16,351 research outputs found
Fuzzy based clustering in CWPSN using machine learning model
90-94Cognitive wireless power sensor network (CWPSN) technology, widely used in almost all fields, has addressed various issues. The
researchers have addressed the problems in the lack of radio spectrum availability and enabled the allocation of dynamic spectrum
access in specific fields. The main challenge has been to support the radio spectrum allocation using intelligent adaptive learning
and decision-making techniques so that various requirements of 5G wireless networks can be encountered. Machine learning (ML)
is one of the most promising artificial intelligence tools conceived to support cognitive wireless networks. This paper aims to
provide energy optimization and enhance security to cognitive wireless power sensor networks using a novel protocol during
resource allocation. In addition to the existing methods, a novel protocol, fuzzy cluster-based greedy algorithms for attack
prediction and energy harvesting using a machine-language model based on neural network techniques have been introduced. The
simulation has been done using MATLAB software tools which gives efficient results
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
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