# 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 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 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 )) 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

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