Essay: Genetic Algorithm for Energy-Efficient QoS Multicast Routing

Essay details:

  • Subject area(s): Information technology essays
  • Reading time: 12 minutes
  • Price: Free download
  • Published on: September 30, 2015
  • File format: Text
  • Number of pages: 2
  • Genetic Algorithm for Energy-Efficient QoS Multicast Routing
    0.0 rating based on 12,345 ratings
    Overall rating: 0 out of 5 based on 0 reviews.

Text preview of this essay:

This page of the essay has 1809 words. Download the full version above.

Abstract: The consideration of energy consumption in wireless
ad hoc networks prevents the problem of the network exhausting
batteries, thus partitioning the entire network. Power-aware
multicasting is proposed to reduce the power consumption. This
letter presents an energy-efficient genetic algorithm mechanism
to resolve quality of service (QoS) multicast routing problem,
which is NP-complete. The proposed genetic algorithm depends
on bounded end-to-end delay and minimum energy cost of
the multicast tree. Simulation results show that the proposed
algorithm is effective and efficient.
Index Terms’Multicast routing, quality of service (QoS),
mobile ad hoc network (MANET), energy.
I. INTRODUCTION
Amobile ad hoc network (MANET) is a self-configuring
network of mobile nodes, which can form a dynamic
topology. All the nodes cooperatively maintain network connectivity
without the aid of any fixed infrastructure units such
as base stations or access points in advance. Each node has
a routing function whereby it communicates by forwarding
packets via intermediate nodes. If two nodes are within the
transmission range of each other, they communicate directly.
Otherwise, other nodes are needed to forward their packets.
MANET is characterized by non-restricted mobility and easy
deployment, which makes them very promising.
Power awareness is crucial in a mobile wireless network,
particularly in a MANET. Nodes need to reduce their power
consumption to prolong their battery lifetime. Therefore, the
transmission power should be carefully chosen since the large
transmission power level leads to the waste of battery energy.
Several heuristic algorithms for constructing source-based
energy-efficient multicast trees have been developed [1]. Since
most multimedia applications are delay-sensitive, end-to-end
delay should be considered in multicast routing to provide
better QoS. However, energy-efficient multicast routing has
not always considered the delay metric. Furthermore, the
design of quality of service (QoS) multicast routing with
multi-constrained metrics, i.e., multi-constrained minimum
cost multicast problem [2], and degree-constrained least-cost
multicast routing [3], has not always considered the energy
consumption. Therefore, these QoS multicast routing schemes
cannot be directly used in MANETs.
It has been demonstrated that the problem of QoS multicast
routing with multiple QoS constraints is NP-complete [4]. In
the field of artificial intelligence, genetic algorithm is a powerful
tool to solve the NP-complete problem. Although genetic
Manuscript received July 6, 2012. The associate editor coordinating the
review of this letter and approving it for publication was S. De.
The authors are with the Department of Electronic Engineering,
Shanghai Jiao Tong University, Shanghai, 200240, China (e-mail:
[email protected]).
Digital Object Identifier 10.1109/LCOMM.2012.112012.121467
algorithms can be seen as not adequate for supporting delaysensitive
applications in MANETs because they may involve
a large number of iterations, new hardware implementations
of genetic algorithms [5] have shown their ability of fast
computation. In this regard, the proposed genetic algorithm
is quite promising for multicast routing in MANETs.
In this letter, we study the source-based multicast routing
problem using a genetic algorithm. We propose an energyefficient
genetic algorithm to find the delay-constrained multicast
tree and reduce the total energy consumption of the tree.
Experiments have proved that our algorithm is effective and
efficient. The remainder of this letter is organized as follows.
Section II states the network model and problem description.
Section III presents the proposed genetic algorithm and
analyzes its convergence. The performance of the proposed
algorithm is evaluated in Section IV. Section V concludes this
letter.
II. PROBLEM DESCRIPTION
A. Energy Consumption Model
The models of energy consumption for a link between two
nodes are studied in [6]. For a transmission of a unit message,
the model of the minimum energy needed for a link between
nodes vi and vj is Pi,j = k1 (ri,j )??+k2, where ri,j Euclidean
distance between vi and vj , k1 is a constant dependent on the
properties of the antenna, ?? is the path loss exponent that
depends on the propagation losses in the medium, and k2 is
a constant that accounts for the overheads of electronics and
digital processing. Note that we assume that each multicast
session only multicasts a unit length message.
B. Network Model and Problem Description
We assume that each node in a MANET determines the
distance between itself and its neighbor nodes using some
distance estimation method [7]. The connectivity of the network
depends on the transmission power of each node. Each
node can dynamically change its transmission power level. A
node can use a different power level for each multicast tree in
which it participates. All nodes use omni-directional antennas.
Every node vi in the network has two coverage areas: (1)
control coverage area (CRi); (2) data coverage area (DRi),
where DRi ‘ CRi. These coverage areas depend on the
transmission power selected by node vi to transmit its control
and data packets, respectively.
According to the control coverage area of each node,
a MANET can be modeled as a graph G(V,E), where
V = {v1, v2, …, vn} is a set of nodes (mobile hosts) and
E = {(i, j)|vi, vj ‘ V } is a set of links. (i, j) ‘ E
indicates that vi and vj are within the control coverage area
1089-7798/13$31.00 c 2013 IEEE
32 IEEE COMMUNICATIONS LETTERS, VOL. 17, NO. 1, JANUARY 2013
of each other. Each link (i, j) is associated with a delay
di,j and a distance li,j . di,j describes the data transmission
delay between vi and vj , which includes queuing delay and
propagation delay. li,j denotes the Euclidean distance between
vi and vj. Both di,j and li,j are positive real numbers.
Let s ‘ V be a multicast source and D ‘ V ‘ {s} be
a set of destinations. A multicast tree T (s,D) ‘ G is a tree
rooted at s and reaching all of the destinations in D. The delay
of a path on T from s to a destination vt ‘ D, denoted as
delay(pT (s, vt)), is delay (pT (s, vt)) =

