Proficient Path Optimization by Fusion of Intelligent Water Drop and Ford-Fulkerson's Algorithm in VANET Milieu

 
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                                858
                                                                                             Volume 3, Issue 5, August 2014

       Proficient Path Optimization by Fusion of Intelligent Water Drop and
                   Ford-Fulkerson’s Algorithm in VANET Milieu
                        P.Dharanyadevi1, R.Preethi2, G.M.Suriyaakumar2 and K.Venkatalakshmi3
               1
                Department of Information and Technology, Anna University Villupuram Campus, Villupuram, India
                                                Email: dharanyadevi@gmail.com
                 2
                   Department of Database Systems, Indian Institute of Information Technology, Tiruchirapalli, India
       3
         Department of Electronics and Communication Engineering, Anna University Tindivanam Campus, Tindivanam, India

ABSTRACT

The rapidly fluctuating and impulsive nature of                   algorithm, several artificial water drops cooperate to
Vehicular Ad-hoc NETwork (VANET) pose a wide                      change their environment in such a way that the optimal
range of challenges such as efficient routing, load               path is revealed as the one with the lowest soil on its
distribution, congestion avoidance, collision avoidance           links. The solutions are incrementally constructed by the
and energy consumption, etc. Despite, a number of                 Intelligent Water Drops algorithm [5]-[8]. Consequently,
existing routing protocols provide effective routing and          the Intelligent Water Drops algorithm is generally a
packet collision avoidance in VANET, very few fulfil              constructive population-based optimization algorithm.
the need or provide a plausible solution for network              In ad-hoc network, a number of different paths with
management. The proposed blend algorithm targets to               varying levels of node capacity and energy may be
develop a similar routing protocol in VANET by fusion             available for a source to transmit data to the destination.
of Intelligent Water Drop and Fulkerson‟s Algorithm.              But not all the routes are capable of providing the same
The blend algorithm provides a probabilistic multi-path           level of quality of service [5]. Many routing protocols
routing algorithm and fits in specific path phenomenon            have been experience problems during the distribution of
which constantly updates the goodness of choosing a               nodes for communication between source and
particular path based on packet collision avoidance in            destination [9]-[11]. In order to solve these issues, this
addition to the optimal path.                                     paper introduces the concept of fusion of intelligent
                                                                  water drop algorithm and the ford fulkerson`s algorithm
Keywords - VANET, Intelligent Water Drop routing,                 and it is named as blend algorithm. The blend algorithm
Ford Fulkerson’s Routing, Blend Algorithm, Path                   provides both the optimal path and the augmenting path
Optimization, Performance.                                        for better data transmission.

  I.    INTRODUCTION                                              The rest of the paper is organised as follows. Section-II
                                                                  presents the related work. Section-III discusses the
Vehicular Ad-hoc Network (VANET) is a technology                  proposed blend algorithm. Section-IV analyses and
that uses moving cars as nodes in a network to create a           compares the blend algorithm with the existing
mobile network [1]-[3]. VANET consists of vehicles                algorithm. Section-V concludes the paper.
equipped with wireless routers and a human machine
interface that acts as a display monitor for business              II.     RELATED WORKS
services [4]. Hamed Shah et al. proposed Intelligent
Water Drops algorithm. The algorithm has been tested              The most popular of meta-heuristic algorithms for the
with artificial and standard TSP problem. Intelligent             packet transmission includes Genetic Algorithm (GA),
Water Drops is a swarm-based bio-inspired optimization            Tabu Search (TS), Simulated Annealing (SA), Ant
algorithm. Intelligent Water Drops algorithm has been             Colony Optimization (ACO) and Particle Swarm
stimulated from natural rivers and the algorithm is used          Optimization (PSO) [12]-[20]. Liang Huang et al.
to find the best optimal paths from source to destination.        proposed an efficient dynamic routing protocol in
These optimal paths follow from actions and reactions             VANET. Dynamic route optimization algorithm
occurring among the water drops and the water drops               effectively continues to optimize the path. In VANET
with their riverbeds. In the Intelligent Water Drops              the topology changes caused by nodes may lead to the
                                                                  occurrence of link failure and redundant links. With the

