Delay Constraint Adaptive Routing Based on Seed

Journal of Computational Information Systems 8: 12 (2012) 5137–5148 Available at http://www.Jofcis.com

Delay Constraint Adaptive Routing Based on Seed Spray in Opportunistic Networks ?

Jia XU 1,2,3,4,5,∗, 1 College

Ruchuan WANG 1,2,3 ,

Lijuan SUN 1,2,3

of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

2 Jiangsu

High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China

3 Key

Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education Jiangsu Province, Nanjing 210003, China

4 Key

Laboratory for Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 211189, China 5 State

Key Lab. for Novel Software Technology, Nanjing University, Nanjing 210093, China

Abstract Opportunistic networks are sparse wireless networks which have no complete path from the source to the destination most of the time. Many applications require the support of delay constrained routing mechanism which can provide acceptable and resilient services in the challenged environments. We proposed Adaptive Seed Spray (ASS) to achieve the delay constraint with low cost in dynamic opportunistic networks. ASS will estimate the routing cost in source node, and use a small number of seed nodes to spray message copies in order to reduce redundant copies. Furthermore, ASS will make spray decisions in relay nodes which have the latest information of the networking conditions. Simulation results have shown that ASS has prominent superiority in routing cost and adaptability, and it is an efficient delay-bounded multi-copy routing protocol for opportunistic networks especially for dynamic conditions. Keywords: Opportunistic Networks; Delay-constraint; Adaptive; Spray Routing

? The subject is sponsored by the National Natural Science Foundation of P. R China (No. 61100199, 61100213, 60973139, 60903181, 61003039, 61003236), Scientific and Technological Support Project (Industry) of Jiangsu Province (No. BE2010197, BE2010198), the Natural Science Foundation for Higher Education Institutions of Jiangsu Province (10KJB520013, 10KJB520014, 11KJA520001, 11KJB520014), China Postdoctoral Science Foundation funded project (20110491453), Jiangsu Planned Projects for Postdoctoral Research Funds (1101126c), talents project of Nanjing University of Posts and Telecommunications (NY210077, NY208021), the Open Research Fund from the Key Laboratory for Computer Network and Information Integration (Southeast University), Ministry of Education, China (K93-9-2010-12, K93-9-2010-13) , Science fund of Huaian (HAG2010049) and the Open Research Fund from the State Key Lab. for Novel Software Technology (Nanjing University) (KFKT2011B20). ∗ Corresponding author. Email address: [email protected] (Jia XU).

1553–9105 / Copyright © 2012 Binary Information Press June 15, 2012

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J. Xu et al. /Journal of Computational Information Systems 8: 12 (2012) 5137–5148

Introduction

Opportunistic networks fall into the general category of Delay Tolerant Networks (DTN). Studies have shown that flooding-based schemes [1, 2] have the least delay in point-to-point delivery, but wasting a lot of energy and suffering from severe contention. Some single-copy routing protocols such as MobySpace [3] and SECMR [4], are energy efficient, but always have large delay. Spray and Wait [5] is a multi-copy routing scheme based on restricted flooding which intends to balance the delay and energy. Spray and Focus [6] replaced the direct transmission at wait phase with utility-based single-copy strategy and proposed the utility transfer mechanism to disseminate the history contact information. Thrasyvoulos was absorbed in utility-based spraying and proposed three potential utility functions [7]: Last-Seen-First Spraying, Most-Mobile-First Spraying and Most-Social-First Spraying. Jindal proposed distance utility-based spray strategy [8] which utilized dynamic programming to calculate the optimal relay node. An adaptive distributed spray mechanism (AMR) was performed in [9] which determined the depth of spray tree by relay nodes. But AMR is only efficient in random waypoint model and can produce at most 0.5M copy redundancy (M is the number of nodes). In addition, theoretical analysis of expected delay in single-copy case and multi-copy case were proposed in [10] and [11] respectively.

Fig. 1: Seed spray tree with m=2, n=8 The above-mentioned spray mechanisms all assume that the decisions of the number of copies and the mode of spray are made by the source node. Actually, it is very difficult to obtain accurate network parameters in many opportunistic networks especially the applications with time-varying network environment. Therefore, it is necessary to propose an adaptive spray mechanism which can be dynamically adjusted in accordance with network environment. An effective method is utilizing the relay nodes to decide the spray depth independently. Obviously, the relay nodes have the latest information of the networking conditions than the source nodes, so their estimations of the delivery delay are more accurate. This paper introduced Adaptive Seed Spray (ASS) routing to achieve the target delay with low cost in dynamic circumstances. The main features of ASS are: (1) providing rational judgment of user-configuration delay constraint and prediction of routing cost, (2) utilizing a small number of seed nodes to spray copies, minimizing redundant copies, thus reducing the routing cost, (3) using relay nodes to spray, perceiving the change of network environment instantly and having great adaptability.


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In the next section we present the details of ASS. Section 3 presents the performance analysis of ASS. Simulation results are presented in Section 4, where the performance of all the strategies are compared with respect to message delivery delay and the number of transmissions per message delivered. Finally, Section 5 is the conclusion.