(i,j)’pT (s,vt) dij .
Then, the delay-constrained minimum Steiner tree problem is
to find a minimum cost multicast tree T ‘ (s,D) such that
delay (pT’ (s, vt)) ‘ ??, ‘vt ‘ D, where ?? is the overall
allowable delay from s to a destination vt ‘ D. Once
T ‘ (s,D) is found, each node on T ‘ adjusts its transmission
power properly to transmit data packets along the tree.
III. PROPOSED ALGORITHM
A. Coding
The representation of candidate solutions is critical for
designing a well-performed genetic algorithm. A number of
representations for a tree, such as one-dimensional binary code
[8], Pr??ufer numbers [9] and sequence and topology encoding
(ST encoding) [10], have been developed. However, these
representations are likely to generate illegal trees (e.g., ST
encoding), or have poor locality (e.g., Pr??ufer numbers), or
have low efficiency that the required search space increases
remarkably with the increase of the network size (e.g., onedimensional
binary code). Recent studies on network optimization
[11] avoid these problems by directly manipulating
trees, i.e., using a data structure of a tree to describe the
chromosome. With this method, a tree directly represents a
chromosome. Therefore, the coding/decoding operations are
omitted. In our study, we use the tree structure coding method,
in which a chromosome represents a multicast tree directly.
B. Initial Population
Two issues should be considered in the process of population
initialization: (1) population size Np; (2) the method
of population formation. Np is set by the system. In the proposed
algorithm, the formation of initial population (random
multicast Steiner trees) is based on the random depth-first
search algorithm [12]. The searching process begins at s and
randomly selects an unvisited node for next visit. This process
terminates when all destinations have been visited.
C. Fitness Function
The fitness function should reflect the individual performance:
the ‘good individual’ has bigger fitness than the ‘bad
one’. The definition of the fitness function is as follows:
f (T) =
a
cost (T )

vt’D
??(delay (pT (s, vt)) ‘ ??) (1)
where
cost (T) =

vi’T
cTi
=

vi’T
b[k1(r

i)?? + k2] (2)
and
??(Z) =

1, if Z ‘ 0,
??, if Z > 0.
(3)
a is the positive real weighting coefficient. ?? is the maximum
allowable delay from s to vt, where vt ‘ D. cos(T ) is
the energy cost of tree T . ??(??) is a penalty function. The
value ??(0 < ?? < 1) determines the degree of penalty: the
smaller the value of ??, the higher the degree of penalty. In our
experiments, we set ?? = 0.5. This letter reduces the energy
consumption of a multicast tree to maximize the network
service time. In Eq. (2), cTi
is the energy cost of vi, b is
a positive real coefficient, and r
i is the maximum distance
between vi and vj, where vj ‘ B(vi). B(vi) is the set of
immediate succeeding nodes of vi on T . Note that the energy
cost of leaf nodes is zero. Particularly, we set k1 = 1, k2 = 0,
b = 1 and ?? = 2 in our experiments.
D. Selection of Parents
In the proposed genetic algorithm, an elitist model is
adopted as the selection operator. First, we select the best
individuals and directly copy them to the next generation.
Then, we select the rest by the roulette wheel selection model.
The probability for selecting a parent Ti, denoted as p(Ti), is
given by:
p(Ti) =
f(Ti)

