1,829 research outputs found
Localization in Long-range Ultra Narrow Band IoT Networks using RSSI
Internet of things wireless networking with long range, low power and low
throughput is raising as a new paradigm enabling to connect trillions of
devices efficiently. In such networks with low power and bandwidth devices,
localization becomes more challenging. In this work we take a closer look at
the underlying aspects of received signal strength indicator (RSSI) based
localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI
has been used for fingerprinting localization where RSSI measurements of GPS
anchor nodes have been used as landmarks to classify other nodes into one of
the GPS nodes classes. Through measurements we show that a location
classification accuracy of 100% is achieved when the classes of nodes are
isolated. When classes are approaching each other, our measurements show that
we can still achieve an accuracy of 85%. Furthermore, when the density of the
GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus
improve the possibility of localizing nodes with an error less than 20m from
20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes
is localized with an error of less than 50m in our experiment with
non-optimized anchor node locations.Comment: Accepted in ICC 17. To be presented in IEEE International Conference
on Communications (ICC), Paris, France, 201
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study
The accuracy of indoor wireless localization systems can be substantially
enhanced by map-awareness, i.e., by the knowledge of the map of the environment
in which localization signals are acquired. In fact, this knowledge can be
exploited to cancel out, at least to some extent, the signal degradation due to
propagation through physical obstructions, i.e., to the so called
non-line-of-sight bias. This result can be achieved by developing novel
localization techniques that rely on proper map-aware statistical modelling of
the measurements they process. In this manuscript a unified statistical model
for the measurements acquired in map-aware localization systems based on
time-of-arrival and received signal strength techniques is developed and its
experimental validation is illustrated. Finally, the accuracy of the proposed
map-aware model is assessed and compared with that offered by its map-unaware
counterparts. Our numerical results show that, when the quality of acquired
measurements is poor, map-aware modelling can enhance localization accuracy by
up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless
Communications, 201
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Location awareness in wireless networks may enable many applications such as
emergency services, autonomous driving and geographic routing. Although there
are many available positioning techniques, none of them is adapted to work with
massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G
technology candidate. In this paper, we discuss possible solutions for
positioning of mobile stations using a vector of signals at the base station,
equipped with many antennas distributed over deployment area. Our main proposal
is to use fingerprinting techniques based on a vector of received signal
strengths. This kind of methods are able to work in highly-cluttered multipath
environments, and require just one base station, in contrast to standard
range-based and angle-based techniques. We also provide a solution for
fingerprinting-based positioning based on Gaussian process regression, and
discuss main applications and challenges.Comment: Proc. of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall
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
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