Proficient Path Optimization by Fusion of Intelligent Water Drop and Ford-Fulkerson's Algorithm in VANET Milieu
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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
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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,
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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
lvc
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)
lvc
„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)
kvc( wd ) f ( Pt ( u , w ))
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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
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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
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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.
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