Np
j=1 f(Tj)
(4)
E. Crossover Scheme
Based on the roulette wheel selection, a pair of chromosomes
is selected as the parents to produce a single offspring.
Let Ta and Tb be the selected parents. The crossover operator
generates a child Tc by identifying the same links between Ta
and Tb, and retaining these common links in Tc. According
to the definition of fitness function, the ‘better’ individual
has higher probability of being selected as a parent. Thus,
the common links between two parents are more likely to
represent the ‘good’ traits. However, retaining these common
links in Tc may generate some separate sub-trees. Therefore,
links are needed to be selected to connect these sub-trees into
a multicast tree.
The process of connecting separate sub-trees is as follows.
First, two separate sub-trees are randomly selected among
these sub-trees. Then, the selected sub-trees are connected by
the least-delay path to form a new sub-tree. The connecting
process repeats until a multicast tree is constructed. In order
to find the least-delay path between two sub-trees, we add two
nodes. One node is connected to all of the nodes of one subtree
with links which have zero delay associated with them.
Similarly, the other node is connected to all the nodes of the
other sub-tree with zero-delay links. Hence, the least-delay
path between two sub-trees is the least-delay path between
the two added nodes. Clearly, there are no routing loops in
the multicast tree with this connecting method. An example
of crossover procedure is shown in Fig. 1. The same links of
Ta and Tb are retained in Tc. Then, all sub-trees are connected
with least-delay paths which are denoted as dot lines in Tc.
LU and ZHU: GENETIC ALGORITHM FOR ENERGY-EFFICIENT QOS MULTICAST ROUTING 33
1
3
8
2
4
9
11
5
12
7
6
13
15
14
10
3,21
3,21
6,7
6,14
7,10
3,20
1,13
4,11
3,24
4,4
5,2
3,5
11,7
1,38
3,21
5,3
4,15
3,22
3,2 3,5
4,6
4,35
1
3
8
2
4
9
11
5
12
7
6
13
15
14
10
3,21
6,14
7,10
3,20
1,13
4,11
3,24
4,4
5,2
3,5
11,7
1,38
3,21
5,3
4,15
3,22
3,2 3,5
4,6
4,35
1
3
8
2
4
9
11
5
12
7
6
13
15
14
10
3,21
6,14
7,10
3,20
1,13
4,11
3,24
4,4
5,2
3,5
11,7
1,38
3,21
5,3
4,15
3,22
3,2 3,5
4,6
4,35
3,21
3,21
6,7
6,7
source node
destination node
(delay, distance)
Ta Tb
Tc
Fig. 1. Example of crossover operation.
Proposed_GA (G, s, D)
{
1. for (i=1; i<= Np; i++) {
2. Chromosome(i) = RandomDFS(G, s, D);
}
3′ for (j=1; j<=Ng; j++) {
4. select the best individuals and copy them into the next
generation;
5. for (k=1; k<=Np-Noptimal; k++) {
6. Ta=MSTSelect (Chromosome)
7. Tb= MSTSelect (Chromosome)
8. Tc=Crossover(Ta, Tb);
9. if (rand() < pm)
10. Mutation(Tc);
}
}
11. Select the best individual and output it;
}
Fig. 2. The pseudo code of the proposed genetic algorithm.
F. Mutation
When a new offspring is produced, the mutation operation
is performed according to the mutation probability pm. First,
mutation procedure randomly selects a subset of nodes and
breaks the multicast tree into some separate sub-trees by
removing all the links that connect these selected nodes and
their farthest child node on T . Then, it re-connects these
separate sub-trees into a new multicast tree with least-delay
paths. Fig. 2 shows the pseuso code of the proposed genetic
algorithm. As shown in Fig. 2, RandomDFS() denotes
random depth-first search algorithm, Ng is the number of
generations, Noptimal is the number of the best individuals.
G. Analysis of convergence
According to the Theorem 2.7 in [13], the proposed algorithm
could finally converge to the global optimal solution.
For a large-scale network, it is time-consuming to obtain
the optimal solution to this NP-complete problem. This can
be overcome by setting an appropriate iteration time for the
genetic algorithm. In this way, we can obtain a near-optimal
solution within a reasonable time limit.
IV. EXPERIMENT
We have implemented the proposed genetic algorithm in
MS VC++ 6.0 using the genetic algorithmlib [14] which is
0 2 4 6 8 10 12 14 16 18 20
15
18
20
25
30
35
generations
d
e
la
y
proposed algorithm
delay bound 18
0 2 4 6 8 10 12 14 16 18 20
1000
2000
3000
4000
generations
c
o
s
t
proposed algorithm
LDT
Fig. 3. Convergence process of the proposed genetic algorithm with ?? = 18.
a C++ Library of Genetic Algorithm. The experiments were
performed on a PC with Pentium Dual-core 2.5GHz CPU
(2 GB memory). Preliminary tests suggest that the mutation
probability pm = 0.05, the crossover probability pc = 1, and
the population size Np = 15 performed well. The proposed
algorithm is compared with the least delay multicast tree
algorithm (LDT). Among all the delay-constrained multicast
routing algorithms, LDT has the highest successful ratio
because it connects the source and each destination with the
least delay path. The successful ratio (SR) of an algorithm is
defined as the number of requests successfully routed divided
by the total number of routing requests. When the multicast
tree constructed by the algorithm satisfies the delay constraint,
the routing request is considered as successfully routed one.
A. Results for Fixed Network
These experiments mainly test the convergence ability and
involve the deterministic, weighted network topology (15
nodes) depicted in Fig. 1. Node 1 is the source and nodes 3,
6, 7, 9, 15 are the destinations. Fig. 3 shows the convergence
processes of the proposed algorithm under ?? = 18. As shown
in Fig. 3, the proposed algorithm can converge to the solution
satisfying the delay bound and having low cost quickly (i.e.,
after eight generations). It is seen that the cost of the proposed
algorithm is small than that of LDT.
B. Results for Random Networks
In this section, we verify that the previous result holds for
all kinds of networks (networks scales). The simulation studies
involve random networks with 20-100 nodes. The distance of
each link is uniformly distributed in [10,200] and the delay
of each link in [0,50]. The maximum allowable delay is
uniformly distributed in [30,160]. For each request, the source
and destinations are randomly generated. MANETs can be
used in applications including military battlefield (e.g., moving
platoon or company), rescue missions, conference room and
so on. These applications involve networks with sizes that
range from small to medium, e.g., tens of nodes. Simulations
reflect this practical reality. For larger networks, some cluster
based schemes can be used. These experiments mainly test
34 IEEE COMMUNICATIONS LETTERS, VOL. 17, NO. 1, JANUARY 2013
20 40 60 80 100
0.2
0.4
0.6
0.8
1
number of nodes
S
R
proposed algorithm
LDT
Fig. 4. Comparison of SR between the proposed algorithm and LDT.
20 30 40 50 60 70 80 90 100
10,000
20,000
30,000
40,000
50,000
60,000
70,000
number of nodes
c
o
s
t
LDT
proposed algorithm
Fig. 5. Comparison of average cost between the proposed algorithm and
LDT.
20 30 40 50 60 70 80 90 100
1
2
3
4
5
6
7
8
9
10
number of nodes
ru n
n
i
n g t i
m
e
(s
)
proposed__GA
LDT
Fig. 6. Comparison of running time between the proposed algorithm and
LDT.
the SR, the average cost, and the running time. The results
are based on 10000 randomly generated routing requests in
each network. The source and destinations of each request are
generated randomly.
Fig. 4 shows the comparison of SR between the proposed
algorithm and LDT. As shown in Fig. 4, it is obvious that
the two algorithms have the same SR. This proves that the
proposed algorithm can find the feasible multicast trees that
satisfy the delay bound if one exists.
Fig. 5 shows the comparison of average cost between the
proposed algorithm and LDT. From the figure, we can see that
the average cost of the proposed algorithm is much smaller
than that of LDT. This proves that the proposed algorithm can
find the multicast trees having low cost.
Fig. 6 shows the running time obtained by the proposed
algorithm and LDT. From Fig. 6 we can see that the running
time of the proposed algorithm is smaller than that of LDT
and grows slowly with the size of the network. Even for the
network with 100 nodes, the running time of the proposed
algorithm is fairly desirable.
V. CONCLUSION
Power awareness is crucial in mobile wireless networks,
