08-08-2012, 12:54 PM
Wireless Sensor Network Path Optimization Based on Particle Swarm Algorithm
[attachment=30553]
INTRODUCTION
After entering the information age, people are living in a
modern society where a variety of networks are closely
related. The computer information network, telephone
communications network, transport network, energy and
logistics distribution network and other network groups
infiltrate all aspects of life. The purpose of network
optimization is to study how to effectively plan, manage and
control the network, so as to maximize the social and
economic benefits. Network optimization is a classic
operations research and important branch of the research
question involves many areas, one hand is how to maximize
the savings, such as the shortest path problem, minimum cost
problems; the other hand, how to maximize its benefits when
there are limited resources in the network cases, such as the
maximum flow problem, optimal resource allocation
problems. How to select the network of wireless sensor
network applications in efficient, accurate and rapid path can
provide high quality services to the key factors. How to use
the energy efficiently and rationally, as much as possible to
extend the network lifetime, has become one of the core
issues of sensor networks.
WIRELESS SENSOR NETWORK PATH OPTIMIZATION
Wireless Sensor network (WSN) which is made by the
convergence of sensor, nested computation, networks and
wireless communication technologies, is a novel technology
about acquiring and processing information. It is a latemodel
wireless networks of infrastructure less network and
can be used for testing, sensing, collecting and processing
information of monitored objects. WSN is a new research
area of computer science and technology and has a wide
application future more and more attention.
PARTICLE SWARM OPTIMIZATION
PSO is a population-based optimization algorithm,
modeled after the social behavior of flocks of birds. Each
particle is an individual and the swarm is composed of
particles. The problem solution space is formulated as a
search space. Each position in the search space is a
correlated solution of the problem. In a PSO system, each
particle is “flown” through the multidimensional search
space, adjusting its position in search space according to its
own experience and that of neighboring particles.
CONCLUSION
The approach takes a novel kind of optimization
algorithm, i.e., PSO optimization algorithm, to train wireless
network sensor network. The feasibility and effectiveness of
this new approach is validated and illustrated by the
experiments. The diagnosis results show the PSO-WSN has
better training performance, better convergence behavior, as
well as better diagnosis ability than GA-WSN. Owning to
the flexibility of this approach, it can be adapted to other
problems very easily.
[attachment=30553]
INTRODUCTION
After entering the information age, people are living in a
modern society where a variety of networks are closely
related. The computer information network, telephone
communications network, transport network, energy and
logistics distribution network and other network groups
infiltrate all aspects of life. The purpose of network
optimization is to study how to effectively plan, manage and
control the network, so as to maximize the social and
economic benefits. Network optimization is a classic
operations research and important branch of the research
question involves many areas, one hand is how to maximize
the savings, such as the shortest path problem, minimum cost
problems; the other hand, how to maximize its benefits when
there are limited resources in the network cases, such as the
maximum flow problem, optimal resource allocation
problems. How to select the network of wireless sensor
network applications in efficient, accurate and rapid path can
provide high quality services to the key factors. How to use
the energy efficiently and rationally, as much as possible to
extend the network lifetime, has become one of the core
issues of sensor networks.
WIRELESS SENSOR NETWORK PATH OPTIMIZATION
Wireless Sensor network (WSN) which is made by the
convergence of sensor, nested computation, networks and
wireless communication technologies, is a novel technology
about acquiring and processing information. It is a latemodel
wireless networks of infrastructure less network and
can be used for testing, sensing, collecting and processing
information of monitored objects. WSN is a new research
area of computer science and technology and has a wide
application future more and more attention.
PARTICLE SWARM OPTIMIZATION
PSO is a population-based optimization algorithm,
modeled after the social behavior of flocks of birds. Each
particle is an individual and the swarm is composed of
particles. The problem solution space is formulated as a
search space. Each position in the search space is a
correlated solution of the problem. In a PSO system, each
particle is “flown” through the multidimensional search
space, adjusting its position in search space according to its
own experience and that of neighboring particles.
CONCLUSION
The approach takes a novel kind of optimization
algorithm, i.e., PSO optimization algorithm, to train wireless
network sensor network. The feasibility and effectiveness of
this new approach is validated and illustrated by the
experiments. The diagnosis results show the PSO-WSN has
better training performance, better convergence behavior, as
well as better diagnosis ability than GA-WSN. Owning to
the flexibility of this approach, it can be adapted to other
problems very easily.