                                                      www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                              859
                                                                                           Volume 3, Issue 5, August 2014

topology changes, the algorithm selects the optimal route          Table 1: Comparison between Existing and Proposed
to bring up to date the transmission path periodically,                                   Model
thereby reducing the number of hops and delay, avoiding                Existing Model             Proposed Model
restarting the route discovery, saving control traffic and        BUFE-MAC protocol         The proposed Blend
energy cost [22]. Korkmaz et al proposed a cross-layer            focuses only on           algorithm focuses on
protocol called controlled vehicular Internet access              collision avoidance.      collision avoidance and also
(CVIA) for vehicular Internet access on highway                                             efficient routing.
applications. The proposed protocol divides the time into         During data transfer all During data transfer only
slots and the service area of the gateway into segments.          segments are traversed. optimal paths are traversed.
The uplink and downlink Internet accesses are achieved            As packets are            The number of hops is
by connecting to the same gateway in a multi-hop                  transferred through       relatively low when
manner using different channel. Kun Yang and others               segments, number of       compared to BUFE-MAC.
proposed another cross-layer protocol, called                     hops will be more.
Coordinated External PEer Communication (CEPEC) for               Time delay is             Time delay is reduced due
Internet access services and peer to peer communications          considerably high.        to optimal path.
in VANETs. The objective of CEPEC is to increase the              Packet transfer           Efficient packet transfer
end-to-end throughput while providing a fairness                  efficiency is less.       compared to the existing
guarantee in bandwidth usage among road segments. To                                        model.
achieve this goal, the road is logically partitioned into
segments of equal length. A relaying head is selected in        III.    PROPOSED BLEND ALGORITHM
each segment that performs both local-packet collecting
and aggregated packets relaying. The CEPEC protocol
                                                                Blend algorithm is the fusion of Intelligent Water Drop
provides higher throughput with guaranteed fairness in
                                                                (IWD) and Ford-Fulkerson‟s (FF) Algorithm. IWD is
multi-hop data delivery in vehicular networks when
                                                                used to find the shortest path and FF is used to find the
compared with the purely IEEE 802.16-based protocol
                                                                non-augmenting path. The IWD algorithm was designed
[1].
                                                                to virtualize the properties of natural water drops. The
Li-Ling Hung et al. proposed BUFE-MAC protocol
                                                                water flow path consists of ‘N’ number of vehicles,
which supports mesh-backbone-based mode and
                                                                where ‘N’ is the maximum number of vehicles from
infrastructure mode. The mesh-backbone-based mode
                                                                source to destination. Each and every water drop is
allows vehicles to transmit packets in a multi-hop
                                                                assumed to carry an amount of soil (packets).
manner, whereas the infrastructure mode supports
                                                                Depending upon of the water drop movement, the soil
vehicles to directly exchange data with a gateway. In
                                                                might be increased or decreased.
order to avoid collision, the segment length is defined as
rcom/2, where rcom/2 is the maximal length to which the
                                                                Network Model of Blend Algorithm: The amount of soil
vehicles can communicate with each other. The vehicles
                                                                (Packet) from source (u) to destination (v) is represented
located at a distance of rcom cannot transmit packets,
                                                                as P(u,v), where P denotes Packet. The velocity of the
thereby resulting in reduced collisions and increase in
fairness to each vehicle [22].                                  packet transfer through the path is represented as V wd .
                                                                                                      pv
                                                                    V (t + 1) = V (t)+                                 (1)
Table 1 depicts the comparison between existing (BUFE                                       qv  rv ( Pt ( u , v ))
MAC protocol) and proposed (Blend algorithm). BUFE-
MAC protocols focus only on collision avoidance (Li-            As expressed in the Eq.1 V wd (t+1) denote the updated
Ling Hunget al., 2012). The proposed algorithm focuses          velocity of the water drop at next vehicle v. p v , q v and
on collision avoidance along with efficient routing. Time       r v are constant parameters for problem calculation. An
Delay is considerably high in existing system when
compared to proposed system. During data transfer all           empirical function Em is considered for the problem to
segments are traversed in existing system but in                measure undesirability of a water drop to move from one
proposed system only paths are traversed.                       vehicle to another. The time consumed by the water drop
                                                                with velocity V wd to move from vehicle u to v is
                                                                represented by time (u, v; V wd (t+1)), is calculated as,

                                                      www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                                 860
                                                                                               Volume 3, Issue 5, August 2014

                                   Em( u ,v )                       As expressed in the Eq.8, f(P(u,v)), computes the inverse
             time (u, v; V ) =                          (2)         of the P between the vehicle‘u’ and ‘v’.
                                  max(  ,V wd )
                                                                                          1
In the above Eq.2 the parameter ɛ is a positive value.              f (Pt (u,v))=                                         (8)
The value of ɛ=0.001. The function Em(u,v) denotes the                             m  h( Pt (u , v))
heuristic undesirability of moving vehicle „u’ to                   The parameter ɛ m is a small positive integer to avert a
vehicle‘j’. As expressed in the Eq.3, with respect to the
                                                                    possible division by zero. The value of ɛ m =0.01.g(P(u,
path optimization Em(u, v) is represented Em op as                  v)) is used to shift the P(u,v) on the path connecting ‘u’
follows,                                                            and ‘v’ towards progressive values and is calculated by
  Em(u, v) = Em op (u, v)= ‖s(u)-s(v)‖                 (3)          Eq.9.
Here s(k) represents the two dimensional positional                            g(P(u,v))= P (u,v) if min wd
                                                                                                            P (u,l)≥0
                                                                                                      lvc
vector for the city road ‘k’. The function Em(u, v)
calculates the Euclidean norm. When two vehicles                           else, g(P (u,v))= P (u,v ) - min
                                                                                                          wd
                                                                                                             ( P (u,l))(9)
                                                                                                       lvc
„u’and ‘v’ are near to each other, the empirical                    vc wd represents the nodes that the water drop should not
undesirability measure Em(u, v) becomes small which                 visit to keep satisfied the conditions. A function should
minimizes the time taken for the water drop to pass from            be considered to measure the approximation of the
vehicle‘u’ to vehicle ‘v’. When the water drop moves                solution.
from one vehicle to another, it carries an amount of soil
with it. The amount soil carried is inversely proportional          The best solution B m is calculated by n( B m ).The
to the time taken by the water drop to reach the                    algorithm completes one iteration when all water drops
destination. Therefore, a fast water drop takes away                have found their solution. As expressed in the Eq.10, the
more soil from the riverbed. This indicates the more soil
                                                                    best solution denoted as B bs , is found by
it carries the velocity of the water drop is higher. This is
what happens in natural rivers, fast moving rivers carry             B bs = arg  min    (n (B m ))                      (10)
                                                                                  B m
more soil while slow rivers lag.
As expressed in the Eq.4, the amount of packet carried              The best solution is the shortest path from source to
are calculated by,                                                  destination is found using the IWD algorithm.The
                                                                    augmenting path is the path with maximum flow in a
                             ps
 P(u, v) =                                             (4)         network. The best non-augmenting path is determined by
                 qs  rs .time( u ,v ,V wd )                        Ford-Fulkerson (FF) algorithm. The Ford - Fulkerson
Here ∆P(u, v) is the amount of Ptremoved by the water               algorithm is also iterative. Fig. 1 represents the working
drop moving from vehicle ‘u’ to ‘v’. The „ps’, „qs‟ and             of the proposed blend algorithm. The blend algorithm is
‘rs’ are constant velocity parameters. As expressed in the          used to find the best optimal path from source to
Eq.5, once the water drops move between vehicle u and               destination devoid of packet collision loss.
v, P between them is reduced by,                                    Blend algorithm consists of five phases are,
P(u, v)=ρ o . Pt (u, v) - ρ n .∆ P(u, v)               (5)               Static parameter initializing phase
                                                                         Dynamic parameter initializing phase
Here ρ o and ρ n are positive numbers chosen between
                                                                         Global soil updating phase
zero and one. As expressed in the Eq.6, the water drop                   Possible paths iterating phase
that has moved from vehicle u to v, increases the packet                 Update the global best solution
transfer P wd by:
P wd = P wd +∆ Pt (u, v)                               (6)          The five phases play an imperative role in solving the
The important property of IWD is to select a path with              vehicle routing problem.
less amount of soil (packet transfer) than other paths.
This is done by passing on a probability to each vehicle            Static Parameters Initializing Phase: During this phase
other than the current vehicle. As expressed in the Eq.7,           of routing, the static parameters of the process are being
the probability p uwd (v) i.e. water drop travelling from           initialized. The static parameters are constant throughout
vehicle u to v is calculated by:                                    the routing. The static parameters include Vehicle Name,
                                                                    Vehicle ID, and Port No etc.
                    f (( u , v ))
p uwd (v)=                                               (7)
             kvc( wd ) f ( Pt ( u , w ))

                                                         www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                                                861
                                                                                                           Volume 3, Issue 5, August 2014

Dynamic parameter initializing phase: The second                   Step 4: This step updates all possible shortest paths
phase initializes the dynamic parameters. The dynamic              found using the IWD algorithm in the routing table.
parameter includes edge selection, location and range.
The edge selection with reference to graph but it is               Step 5: In Step5, from the available paths we select the
actually the selection of vehicle via which the                    optimal non-augmenting path.
transmission of request occurs.
                                                                   Step 6: The total best solution is updated to the vehicle
 Local Soil Updating: This phase updates all possible              in comparison with the local path updating and the non-
shortest paths found using the IWD algorithm in the                augmenting path iterated.
routing table. Since it is only the half way through the
algorithm, it is known as the local soil updating.

Possible Path Iteration Phase: In Blend algorithm, we
focus to find the optimal non-augmenting path. The
possible path iteration is done to identify the non-
augmented paths. So that it makes easy for efficient
transmission of data without any compression. During
this phase the possible paths between source and
destination would be found and the optimal path is
chosen.

Updating the total best solution: Finally the total best
solution for the particular vehicle is being updated. The
total best solution is updated as a fusion of both the IWD
and FF.

Once the iterations are complete, for the optimal shortest
path using intelligent water drop algorithm, we go for
Ford-Fulkerson algorithm to find the optimal non-                                                                               DESTINATION
augmenting path. We combine these algorithms to
provide the users with shortest and non-augmenting                                     SOURCE
path. We prefer the path which is shortest and also least
utilized to make user`s data transmitted efficiently.
The method starts initially with a flow equal to zero. The                          STATIC PARAMETERS
run time of the algorithm O(E(|f*|) , here f* is the
maximum flow. The running time of the Ford-Fulkerson
                                                                    ROUTING TABLE

algorithm depends on the choice of the non-augmenting
                                                                                    DYNAMIC PARAMETERS
path. If we do it wrongly the algorithm might even not
stop. If f∗ is small the algorithm finishes fast, but even in
easy cases it might need | f ∗| iterations.
                                                                                    GLOBAL SOIL UPDATING
The algorithm 1 depicts the Blend algorithm, which                                      ( MIN (PATH))
consists of following steps:
                                                                                     POSSIBLE PATH
Step 1: This step includes the static parameters which                               ITERATION (MAX
are constant throughout the routing.                                                    (ENERGY))

Step 2: This step includes dynamic parameter                                           UPDATE THE                          BEST SOLUTION PATH
initialization, in which parameters could be changed                                   TOTAL BEST
throughout the routing.                                                                 SOLUTION

 Step 3: IWD algorithm determines the shortest path
from source to destination.                                                                Fig 1: Hybrid IWD and FF Architecture

                                                         www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                                  862
                                                                                                 Volume 3, Issue 5, August 2014

            Algorithm 1: Blend Algorithm                              IV.    RESULT ANALYSIS
Static parameter initialization:
                                                                     This section swot up the performance of the blend
 The graph of the problem contains Ns vehicles
                                                                     algorithm against the existing BUFE - MAC algorithm
 The best solution B m is initially set to worst case :             in terms of packet collision ratio, average packet
n (B m ) = - ∞                                                       transmission delay time and packet delivery ratio.To
 Velocity updating parameters are                                   conjure up the pragmatic situation the Network
                                                                     Simulator (NS-2) Ver.2.35 is used for simulation. Table
p v = r v = 1 and q v = 0.01.                                        2 illustrate the simulation parameters.
 Soil updating parameters are p s ,q s and r s .
                                                                     Table 2: Simulation parameters
Here, The p s = r s = 1 and q s = 0.01.
 The local soil updating parameter is ρ .                            Parameter Type                Parameter Value
 Here, ρ n = 0.9, except for the AMT, which is ρ n = − 0.9.           Operating System              Linux
     While (termination condition are not met) do                     Network size                  300 m _ 300 m
Dynamic Parameter Initialization:                                     Simulator tool                NS-2 Version
                                                                                                    2.35
Solution construction by IWDs.                                        No. of nodes (vehicles)       200
                                                                      No. of transceiver            200
    i)   Edge selection
                                                                      Maximum vehicle speed         60-140 km/h
    ii) Local soil updating                                           Transmission Range            300 m
    iii) Find the iteration best solution                             Node placement                Uniform
                                                                      Service Class                 Real time
                                                                      Packet size                   2312 bytes
Find the iteration-best solution B bs from all the solutions
                                                                      MAC layer                     IEEE 802.11
found by the IWDs using
                                                                      Total input load              0~3000 packets/s
B bs = arg   min            m)                                        No. of concurrent events      3–10
                     n (B
             B m
Where, function n (.) gives the quality of the solution.
Global soil updating:
Possible Paths Iteration:
For each vehicle (u, v) in G
 f (u,v) = f(v,u) = 0
While ϶ path p from s to t in residual network Gf
 Do sf (p) = min {sf (u, v) : (u, v) is in p}
  For each edge (u, v) on p
 f (u,v) = f(u,v) + sf (p)
 f(v,u) = -f(u,v)
 Find the optimal path
Update the total best solution :
 Return the best solution.
END
                                                                                   Fig 2: Packet collision Ratio

                                                           www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                          863
                                                                                        Volume 3, Issue 5, August 2014

                                                                V.     CONCLUSION AND FUTURE WORK

                                                                  In this paper, we proposed a hybrid probabilistic
                                                              multi-path routing algorithm and it is named as Blend
                                                              algorithm. Blend algorithm constantly updates the
                                                              goodness of choosing a optimal path based on packet
                                                              collision avoidance in addition to shortest-path metrics
                                                              thereby solving the vehicle routing problem and packet
                                                              loss. Blend algorithm is a swarm-based optimization
                                                              algorithm. Intelligent drops in IWD were able to find the
                                                              optimal solutions in many difficult benchmark instances.
                                                              With the use of FF algorithm local search heuristics we
                                                              improve the solution quality. We compare the proposed
                                                              blend algorithm with the existing BUFE-MAC. The
              Fig 3: Average Delay Time                       comparison results indicate that proposed Blend
                                                              algorithm consumes minimum number of variables and
                                                              provides optimal path in minimal time with fewer packet
                                                              collision. Further we have shown through experiments
                                                              that the performance of proposed blend algorithm mostly
                                                              depends on the number of drops (vehicles) and the
                                                              optimal/minimum number of iterations. Finally we
                                                              conclude that the solution quality of Blend algorithm
                                                              improves when the values of these variables increase. As
                                                              future work, we intend to enhance the performance of
                                                              blend algorithm by introducing privacy preserving and
                                                              data dissemination in VANET to optimize the solution
                                                              quality.

                                                                                   REFERENCES
                  Fig 4: Success Rate                         [1]    Iman Kamkarl., Mohammad-R., Akbarzadeh., and
                                                                     Mahdi Yaghoobi I., “Intelligent Water Drops a
As illustrated in the Fig.2 the packet collision ratio               new optimization algorithm for solving the Vehicle
increases when the vehicle density increases. The packet             Routing Problem,” IEEE International Conference
collision ratio of blend algorithm is lesser than the                on Systems Man and Cybernetics (SMC), 2010, pp.
BUFE-MAC. Consequently the packet loss will be low                   4142-4146.
in blend algorithm.                                           [2]    Jamal Toutouh., Jos´e Garc´ıa-Nieto., and Enrique
As showed in the Fig.3 the average delay time increase               Alba., “Intelligent OLSR Routing Protocol
when the total input load increases. The average delay               Optimization for VANETs,” IEEE Transaction on
time of BUFE-MAC is higher than the proposed blend                   Vehicular Technology, Vol. 61, No. 4, May 2012.
algorithm. The packet transmission delay will be raises       [3]    Hamed Shah-Hosseini., “An approach to
when the number of transmitting hops increases.                      continuous optimization by the Intelligent Water
As depicted in the Fig.4 the success rate of proposed                Drops algorithm,” 4th International Conference
blend algorithm is high when compared to the existing                of Cognitive Science (ICCS 2011), Procedia -
BUFE-MAC. The success rate is premeditated by the                    Social and Behavioral Sciences, Volume 32, 2012,
average ratio of triumphant service replies to the total             Pages 224-229.
number of service request sent during the simulation          [4]    Liang Huang., Fubao Wang., Guoqiang Yan., and
time.From the simulation result it is proven that the                Weijun Duan., “An Efficient Dynamic Route
proposed blend algorithm is efficient than the existing.             Optimization Algorithm for Mobile Ad hoc
                                                                     Networks,” 2011 2nd International Conference on
                                                                     Challenges in Environmental Science and

                                                    www.ijsret.org
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
                                                                                                                         864
                                                                                        Volume 3, Issue 5, August 2014