particularly in MANETs. Nodes need to reduce their power
consumption to prolong their battery lifetime. In this letter,
we proposed the energy-efficient delay-constrained multicast
routing algorithm. The proposed algorithm is a source-based
algorithm which takes into account energy consumption as
well as end-to-end delay in route selection. The proposed
algorithm applies crossover and mutation operations directly
on trees, which simplifies the coding operation and omits
the coding/decoding process. Heuristic mutation technique can
improve the total energy consumption of a multicast tree. A
series of experiments was performed to verify the convergence
performance, SR and running time of the proposed algorithm.
The results demonstrate that the proposed algorithm is effective
and efficient. This study only focuses on source-based
routing trees. Future works could apply the proposed algorithm
to shared multicasting trees.
REFERENCES
[1] Z. Nutov and M. Segal, ‘Improved approximation algorithms for maximum
lifetime problems in wireless networks,’ Theoretical Computer
Science, vol. 453, no. 28, pp. 88’97, 2012.
[2] M. Molnr, A. Bellabas, and S. Lahoud, ‘The cost optimal solution of the
multi-constrained multicast routing problem,’ Computer Networks, vol.
13, no. 13, pp. 3163’3149, 2012.
[3] S. Y. Tseng, Y. M. Huang, and C. C. Lin, ‘Genetic algorithm for delayand
degree-constrained multimedia broadcasting on overlay networks,’
Computer Commun., vol. 29, no. 17, pp. 3625’3632, Nov. 2006.
[4] Z. Wang and J. Crowcroft, ‘Quality of service for supporting multimedia
applications,’ IEEE J. Sel. Areas Commun., vol. 14, no. 7, pp. 1228’1234,
1996.
[5] F. Ahmadi, R. Tati, S. Ahmadi, and V. Hossaini, ‘New hardware engine
for genetic algorithms,’ in Proc. 2011 ICGEC, pp. 122’126.
[6] L. M. Feeney and M. Nilsson, ‘Investigating the energy consumption of
a wireless network interface in an ad hoc networking environment,’ in
Proc. 2001 INFOCOM, pp. 1548’1557.
[7] W. C. Y. Lee, Mobile Communication Engineering. McGraw-Hall, 1993.
[8] F. Xiang, L. Junzhou, W. Jieyi, and G. Guanqun, ‘QoS routing based on
genetic algorithm,’ Computer Commun., vol. 22, no. 15-16, pp. 1392′
1399, Sep. 1999.
[9] A. T. Haghighat, K. Faez, M. Dehghan, A. Mowlaei, and Y. Ghahremani,
‘A genetic algorithm for Steiner tree optimization with multiple
constraints using Prfer number,’ Proc. 2002 EurAsia-ICT, pp. 272’280.
[10] Y. S. Yen, H. C. Chao, R. S. Chang, and A. Vasilakos, ‘Floodinglimited
and multi-constrained QoS multicast routing based on the genetic
algorithm for MANETs,’ Mathematical and Computer Modelling, vol.
53, no. 11, pp. 2238’2250, 2011.
[11] S. Y. Tseng, Y. M. Huang, and C. C. Lin, ‘Genetic algorithm for delayand
degree-constrained multimedia broadcasting on overlay networks,’
Computer Commun., vol. 29, no. 17, pp. 3625’3632, 2006.
[12] C. P. Ravikunmar and R. Bajpai, ‘Source-based delay-bounded multicasting
in multimedia networks,’ Computer Commun., vol. 21, no. 2, pp.
126’132, Mar. 1998.
[13] C. Guoliang, W. Xufa, Z. Zhenquan, and W. Dongsheng, Genetic
Algorithm and its Application. People’s Posts and Telecommunications
Press, 1996.
[14] M. Wall, GAlib: A C++ Library of Genetic Algorithm Components.
http://lancet.mit.edu/ga.

About Essay Sauce

Essay Sauce is the free student essay website for college and university students. We've got thousands of real essay examples for you to use as inspiration for your own work, all free to access and download.

...(download the rest of the essay above)

About this essay:

This essay was submitted to us by a student in order to help you with your studies.

If you use part of this page in your own work, you need to provide a citation, as follows:

Essay Sauce, Genetic Algorithm for Energy-Efficient QoS Multicast Routing. Available from:<https://www.essaysauce.com/information-technology-essays/essay-genetic-algorithm-for-energy-efficient-qos-multicast-routing/> [Accessed 26-05-20].

Review this essay:

Please note that the above text is only a preview of this essay.

Name
Email
Review Title
Rating
Review Content

Latest reviews: