391_a2_1_x

ticketdonkeyAI and Robotics

Nov 25, 2013 (3 years and 11 months ago)

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Section 1



Supervisor

Hosein Marzi

Information System

Intelligent real
-
time system design



Broad research issue
---

Optimization of Wireless Sensor Network
s


Wireless Sensor Networks

(WSNs)

is a new kind wireless network
which is
composed by

a large number

of sensor nodes
with the purpose of collecting and
processing information from the sensor area
.

(
Nakamura, Loureiro, &
Frery, 2007
)

The features of Wireless Sens
or Networks are widely covered and
dense
.

There
are some defects of a general sensor node which
is low power and
limited
lifetime
. Many applications in different areas have already been implemented
using Wireless Sensor Networks such as animal life monitor
ing, env
ironment
variables measurement and traffic engineering.
As the
frequent

use
s

of WSNs,

to
solve the constrained energy problem is becoming more important and the
problem is related to the factors including device hardware, routing, density
control a
nd location of nodes.
If the optimization of WSNs is focus on the routing
and location, the research will cover some algorithms like genetic algorithms and
Ant Colony Optimization. Therefore, optimizing the factors for Wireless Sensor
Networks is important

to improve the condition of low energy and limited
lifetime.




Reference

Nakamura, E.F., Loureiro, A.F., & Frery, A.C. (2007). Information Fusion for
Wireless Sensor Networks: Method, Models, and Classifications.
ACM Computing
Survey, 39
(3)
, 9/1
-
9/55.








Section 2



Abstract

1.
The optimization problem of Wireless Sensor Networks in this paper is related to
the energy constraint. This paper model
s

three layers
which are the link layer, the
medium access control layer and the routing layer
to identify

the traditional network.
At first, the assumption
of computing

a maximum network lifetime is based on one
layer at a time and the other layers are fixed.
Then
this paper

constructs that the
optimization problem could be solved by cross
-
layer

of Time Division Multiple
Access (TDMA)

and it also gives some examples to illustrate the benefits of
cross
-
layer design
ing
.

2.

This paper constructs a parallel energy
-
efficient coverage optimization mechanism
with maximum entropy clustering to solve en
ergy constraint problem in wireless
sensor networks. First,
all
the sensor nodes are
divided into clusters by maximum
entropy cluste
ring. Second, Dijkstra’s algorithm is used to calculate the
lowest cost
paths inside

each
cluster. Third, swarm optimization

is used to solve the problem of
maximizing the coverage metric and minimizing the energy metric for parallel
clusters.
A tradeoff between coverage rate and energy efficient can be solved in this
paper.

3.

The security level problem in Wireless Sensor Net
works is proposed in this paper.
This paper uses a Bio
-
inspired Trust and Reputation Model to minimize the level of
security for Wireless Sensor Networks (BTRM
-
WSN). BTRM
-
WSN is based on the
Ant Colony System and
the results of testing

the model are accura
te, robust and
scalable.

4.

According to application
-
specific requirements, communication constraints and
energy
-
conservation characteristic, this paper uses genetic algorithm
to solve the
energy
constraint problem.
This proposal of using genetic algorithm is suitable for all
application
-
specific requirements and can solve the communication and energy
constraint problem. This research maximize the lifetime of sensor nodes for all
application
-
specific requirements.

5.

Coverage and detection probability of wireless sensor networks which are included
in dynamic deployment
are the research issue
s in

this paper.

An algorithm which is
called virtual force
-
directed particle swarm optimization (VFPSO) is proposed to
solve the

problem. This algorithm is the combination of virtual force (VF) and particle
swarm optimization (PSO).The results demonstrate

that VFPO can solve dynamic
deployment problem more efficiently than PSO.



Explaining

1.
This research is going to solve the ene
rgy
-
constrained sensor nodes problem and it
compares the different results between single layer optimization and cross
-
layer
optimization. This paper divided the network system through physical level and the
results indicate that
cross
-
layer of
TDMA

can so
lve the optimization problem better.
This research combines the physical method and TDMA schedule to minimize the
cross
-
layer power and maximize the cross
-
layer lifetime.

2.
This paper is going to solve the energy constraint problem and a parallel
energy
-
e
fficient coverage optimization mechanism with maximum entropy clustering
is proposed. The combination of parallel optimization and
maximum entropy
clustering is efficient to solve the energy problem in wireless sensor networks.

3.
This paper uses bio
-
insp
ired technique to minimize the level of security. The
technique is called BTRM
-
WSN and it is based on Ant Colony System
. The model
can improve the trust and reputation of WSNs in most cases. Therefore, the level of
security problem can be solved efficientl
y using this model.

4.
As higher energy consumption for communication purpose, this paper uses genetic
algorithm to minimize energy consumption for application
-
specific requirements. This
research efficiently extend the lifetime

of the sensor nodes.

5.
This paper proposes the virtual force
-
directed particle swarm optimization (VFPSO)
has been proposed as a practical approach for dynamic deployment. Particle swarm
optimization (PSO) is combined with virtual force
-
directed (VF)

to direct the
movement of pa
rticle and to control the impact of virtual force. VFPSO is better than
VF and PSO and can efficiently solve the dynamic deployment problem.



Bibliography

1
.
Madan,R.,Cui,S.,Lall,S.,& Goldsmith, A.J.(2007). Modeling and Optimization of
Transmission Schemes
in Energy
-
Constrained Wireless Sensor Networks.
IEEE/ACM
Transactions on Networking, 15
(6), 1359
-
1372.

2. Wang,X., Ma,J., & Wang, S. (2009). Parallel energy
-
effcient coverage optimization
with maximum entropy clustering in wireless sensor networks. Jaurnal

of Parallel and
Distributed Computing, 69,

838
-
847.

3.
Marmol,F.G., & Perez,G.M. (2010). Providing trust in wireless sensor networks
using a bio
-
inspired technique
.
Springer, 46,
163
-
180.doi:
10.1007/s11235
-
010
-
9281
-
1

4.
Ferentinos,K.P., &
Tsiligiridis,T.A. (2007). Adaptive design optimization of
wireless sensor networks using genetic algorithms.
Computer Networks, 51
,
1031
-
1051.

5.
Wang,X., Wang,S.,& Bi,D. (2007). Virtual

Force
-
Directed Particle Swarm
Optimization for Dynamic Seplyment

in Wireless Sensor Networks.
Spinger
-
Verlag,

292
-
303.




Subtopic

Wireless Sensor Network

using Ant Colony Optimization




Abstract

1.
The optimization problem of Wireless Sensor Networks (WSNs) in this paper
considers the power balanced coverage time for
clustered WSNs. The advantage of
clustering is decreasing energy consumption and the disadvantage is increasing cluster
heads’ communication burden. Deterministic setups and stochastic setups are the two
methods to solve the optimization problems. Stochast
ic setups can be divided into two
parts which are a routing
-
aware optimal cluster planning and a clustering
-
aware
optimal random relay and they can balance power consumption.

2.
Prolonging the sensor’s network lifetime in the Wireless Sensor Networks (WSNs
)
is the main point of this paper. The paper considers a movement
-
assisted sensor
deployment problem in a cluster
-
based WSNs. Based on this mobility assumption for
sensors, the other sensors can low battery energy and increase the overall network
lifetime
by relocating their positions. This paper compares traditional
movement
-
assisted deployment methods with proposed sensor relocation methods.
The latter one can not only solve the coverage hole problem, just like traditional
methods, but also increase the n
etwork lifetime significantly of the resulting WSNs.

3.

Using supervised learning techniques to deal with challenges in Wireless Sensor
Networks (WSNs) is proposed in this paper. A supervised learning framework can
produce useful information and make decis
ions in sensor network. It can be applied to
other problems which could benefit from information discovering. This research
provides a new direction to routing optimizations for deploying the wireless sensor
networks.


4.
Maximizing the coverage time by ba
lancing the power consumption of different
cluster heads (CHs) is a major optimization problem of Wireless Sensor Networks
(WSNs). Two mechanisms are proposed for achieving balanced power consumption.
One is the routing
-
aware optimal cluster planning and t
he other one is the
clustering
-
aware optimal random relay. The research demonstrates that both
mechanisms maximize the power consumption of CHs. The optimization problems
are formulated as signomial optimizations and they are solved efficiently. This
resea
rch demonstrates that the two schemes substantially prolong the coverage time of
the network.

5.
Maximum lifetime routing in Wireless Sensor Networks (WSNs) is one of the
optimization problems. In wireless sensor network where nodes operated on limited
bat
tery energy, it is important to utilize the energy efficiently. In this paper the routing
problem is formulated as a linear program to maximize the network lifetime. The
research demonstrates that the newly proposed routing algorithm can maximize the
syste
m lifetime.



Bibliography

1.
Shu,T.,&

Krunz,M.(2010).Coverage
-
Time Optimization for Clustered Wireless
Sensor Networks: A Power
-
Balancing Approach.
IEEE/ACM Transaction on
Networking, 18
(1), 202
-
215.

2.
Wang,C.F., & Lee,C.C. (2010). The optimization of sens
or relocation in wireless
sensor mobile sensor networks.
Computer Communications, 33,

828
-
840.

3.
Szynkiewicz,E.N.,& Marks,M.(2009). Optimization for Schemes for Wireless
Sensor Network Localization.
Int.J.Appl.Math.Comput.Sci,19
(2), 291
-
302.

4.
Xie,H.,
Zhang,Z.,&Feng,N
. (2010)
. A Novel Routing Protocal in Wireless Sensor
Networks based on Ant Colony Opyimization.
International Journal of Intelligent
Information Technology Application, 3
(1), 1
-
5.

5. Chang,J.H.,&Tassiulas,L.(2004). Maximum Lifetime Routin
g in Wireless Sensor
Network.
IEEE/ACM Transactions on Networking, 12
(4), 609
-
619.




Title

Ant Colony Optimization for solving routing

problem in Wireless Sensor Networks