6,265 research outputs found
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
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
RSSI-Based Self-Localization with Perturbed Anchor Positions
We consider the problem of self-localization by a resource-constrained mobile
node given perturbed anchor position information and distance estimates from
the anchor nodes. We consider normally-distributed noise in anchor position
information. The distance estimates are based on the log-normal shadowing
path-loss model for the RSSI measurements. The available solutions to this
problem are based on complex and iterative optimization techniques such as
semidefinite programming or second-order cone programming, which are not
suitable for resource-constrained environments. In this paper, we propose a
closed-form weighted least-squares solution. We calculate the weights by taking
into account the statistical properties of the perturbations in both RSSI and
anchor position information. We also estimate the bias of the proposed solution
and subtract it from the proposed solution. We evaluate the performance of the
proposed algorithm considering a set of arbitrary network topologies in
comparison to an existing algorithm that is based on a similar approach but
only accounts for perturbations in the RSSI measurements. We also compare the
results with the corresponding Cramer-Rao lower bound. Our experimental
evaluation shows that the proposed algorithm can substantially improve the
localization performance in terms of both root mean square error and bias.Comment: Accepted for publication in 28th Annual IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017
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