2 research outputs found
Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices
open2noThe application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the de-facto technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding.openAndrea Agiollo; Andrea OmiciniAndrea Agiollo; Andrea Omicin
Opportunistic timing signals for pervasive mobile localization
MenciĂłn Internacional en el tĂtulo de doctorThe proliferation of handheld devices and the pressing need of location-based services call for
precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices.
Despite the large investments and efforts in academic and industrial communities, a pin-point solution
is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite
System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban
areas and indoor environments, where the visibility of GNSS satellites is reduced drastically.
In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive
positioning system should still rely on GNSS, yet complemented with technologies that can
guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals
are made of data with timing information. We then investigate solutions where opportunistic
timing signals can be extracted out of terrestrial technologies. These signals can then be used as
additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system
that combines range measurements from the Global Positioning System (GPS), the world’s
most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our
investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start
investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques
have been recently suggested for ranging mobile devices overWiFi radios. However, these
techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements
suffer from extensive device-related noise which makes it challenging to differentiate
between direct path from non-direct path signal components when estimating the ranges. Existing
multipath mitigation techniques tend to fail at identifying the direct path when the device-related
Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to
address this challenge, we propose a new method for filtering ranging measurements that is better
suited for the inherent large noise as found in WiFi radios. Our technique combines statistical
learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not
require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration.
This makes the method we propose as the first contribution of the present work particularly
suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures.
We evaluate our technique for indoor mobile tracking scenarios in multipath environments,
and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters.
The next step we envisioned towards preparing theoretical and practical basis for the aforementioned
hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF
ranges, and initial foundations of single-technology self-localization. Self-localization systems
based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance
therefore heavily rely on the design and parametrization of robust algorithms. We study the noise
sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning
algorithms at a mobile node using those ranges. Our results show that the localization
error varies greatly depending on the ranging technology, algorithm selection, and appropriate
tuning of the algorithms. We characterize the localization error using real-world measurements
and different parameter settings to provide guidance for the design of robust location estimators
in realistic settings.
These tools and foundations are necessary to tackle the problem of hybrid positioning system
providing high localization capabilities across indoor and outdoor environments. In this context,
the lack of a single positioning system that is able the fulfill the specific requirements of
diverse indoor and outdoor applications settings has led the development of a multitude of localization
technologies. Existing mobile devices such as smartphones therefore commonly rely on
a multi-RAT (Radio Access Technology) architecture to provide pervasive location information
in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures
consider the different localization technologies as monolithic entities and choose the final navigation
position from the RAT that is foreseen to provide the highest accuracy in the particular
context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements
of diverse radio technologies in order to circumvent the limitations of the individual radio access
technologies and improve the overall localization accuracy in different contexts. We introduce
an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a
rich set of multiple ranges can be available across different RATs, the intelligent selection of the
subset of ranges with accurate timing information is critical to achieve the best positioning accuracy.
We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging
measurements. We also address practical problems of the design space, such as removal of WiFi
chipset and environmental calibration to make the positioning system as autonomous as possible.
Experimental results show that our solution considerably outperforms the use of monolithic
technologies and methods based on classical fault detection and identification typically applied in
standalone GPS technology.
All the contributions and research questions described previously in localization and positioning
related topics suppose full knowledge of the anchors positions. In the last part of this work, we
study the problem of deriving proximity metrics without any prior knowledge of the positions of
the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and
respective received signal strength indicator (RSSI) values. Applications that benefit from proximity
metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data
set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors,
we show that metrics from related work perform poorly on real-world data. We analyze the
cause for this poor performance, and show that imperfect observations of APs with commodity
WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide
such proximity estimates, without requiring knowledge of location for the observed AP. We
address the challenge of imperfect observations of APs in the design of these improved metrics.
Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints.
We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en IngenierĂa TelemáticaPresidente: Francisco BarcelĂł Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior