Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network

Journal of Bionic Engineering 11 (2014) 490–496

Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network Hao Dong, Xiaohui Zhao, Liangdong Qu, Xuefen Chi, Xinyu Cui College of Communication Engineering, Jilin University, Changchun 130000, P. R. China

Abstract This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network (VANET). A novel bionic swarm intelligence algorithm, which is called ant colony algorithm, was introduced into a traditional ad-hoc route algorithm named AODV. Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units, the parameters in ant colony system were modified to enhance the performance of the route selection probability rules. When the vehicle moves into the range of several different roadsides, it could build the route by sending some route testing packets as ants, so that the route table can be built by the reply information of test ants, and then the node can establish the optimization path to send the application packets. The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm, especially when the vehicle is in higher speed or the number of nodes increases. Keywords: multi-hop routing optimization, ant colony algorithm, VANET, bionic swarm intelligence algorithm Copyright © 2014, Jilin University. Published by Elsevier Limited and Science Press. All rights reserved. doi: 10.1016/S1672-6529(14)60061-5

1 Introduction With the rapid development of automotive electronics, more and more electronic equipments are adopted in automotive applications. A wide variety of the applications which are aimed to improve road safety or provide comfort for passengers in vehicles are intended to be delivered in Vehicle Ad-hoc Networks (VANET) in the near future. Almost all these applications require vehicles to access a remote network server through the roadside wireless equipments, such as road lamps and gas stations[1,2]. The VANET is characterized by high node speed, which rapidly changes topologies, multi hop, and self-organization. As a matter of fact, the VANET can be envisioned as a mobile platform for road traffic monitoring, which will replace more roadside sensor network devices. The vehicles can use the roadside gateway to connect the data server and collect the data which regard the local observed traffic, such as the state of highway in bad weather, real-time traffic information; even GPS map information updated online and so on. Every driver can collect data and share the Corresponding author: Liangdong Qu E-mail: [email protected]

necessary information with the public server through the roadside infrastructure. As a part of the Intelligent Transportation System (ITS), the Vehicle to Infrastructure (V2I) contains a few stationary access points (Road-Side Units, RSUs) which could provide discontinuous Internet connection and make the access to ITS servers available. So it is important for the vehicles to get access to the RSUs outside of whose transmission range with multi-hop communications. In this case, the data packets from these vehicles must be forwarded by some vehicles which can connect with the RSU single hop. We define the communications between vehicle and vehicle as V2V and the communications between vehicle and infrastructure or roadside units as V2I/V2R. The structure of the system is illustrated in Fig. 1[3–5]. There are a number of methods which deal with multi-hop communications in VANET, such as Ad hoc On-demand Distance Vector routing (AODV)[6], Destination-Sequenced Distance-Vector routing (DSDV) and so on[7]. These route protocols provide the solutions to the route selection problems, but there are also some

Dong et al.: Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 491

Fig. 1 Vehicle to roadside communications.

aspects which should be improved[8,9]. For instance, the AODV protocol can periodically broadcast HELLO packets to maintain routing when a certain link is broken down, and the node sends the ERROR packet to repair the link or delete the route table. However, some invalid routes can not be found within the time period. Furthermore, when a new ad hoc topology network begins to be rebuilt, many Route Request (RREQ) broadcast packets will be sent to find a route from the source to the destination, which may be not the most optimal routing. In addition, a large number of broadcast packets reduce the bandwidth utilization ratio. Some new protocols were proposed to improve the performance. For example, combined with DSR and AODV protocols, a new protocol was designed to reduce the rebuilt time when the moving nodes disconnected from the VANET. However, the other performances were not improved. In order to lower the end-to-end delay in different traffic density models, the multi-hop broadcast protocol was put forward. There are also different protocols which were designed to improve different performances in VANET, such as Floor Acquisition Multiple Access with Nonpersistent Carrier Sensing (FAMA-NCS) protocol, Greedy Perimeter Stateless Routing (GPSR)[10,11]. The FAMA-NCS protocol can eliminate the effect of hidden transmitting terminal but occupy more channel capacity. The GPSR protocol may increase the number of the hops and the transmission delay. An increasing number of researches make use of the embedded auxiliary devices to help the route protocol to determine switching time. In this paper, we propose a new protocol which takes advantage of the Ant Colony Optimization (ACO)

algorithm to improve the AODV route selection in V2I environment. This algorithm can use the information of the vehicle movement direction and GPS information to calculate the status of vehicle in the next period of time. In order to obtain a higher convergence rate by the information, we change the ACO algorithm heuristic information, so the modified ACO can make the AODV algorithm find an optimized route, and also can increase the stability of VANET with position information. The ACO algorithm is a kind of probabilistic search algorithm based on artificial ants, and it can solve the problem of route selection. The key of the convergence of ACO is the probability of the first selection, so a position information parameter is important in the algorithm to decrease the execution time and quicken the velocity of convergence. The rest of the paper is organized as follows. In section 2, some basics of ant colony system are briefly explained. In section 3, we present the proposed PIACO-AODV algorithm, and describe the implementation of the new route protocol. In section 4, the results of simulation experiment with NS-2 platform are discussed. Finally, this paper will draw the conclusion in section 5.

2 Ant Colony Optimization ACO algorithm is biologically inspired from the behavior of colonies of real ants, and in particular how they forage for food. This algorithm proposed by Dorigo M and co-workers in 1996 is a novel heuristic evolutionary optimization algorithm[12]. It can point out a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. ACO has been applied to many problems, e.g. Traveling Salesman Problem (TSP), Sequential Ordering Problems (SOP), and Open Shop Scheduling (OSS) problem. The ACO algorithms are the effective ways to solve the feature selection problems[13–16]. The ants in ACO algorithm can be represented as some special packets in the vehicle ad-hoc network, and we configure some rules according to the algorithm for the packets. These packets can be sent in Internet Protocol level. As shown in Fig. 2[17–20], A is defined as the start point of ants, and D is defined as the food that ants must get. The line BC is defined as the obstacle from A to D;

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B

B

D

A

⎧ [τ ij (t )]α [ηik (t )]β ⎪ pijk (t ) = ⎨ ∑ j∈I k [τ is (t )]α [ηis (t )]β ⎪ ⎩ 0

A

D C

C (a)

(b)

B B

D

A

A C

C

(c)

(d)

Fig. 2 Ant finding food behavior.

so we can define the DABD and DACD are the distances between A and D along with different routes RABD and RACD. At time t=0, an ant in node A moves towards to B or C with a probability given by the random proportional rule. We assume DABD>DACD, so if we define the ant makes the random choice to select the two routes with the probability of 0.5 (Fig. 2a), and there is no pheromone at first. When the ant chooses the route, it will leave pheromone according to the distance of its journey. The longer the route, the less the pheromone left by the ant. How to dispose pheromone is a positive feedback mechanism which could attract more ants so that more pheromones are disposed on the shorter path. However, the evaporation of pheromone is a negative feedback to reduce the pheromone strength. At time t = 1, other ants start to move from A to D. When these ants meet the choice point, they will choose the route which has more pheromone. As shown in Fig. 2b and Fig. 2c, after a period of time, because we defined the distance DABD>DACD, more and more ants will choose the route RACD for the reason that it has more pheromone than route RABD. With the time consumption (e.g. t = 2, t = 3…), the ants from A to D will choose the route RACD with higher and higher probability. Finally, as shown in Fig. 2d, all of the ant colony will find the food through the route RACD. So the ant colony system can be established as follows. The probability pijk (t ) of ant k in node i moving to node j at time t can be defined as state transition rule[18]

if j ∈ I k

(1)

,

else

where Ik is the neighborhood node set which ant k can choose at next step, and it does not include the nodes already visited by ant k. The parameter τij(t) is the intensity of pheromone left by passing ants at time t, and ηij(t) is the expectation that ant k moves from node i to node j, as shown in Eq. (2). dij is the distance from node i to node j; both α and β are above zero, and α is the information heuristic factor and β determines the importance of heuristic information[18]

ηij (t ) =

1 . dij

(2)

After all nodes are traversed by every ant once, the pheromone on the way from i to j can be updated according to the following equation[18] N

τ ij (t + 1) = (1 − ρ ) ⋅ τ ij (t ) + ∑ Δτ ijk (t ),

(3)

k =1

where ρ is the pheromone evaporation rate and ρ∈ (0, 1). N is the number of ants at each iteration. Δτij(t) is the increment of pheromone from node i to node j of the route in one cycle, and Δτ ijk (t ) represents the information of ant k on route from i to j. In ant-cycle model, it can be updated as[18]

⎧Q ⎪ Δτ ijk (t ) = ⎨ Lk ⎪0 ⎩

if ant k pass i to j in Δt

,

(4)

else

In this model, Δt is a limited time interval. Q is a predefined constant, which means the intensity of pheromone; Lk is the length of route which ant k arrives in one cycle.

3 PIACO-AODV 3.1 Modified ACO algorithm In order to modify the AODV with ACO algorithm, the location information of roadside devices and vehicles are set as the heuristic information when the first route is selected at the beginning or the route is reconnected. According to the coordinates of both the roadside device and vehicle, and the direction of the vehicle movement, we can assume the probability of the route selection. The spatial relationship of vehicle and the

Dong et al.: Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 493

the range of the roadside unit. The new equation is shown as

ηij (t ,θ ) =

1 Dij sin θ

0 <θ <

π 2

,

(6)

where Dij can be defined as the length of point B to point A, in other words, it is the distance from the vehicle and the first roadside unit. It can be obtained by Di j = ( X i -X j )2 + (Yi -Y j ) 2 .

Fig. 3 The spatial relationship between vehicle and roadside unit.

roadside unit can be considered to determine the start time of route selection (Fig. 3). We call this modified ACO algorithm Position Information Ant Colony Optimization (PIACO). We define the Vehicle and Roadside unit as nodes, so some of them can move, and the packets send from nodes which are used to probe the route can be defined as the ants. As shown in Fig. 3, where A is defined as the roadside unit position and B is the position where signal strength is above the threshold when the vehicle passes through. The direction of vehicle is from B to C, and LBC is considered as the distance of vehicle moving in time Ts. Position C is the place where the strength of the signal can not be detected by the vehicle. So we can assume that θ = ∠ABC = ∠ABD − ∠CBD, point D is the true north of point B, so that we can easily get the degree of the angle. The dotted circle represents the range of the roadside unit in theory. If a vehicle passes through between two or more roadside units, it can get different θ with different roadside units. So the probability of the first route selection can be set with parameter θ. According to Eq. (1), we can get the new state transition rule ⎧ [τ ij (t )]α [ηik (t ,θ )]β ⎪ pijk (t ) = ⎨ ∑ j∈I k [τ is (t )]α [ηis (t ,θ )]β ⎪ 0 ⎩

if j ∈ I k

, (5)

else

where the heuristic function η, indicating the degree of heuristic information, is taken into consideration by the artificial ant in choosing the path. η can be defined with parameter θ, and the range of θ is (0, π/2]. If the θ is greater than π/2, it means the vehicle is moving out of

(7)

Eq. (6) and Eq. (7) show that two factors can decide the probability of the ant route selection, i.e. the length of distance and the point where the ant will access in a short time in the future. So the pheromone on the path is derived as N

τ ij (t (θ ) + 1) = (1 − ρ ) ⋅ τ ij (t (θ )) + ∑ Δτ ijk (t (θ )),

(8)

k =1

t(θ) is the cycle time determined by the direction and speed of vehicle. Δτij(t(θ)) is the increment of pheromone at cycle time t(θ), which can be decided as

⎧Q if path is chosen in t (θ ) ⎪ Δτ (t (θ )) = ⎨ Lk . ⎪0 else ⎩ k ij

(9)

In this model, the time t(θ) is not a certain value, and it can be changed by vehicle. 3.2 PIACO algorithm in AODV How to find the optimal path to avoid the routing handoff if possible is an important problem in the VANET. So we propose the modified PIACO-AODV algorithm to improve the performance of the packet delivery fraction, end-to-end average delay, and routing overhead. The algorithm flowcharts are shown in Fig. 4 and Fig. 5. Fig. 4 is the flowchart of source node initiating route discovery. It starts when the vehicle moves into the range of the roadside unit. As shown in Fig. 4 and Fig. 5, the main steps of the PIACO-AODV algorithm are explained as below. Step 1: Initialize the parameters in the system at the start time, such as the initial pheromone, the speed and the direction of vehicle, the source node and destination node, and other necessary information.

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494 Start

Check local route table pheromone

Route to D ( node ) exist? N Send hello to next node

Y

Build link and send data End

Fig. 4 The flowchart of source node initiating route discovery.

Start Node i receives ant(i-1) packet Yes

Pheromone < min (p) No Update route to source

Yes

If node i in route table No

Drop the packets

Update route to source Yes If Node i = D (node) No Yes

If existing route = D (node)

4 Results and discussion

No Send reply to source Send hello to next node

End

Fig. 5 Flowchart of route searching of the middle node. AODV PIACO-AODV

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

10

20

30

40 50 Speed (km·h−1)

According to Eq. (5), every ant should choose one route and pass the nodes one by one. If an obstacle is detected on the way, that is to say, the ant can find several different routes to the destination, node D in Fig. 2. Consequentially, the ant moves randomly by the probability following the rule defined by Eq. (5). Step 3: Update the pheromone on every path after the cycle time. The pheromone on one route can be changed according to Eq. (8). However, if the route is not the chosen one, the pheromone on this route will evaporate with this time as defined by Eq. (9). Step 4: Generate the route for the ants according to Eq. (5). Eq. (5) shows the way to use the heuristic information and pheromone to generate the appropriate route in limited periods. Step 5: Send the optimal route to the source. When the optimal route is found in limited periods, the route will be sent to the source and the route information will be maintained. If the route information is changed, the path should be rebuilt. Step 6: According to Step 5, all the nodes choose the optimal route from the source node to the destination node.

60

70

Fig. 6 The end-to-end average delay of two algorithms at different speeds of node.

Step 2: Start from the source node, and every node begins to choose the route according to the pheromone which is decided by the PIACO-AODV algorithm.

After the model was built, a simulation was carried out to test the performance of PIACO-AODV and the traditional AODV with NS-2. The test field was 1000 m × 1000 m. The number of artificial ants in the colony is 30. The cover range of roadside device is 200 m, and the test time is set to 300 s. CBR is the data source, and every packet is 512 bytes. In order to reflect the better performance of the modified AOC in the experiment, we tried many different combinations of parameters with ρ, α and β. We found that the combination: ρ=0.7, α=1 and β=3, can make the performance of PIACO-AODV better. So we used these parameters for test. The end-to-end average delay at different speeds of the node is tested. The number of nodes is set to 50. The results are shown in Fig. 6. When the speed of node is lower than 40 km·h−1, the performance of end-to-end average delay of AODV and PIACO-AODV is similar. But when the speed is higher than 40 km·h−1, the PIACO-AODV algorithm has better performance than AODV.

Dong et al.: Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 495

The result of packet delivery fraction experiment is shown in Fig. 7. It is seen that, the packet delivery fraction of the PIACO-AODV can perform better at higher speed (>50 km·h−1) than that of AODV. However, they have similar performance at lower speed (<50 km·h−1). Fig. 8 represents the routing overhead of two algorithms at different speeds of node. It is shown that PIACO-AODV algorithm have better performance than AODV. 0.9

AODV PIACO-AODV

0.16 0.12 0.08 0.04 0.00

10

20

30

40

50 60 70 Node number

80

90

100

Fig. 10 The end-to-end average delay of two algorithms with different number of nodes.

0.8 Packet loss rate (%)

0.7 0.6 0.5

AODV PIACO-AODV

0.4 0.3 0.2 0.1 0.0 10

20

30 40 50 Speed (km·h−1)

60

Fig. 11 Relationship between packet loss rate and the number of nodes.

70

Fig. 7 The packet delivery fraction of two algorithms at different speeds of node. 3.2 2.8 2.4 2.0 AODV PIACO-AODV

1.6 1.2 0.8 0.4 0.0

10

20

30 40 50 Speed (km·h−1)

60

70

Packet delivery fraction

Fig. 8 The routing overhead of two algorithms at different speeds of node.

Fig. 9 The packet delivery fraction of two algorithms with different number of nodes.

From Fig. 6 to Fig. 8, we can discover that the performance of PIACO-AODV algorithm is similar to that of AODV algorithm at low speed (less than 50 km·h−1). When the speed is higher than 50 km·h−1, the PIACO-AODV algorithm can make a little better performance than traditional AODV. Therefore, we design another experiment to test the two algorithms at the same speed but with different number of nodes, which indicates that the performance under different traffic densities. We assume that the parameters are all the same except the number of nodes. The speed is 50 km·h−1 in the whole simulation. The packet delivery fraction and the end-to-end average delay of two algorithms are shown in Fig. 9 and Fig. 10, respectively. As shown in Fig. 9 and Fig. 10, we can reach the opinion that with the number of nodes, the PIACO-AODV represents the more stable performance than AODV. In other words, when there are a large number of the vehicles in the road, the PIACO-AODV algorithm can help the vehicles find the route more quickly and maintain the route table more stable. As shown in Fig. 11, the packet loss rate of PIACO-AODV is lower than that of traditional AODV algorithm. When the number of nodes increases from 30 to 50, the packet loss rate of the traditional AODV al-

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gorithm grows rapidly. So the performance of PIACO-AODV algorithm is more stable.

[7]

tion-sequenced distance-vector routing (DSDV) for mobile computers. Proceedings of the Conference on Communica-

5 Conclusion In this paper, we present an improved AODV protocol with ACO algorithm in VANET. The proposed algorithm changes the route selection method in the AODV protocol. By modifying the value of the pheromone evaporation rate, the node could choose the better route in two main route discovery steps with higher possibility. In addition, we employ the position information in the new protocol, so that the node could maintain a more stable routing. The experimental results show that the new protocol has more effective performance of than AODV. By changing the parameters in ACO, the algorithm could find the optimal route more quickly, which enhances the efficiency of the whole system. The results also represent that the new algorithm can reduce the handoff frequency in a given time, improve the routing path duration, and the transmission efficiency of message.

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