107,797 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
Using Artificial Intelligence in Wireless Sensor Routing Protocols
This paper represents a dissertation about how an artificial
intelligence technique can be applied to wireless sensor networks. Due
to the constraints on data processing and power consumption, the use
of artificial intelligence has been historically discarded in these kind of
networks. 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
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
A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks
The study of forest fires has been traditionally considered as an important
application due to the inherent danger that this entails. This phenomenon
takes place in hostile regions of difficult access and large areas. Introduction of
new technologies such as Wireless Sensor Networks (WSNs) has allowed us to
monitor such areas. In this paper, an intelligent system for fire prediction based
on wireless sensor networks is presented. This system obtains the probability of
fire and fire behavior in a particular area. This information allows firefighters to
obtain escape paths and determine strategies to fight the fire. A firefighter can
access this information with a portable device on every node of the network. The
system has been evaluated by simulation analysis and its implementation is being
done in a real environment.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570
Channel and active component abstractions for WSN programming - a language model with operating system support
To support the programming of Wireless Sensor Networks, a number of unconventional programming models have evolved, in particular the event-based model. These models are non-intuitive to programmers due to the introduction of unnecessary, non-intrinsic complexity. Component-based languages like Insense can eliminate much of this unnecessary complexity via the use of active components and synchronous channels. However, simply layering an Insense implementation over an existing event-based system, like TinyOS, while proving efficacy, is insufficiently space and time efficient for production use. The design and implementation of a new language-specific OS, InceOS, enables both space and time efficient programming of sensor networks using component-based languages like Insense
Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties
In an autonomous wireless sensor network, self-organization of the nodes is
essential to achieve network wide characteristics. We believe that connectivity
in wireless autonomous networks can be increased and overall average path
length can be reduced by using beamforming and bio-inspired algorithms. Recent
works on the use of beamforming in wireless networks mostly assume the
knowledge of the network in aggregation to either heterogeneous or hybrid
deployment. We propose that without the global knowledge or the introduction of
any special feature, the average path length can be reduced with the help of
inspirations from the nature and simple interactions between neighboring nodes.
Our algorithm also reduces the number of disconnected components within the
network. Our results show that reduction in the average path length and the
number of disconnected components can be achieved using very simple local rules
and without the full network knowledge.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
Un nuevo esquema de agrupación para redes sensoras inalámbricas de radio cognitivas heterogéneas
Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019.
Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads.
Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime.
Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption.
Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption.
Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition.
Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors.
Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes
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