.
The research found that pipeline consisting of CNN-autoencoder followed by a classic
clustering algorithm such as DBSCAN produces useful results in the form of trajectory
clusters. Regularization such as L1 regression improves this performance.
The research done in this paper presents useful algorithms for processing raw, noisy
localization data from indoor environments that can be used for further implementations
in both industrial applications and academia
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
A Review of Physical Human Activity Recognition Chain Using Sensors
In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.
Calibrated Simplex Mapping Classification
We propose a novel supervised multi-class/single-label classifier that maps
training data onto a linearly separable latent space with a simplex-like
geometry. This approach allows us to transform the classification problem into
a well-defined regression problem. For its solution we can choose suitable
distance metrics in feature space and regression models predicting latent space
coordinates. A benchmark on various artificial and real-world data sets is used
to demonstrate the calibration qualities and prediction performance of our
classifier.Comment: 24 pages, 8 figures, 7 table
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