8,132 research outputs found
AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor Networks
In dense underwater sensor networks (UWSN), the major confronts are high
error probability, incessant variation in topology of sensor nodes, and much
energy consumption for data transmission. However, there are some remarkable
applications of UWSN such as management of seabed and oil reservoirs,
exploration of deep sea situation and prevention of aqueous disasters. In order
to accomplish these applications, ignorance of the limitations of acoustic
communications such as high delay and low bandwidth is not feasible. In this
paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized
Depth-based routing (AMCTD), exploring the proficient amendments in depth
threshold and implementing the optimal weight function to achieve longer
network lifetime. We segregate our scheme in 3 major phases of weight updating,
depth threshold variation and adaptive mobility of courier nodes. During data
forwarding, we provide the framework for alterations in threshold to cope with
the sparse condition of network. We ultimately perform detailed simulations to
scrutinize the performance of our proposed scheme and its comparison with other
two notable routing protocols in term of network lifetime and other essential
parameters. The simulations results verify that our scheme performs better than
the other techniques and near to optimal in the field of UWSN.Comment: 8th International Conference on Broadband and Wireless Computing,
Communication and Applications (BWCCA'13), Compiegne, Franc
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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