10,010 research outputs found
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
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
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
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
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks
This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for
Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN
applications having different types of data traffic with several priorities.
The protocol achieves to increase Packet Reception Ratio (PRR) and reduce
end-to-end delay while considering multi-queue priority policy, two-hop
neighborhood information, link reliability and power efficiency. The protocol
is modular and utilizes effective methods for estimating the link metrics.
Numerical results show that the proposed protocol is a feasible solution to
addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page
The Bus Goes Wireless: Routing-Free Data Collection with QoS Guarantees in Sensor Networks
Abstract—We present the low-power wireless bus (LWB), a new communication paradigm for QoS-aware data collection in lowpower sensor networks. The LWB maps all communication onto network floods by using Glossy, an efficient flooding architecture for wireless sensor networks. Therefore, unlike current solutions, the LWB requires no information of the network topology, and inherently supports networks with mobile nodes and multiple data sinks. A LWB prototype implemented in Contiki guarantees bounded end-to-end communication delay and duplicate-free, inorder packet delivery—key QoS requirements in many control and mission-critical applications. Experiments on two testbeds demonstrate that the LWB prototype outperforms state-of-theart data collection and link layer protocols, in terms of reliability and energy efficiency. For instance, we measure an average radio duty cycle of 1.69 % and an overall data yield of 99.97 % in a typical data collection scenario with 85 sensor nodes on Twist. I
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