       Computer Engineering, Procedia Environmental            [15] Del Valle., Y.; Venayagamoorthy., G.K.;
       Sciences, Volume 11, 2011, Pages 518-524.                    Mohagheghi, S.; Hernandez, J.-C.; Harley, R.G.,
[5]    Hamed Shah-Hosseini., “Intelligent water drops               “Particle Swarm Optimization: Basic Concepts,
       algorithm: A new optimization method for solving             Variants and Applications in Power Systems,”
       the multiple knapsack problem,” International                IEEE Transactions on Evolutionary Computation,
       Journal of Intelligent Computing and Cybernetics,            vol.12, no.2, 2008, pp. 171-195.
       Vol. 1 Issue: 2, 2008, pp. 193 – 212.                   [16] Kulkarni., R.V.; Venayagamoorthy., G.K.; Cheng,
[6]    Hamed Shah-Hosseini, “The intelligent water                  M.X., “Bio-inspired node localization in wireless
       drops algorithm: a nature-inspired swarm-based               sensor networks,” IEEE International Conference
       optimization algorithm,” International Journal of            on Systems, Man and Cybernetics, 2009, pp. 205-
       Bio-Inspired Computation (IJBIC), Vol. 1, No. 1/2,           210.
       2009, pp. 71-79.                                        [17] Low, K.-S.; Nguyen, H.A.; Guo, H., “A particle
[7]    Hifza Afaq and Sanjay Saini, “On the Solutions to            swarm optimization approach for the localization
       the Travelling Salesman Problem using Nature                 of a wireless sensor network,” IEEE International
       Inspired     Computing       Techniques,”   IJCSI            Symposium on Industrial Electronics, 2008, pp.
       International Journal of Computer Science Issues,            1820-1825.
       Vol. 8, Issue 4, No 2, 2011, pp. 326- 334.              [18] X. Wang., S. Wang., and J. J. Ma., “An improved
[8]    Haibin Duan., Senqi Liu., Jiang Wu., “Novel                  co-evolutionary particle swarm optimization for
       intelligent water drops optimization approach to             wireless sensor networks with dynamic
       single UCAV smooth trajectory planning,”                     deployment,” Sensors, vol. 7 Issue. 3, 2007, pp.
       Aerospace Science and Technology, Volume 13,                 354–370.
       Issue 8, 2009, pp. 442-449.                             [19] Davoud Sedighizadeh., and EllipsMasehian.,
[9]    Shilpi Gupta., Shweta Bhardwaj., and Parul Kalra             “Particle     Swarm      Optimization    Methods,
       Bhatia., “A Reminiscent Study Of Nature Inspired             Taxonomy and Applications,” International
       computation”, International Journal of Advances              Journal of Computer Theory and Engineering,
       in Engineering & Technology, Vol. 1,Issue 2,2011,            Vol. 1, No. 5, 2009, pp. 1793-820.
       pp. 117-125.                                            [20] Khan, M.K.; Nyström, I., “A Modified Particle
[10]   Pries, R.; Staehle, D.; Stoykova, M.; Staehle, B.;           Swarm Optimization Applied in Image
       Tran-Gia, P., “Wireless Mesh Network Planning                Registration,” 20th International Conference on
       and Optimization through Genetic Algorithms,”                Pattern Recognition (ICPR), 2010, pp. 2302-2305.
       Second International Conference on Advances in          [21] Yun-Sheng Yen., Han-Chieh Chao., Ruay-Shiung
       Mesh Networks, 2009, pp. 55-61, 18-23.                       Chang., and Athanasios Vasilakos., “Flooding-
[11]   Yoon, C.; Hwang-Bin Ryou, “A Genetic                         limited and multi-constrained QoS multicast
       Algorithm for the Routing Protocol Design of                 routing based on the genetic algorithm for
       Wireless Mesh Networks,” 2011 International                  MANETs,”        Mathematical     and    Computer
       Conference on Information Science and                        Modelling, Volume 53, Issues 11–12, , 2011, pp.
       Applications (ICISA), 2011, 26-29.                           2238-2250.
[12]   Bin Yu., Zhong-Zhen Yang., Baozhen Yao., “An            [22] Li-Ling Hung., Chih-Yung Chang., Cheng-Chang
       improved ant colony optimization for vehicle                 Chen., Yu-Chieh Chen., “BUFE-MAC: A Protocol
       routing problem,” European Journal of                        With Bandwidth Utilization and Fairness
       Operational Research, Volume 196, Issue 1, 2009,             Enhancements        for     Mesh-Backbone-Based
       pp. 171-176.                                                 VANETs,” IEEE Transactions on Vehicular
[13]   Xuemei Song., Bing Li., and Hongmei Yang.,                   Technology, vol.61, no.5, 2012, pp. 2208-2221.
       “Improved Ant Colony Algorithm and its
       Applications in TSP,” Sixth International
       Conference on Intelligent Systems Design and
       Applications, Vol. 2. IEEE Computer Society,
       Washington, DC, USA, 2006, pp. 1145-1148.
[14]   B. He, B. Xie, and D. P. Agrawal, “Optimizing
       deployment of Internet gateway in Wireless Mesh
       Networks,” Computer Communications, vol. 31,
       no. 7, 2008, pp. 1259–1275.

                                                     www.ijsret.org
You can also read
Next slide ... Cancel