2,532 research outputs found
Low-effort place recognition with WiFi fingerprints using deep learning
Using WiFi signals for indoor localization is the main localization modality
of the existing personal indoor localization systems operating on mobile
devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals
are usually available indoors and can provide rough initial position estimate
or can be used together with other positioning systems. Currently, the best
solutions rely on filtering, manual data analysis, and time-consuming parameter
tuning to achieve reliable and accurate localization. In this work, we propose
to use deep neural networks to significantly lower the work-force burden of the
localization system design, while still achieving satisfactory results.
Assuming the state-of-the-art hierarchical approach, we employ the DNN system
for building/floor classification. We show that stacked autoencoders allow to
efficiently reduce the feature space in order to achieve robust and precise
classification. The proposed architecture is verified on the publicly available
UJIIndoorLoc dataset and the results are compared with other solutions
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
A Robust Zero-Calibration RF-based Localization System for Realistic Environments
Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based
location determination systems usually require a tedious calibration phase to
construct an RF fingerprint of the area of interest. This fingerprint varies
with the used mobile device, changes of the transmit power of smart access
points (APs), and dynamic changes in the environment; requiring re-calibration
of the area of interest; which reduces the technology ease of use. In this
paper, we present IncVoronoi: a novel system that can provide zero-calibration
accurate RF-based indoor localization that works in realistic environments. The
basic idea is that the relative relation between the received signal strength
from two APs at a certain location reflects the relative distance from this
location to the respective APs. Building on this, IncVoronoi incrementally
reduces the user ambiguity region based on refining the Voronoi tessellation of
the area of interest. IncVoronoi also includes a number of modules to
efficiently run in realtime as well as to handle practical deployment issues
including the noisy wireless environment, obstacles in the environment,
heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on
different Android phones using the iBeacons technology in a university campus.
Evaluation of IncVoronoi with a side-by-side comparison with traditional
fingerprinting techniques shows that it can achieve a consistent median
accuracy of 2.8m under different scenarios with a low beacon density of one
beacon every 44m2. Compared to fingerprinting techniques, whose accuracy
degrades by at least 156%, this accuracy comes with no training overhead and is
robust to the different user devices, different transmit powers, and over
temporal changes in the environment. This highlights the promise of IncVoronoi
as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201
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