15,979 research outputs found
A new QoS routing algorithm based on self-organizing maps for wireless sensor networks
For the past ten years, many authors have focused
their investigations in wireless sensor networks. Different
researching issues have been extensively developed: power
consumption, MAC protocols, self-organizing network algorithms,
data-aggregation schemes, routing protocols, QoS
management, etc. Due to the constraints on data processing
and power consumption, the use of artificial intelligence
has been historically discarded. However, in some special
scenarios the features of neural networks are appropriate to
develop complex tasks such as path discovery. In this paper,
we explore and compare the performance of two very well
known routing paradigms, directed diffusion and Energy-
Aware Routing, with our routing algorithm, named SIR,
which has the novelty of being based on the introduction of
neural networks in every sensor node. Extensive simulations
over our wireless sensor network simulator, OLIMPO, have
been carried out to study the efficiency of the introduction
of neural networks. A comparison of the results obtained
with every routing protocol is analyzed. This paper attempts
to encourage the use of artificial intelligence techniques in
wireless sensor nodes
Using artificial intelligence in routing schemes for wireless networks
For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have
been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing
protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has
been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks
such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and
Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks
in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study
the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This
paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes
Giving Neurons to Sensors: An Approach to QoS Management Through Artificial Intelligence in Wireless Networks
For the latest ten years, many authors have focused their investigations
in wireless sensor networks. Different researching issues have
been extensively developed: power consumption, MAC protocols, selforganizing
network algorithms, data-aggregation schemes, routing protocols,
QoS management, etc. Due to the constraints on data processing
and power consumption, the use of artificial intelligence has been historically
discarded. However, in some special scenarios the features of
neural networks are appropriate to develop complex tasks such as path
discovery. In this paper, we explore the performance of two very well
known routing paradigms, directed diffusion and Energy-Aware Routing,
and our routing algorithm, named SIR, which has the novelty of being
based on the introduction of neural networks in every sensor node. Extensive
simulations over our wireless sensor network simulator, OLIMPO,
have been carried out to study the efficiency of the introduction of neural
networks. A comparison of the results obtained with every routing protocol
is analyzed. This paper attempts to encourage the use of artificial
intelligence techniques in wireless sensor nodes
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
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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