5,447 research outputs found
Spatial Wireless Channel Prediction under Location Uncertainty
Spatial wireless channel prediction is important for future wireless
networks, and in particular for proactive resource allocation at different
layers of the protocol stack. Various sources of uncertainty must be accounted
for during modeling and to provide robust predictions. We investigate two
channel prediction frameworks, classical Gaussian processes (cGP) and uncertain
Gaussian processes (uGP), and analyze the impact of location uncertainty during
learning/training and prediction/testing, for scenarios where measurements
uncertainty are dominated by large-scale fading. We observe that cGP generally
fails both in terms of learning the channel parameters and in predicting the
channel in the presence of location uncertainties.\textcolor{blue}{{} }In
contrast, uGP explicitly considers the location uncertainty. Using simulated
data, we show that uGP is able to learn and predict the wireless channel
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Towards Odor-Sensitive Mobile Robots
J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012
Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical
ones when operating in real environments. Until now, these sensorial systems mostly relied on range
sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely
been employed, they can provide a complementary sensory information, vital for some applications, as
with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities
and also reviews some of the hurdles that are preventing smell from achieving the importance of other
sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status
on the three main fields within robotics olfaction: the classification of volatile substances, the spatial
estimation of the gas dispersion from sparse measurements, and the localization of the gas source within
a known environment
- …