122,004 research outputs found
Learning to be energy-efficient in cooperative networks
Cooperative communication has great potential to improve the transmit diversity in multiple users environments. To achieve a high network-wide energy-efficient performance, this letter poses the relay selection problem of cooperative communication as a noncooperative automata game considering nodes’ selfishness, proving that it is an ordinal game (OPG), and presents a game-theoretic analysis to address the benefit equilibrium decision-making issue in relay selection. A stochastic learning-based relay selection algorithm is proposed for transmitters to learn a Nash-equilibrium strategy in a distributed manner. We prove through theoretical and numerical analysis that the proposed algorithm is guaranteed to converge to a Nash equilibrium state, where the resulting cooperative network is energy-efficient and reliable. The strength of the proposed algorithm is also confirmed through comparative simulations in terms of energy benefit and fairness performances
Cooperation Enforcement for Packet Forwarding Optimization in Multi-hop Ad-hoc Networks
Ad-hoc networks are independent of any infrastructure. The nodes are
autonomous and make their own decisions. They also have limited energy
resources. Thus, a node tends to behave selfishly when it is asked to forward
the packets of other nodes. Indeed, it would rather choose to reject a
forwarding request in order to save its energy. To overcome this problem, the
nodes need to be motivated to cooperate. To this end, we propose a
self-learning repeated game framework to enforce cooperation between the nodes
of a network. This framework is inspired by the concept of "The Weakest Link"
TV game. Each node has a utility function whose value depends on its
cooperation in forwarding packets on a route as well as the cooperation of all
the nodes that form this same route. The more these nodes cooperate the higher
is their utility value. This would establish a cooperative spirit within the
nodes of the networks. All the nodes will then more or less equally participate
to the forwarding tasks which would then eventually guarantee a more efficient
packets forwarding from sources to respective destinations. Simulations are run
and the results show that the proposed framework efficiently enforces nodes to
cooperate and outperforms two other self-learning repeated game frameworks
which we are interested in.Comment: Published in the proceedings of the IEEE Wireless Communications and
Networking Conference (WCNC 2012), Paris, France, 201
RESOURCE AND ENVIRONMENT AWARE SENSOR COMMUNICATIONS: FRAMEWORK, OPTIMIZATION, AND APPLICATIONS
Recent advances in low power integrated circuit devices,
micro-electro-mechanical system (MEMS) technologies, and
communications technologies have made possible the deployment of
low-cost, low power sensors that can be integrated to form wireless
sensor networks (WSN). These wireless sensor networks have vast
important applications, i.e.: from battlefield surveillance system
to modern highway and industry monitoring system; from the emergency
rescue system to early forest fire detection and the very
sophisticated earthquake early detection system. Having the broad
range of applications, the sensor network is becoming an integral
part of human lives. However, the success of sensor networks
deployment depends on the reliability of the network itself. There
are many challenging problems to make the deployed network more
reliable. These problems include but not limited to extending
network lifetime, increasing each sensor node throughput, efficient
collection of information, enforcing nodes to collaboratively
accomplish certain network tasks, etc. One important aspect in
designing the algorithm is that the algorithm should be completely
distributed and scalable. This aspect has posed a tremendous
challenge in designing optimal algorithm in sensor networks.
This thesis addresses various challenging issues encountered in
wireless sensor networks. The most important characteristic in
sensor networks is to prolong the network lifetime. However, due to
the stringent energy requirement, the network requires highly energy
efficient resource allocation. This highly energy-efficient resource
allocation requires the application of an energy awareness system.
In fact, we envision a broader resource and environment aware
optimization in the sensor networks. This framework reconfigures the
parameters from different communication layers according to its
environment and resource. We first investigate the application of
online reinforcement learning in solving the modulation and transmit
power selection. We analyze the effectiveness of the learning
algorithm by comparing the effective good throughput that is
successfully delivered per unit energy as a metric. This metric
shows how efficient the energy usage in sensor communication is. In
many practical sensor scenarios, maximizing the energy efficient in
a single sensor node may not be sufficient. Therefore, we continue
to work on the routing problem to maximize the number of delivered
packet before the network becomes useless. The useless network is
characterized by the disintegrated remaining network. We design a
class of energy efficient routing algorithms that explicitly takes
the connectivity condition of the remaining network in to account.
We also present the distributed asynchronous routing implementation
based on reinforcement learning algorithm. This work can be viewed
as distributed connectivity-aware energy efficient routing. We then
explore the advantages obtained by doing cooperative routing for
network lifetime maximization. We propose a power allocation in the
cooperative routing called the maximum lifetime power allocation.
The proposed allocation takes into account the residual energy in
the nodes when doing the cooperation. In fact, our criterion lets
the nodes with more energy to help more compared to the nodes with
less energy. We continue to look at the problem of cooperation
enforcement in ad-hoc network. We show that by combining the
repeated game and self learning algorithm, a better cooperation
point can be obtained. Finally, we demonstrate an example of
channel-aware application for multimedia communication. In all case
studies, we employ optimization scheme that is equipped with the
resource and environment awareness. We hope that the proposed
resource and environment aware optimization framework will serve as
the first step towards the realization of intelligent sensor
communications
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