2

Adaptive Seed Spray

2.1

Basic principles

Seed : the token of the node for forwarding message copy. In traditional spray mechanism, all of the nodes which received more than one copies can spray further, while spray operation was only authorized in the nodes with seed(s) in ASS. Seed Spray : the source node creates L message copies and K seeds at the beginning. Each node A which has m seeds (K ≥ m > 0) and n message copies (L ≥ n > 1) will give bm/2c seeds to the next potential relay node B which does not have seeds and the same copy, and A will reserve dm/2e seeds in itself. At the same time, node A disseminates the copies according 1 m > 1, forwards bn/2c copies to node B and reserves dn/2e copies in to the following cases: ○ 2 m = 1, forwards one copy to node B and reserves n − 1 copies in itself. Finally, every itself, ○ node which has only one copy will transmit directly to the destination. The process of Seed Spray with m=2, n=8 is depicted in Figure 1. Theorem 1: The number of nodes which received message copies is given by formula 1 (where K denotes the number of seeds, H denotes the current spray depth):

( Nc =

Nc1 (H) = 2H−1

K > 2H−2

Nc2 (H, K) = K(H − blogKc − 1) + 2blogKc K < 2H−2

(1)

Proof: When K > 2H−2 , all the nodes which received copies have the seeds after spray at H-1 layer. This means the number of children at H layer is 2H−1 which is equal to the number of Binary Spray. When K < 2H−2 , Seed Spray will increase K children (nodes which have copies) after spray at H-1 layer. In this case, the number of children is the sum of the number at H-1 layer and the increased number after H-1 layer. Since the maximum of H satisfy K > 2H−2 is blogK +2c, the maximum of H-1 is blogK +1c, and the number of children at H-1 layer is 2blogKc . The increased children after H-1 layer is K(H − blogK + 1c). Putting them altogether, we get that the number of nodes which received copies is Nc = K(H − blogKc − 1) + 2blogKc . It must be noted that ASS assumes that all nodes move according to some stochastic mobility model, whose “meeting times” are approximately exponentially distributed or have an exponential tail with expected meeting time equal to EMT. For the realization of ASS, we add P acket → H (current depth of spray), P acket → Seed (the number of seeds), P acket → CurSeed (the number of seeds in current node) and P acket → Dt (target delay) in the head of bundles.

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2.2 2.2.1


Core algorithm Seed allocation algorithm

Seed Allocation Algorithm (SAA) examines the rationality of target delivery delay derived from application in the source node. If the target delivery delay can be satisfied, the source node calculates the minimum of seeds in order to satisfy the delay constraint. The prediction of Routing Cost can also be obtained from SAA. If not, SAA will refuse to provide routing services. For specific networks, some harsh delay constraint can not be satisfied even using optimal algorithm. In fact, there are some works which attend to calculate optimal expected delay EDopt with specific mobility model and parameters. For example, EDopt is relevant to the network torus, network scale and transmission range in Random Walk [12], while EDopt is relevant to average relative velocity of nodes in Random Waypoint [13]. The superiority of rationality judgment is that network can refuse the application with harsh target delivery delay. This mechanism helps to reduce unnecessary resource consumption. On the other hand, prediction of Routing Cost for reasonable delay constraint will provide feedback to users. This mechanism helps users to grasp the expectation of QoS. Therefore, SAA can provide acceptable and resilient services in the challenged environments. The following upper bound holds for the expected delay of ASS. Theorem 2: denote K as the number of seeds, N as the depth of spray, M as the number of nodes, EDASS as the expected delay of ASS, then we can get the following conclusions: (1)K > 2N −2 : EDASS 6

N −1 X

EM T (M − Nc1 (N )) EM T + Nc1 (H)(M − Nc1 (H)) (M − 1) Nc1 (N ) H=1

(2)

(2)K < 2N −2 : blogK+2c

EDASS 6

X H=1

EM T + Nc1 (H)(M − Nc1 (H))

N −1 X H=blogK+3c

EM T + K(M − Nc2 (H, K))

(M − Nc2 (N, K)) EM T (M − 1) Nc2 (N, K)

(3)

Proof: Suppose that, ASS doesn’t carry out Wait Phases in spray phases. Therefore, the expected delay of ASS is the sum of spray delay and wait delay. According to Theorem 1, the number of nodes which received copy is Nc1 (H) at layer H when K > 2N −2 . This means the next spray EM T will disseminate the copies from Nc1 (H) to M −Nc1 (H), and the required time is Nc1 (H)(M . −Nc1 (H)) PN −1 EM T Repeat above step until N th layer, and the total spray delay is H=1 Nc1 (H)(M . If the des−Nc1 (H)) tination is not among the Nc1 (N ) − 1 nodes which have received the copies in spray phases, wait T c1 (N ) phases will be started with probability of 1 − Nc1M(N−1)−1 = M −N . The wait delay is NEM since M −1 cl (N ) the meeting times are exponentially distributed with EMT. Note that, we assume ASS doesn’t carry out wait phases in spray phases. Therefore the sum of spray delay and wait delay is the upper bound of the expected delay of ASS. We can calculate the upper bound when K < 2N −2 by using similar approach.


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Fig. 2: The upper bound of ED calculated by Theorem 2 with different K and N

Figure 2 shows the upper bound of expected delay of ASS with different N and K. We can see that the upper bound decreased with increasing K under fixed N. Theorem 2 has shown that the number of message copies is an increasing function of K. Therefore, the upper bound must decrease if more message copies are added to network. Theorem 2 provides the method of calculating the minimum of seeds and the corresponding depth of spray. Since the upper bound omits the case in which the destination is found in spray phases, the smaller the spray depth is, the more accurate the estimation of K and N for fixed M is. This indicates that the precision of K and N estimated by Theorem 2 is in relation with the N . We present the pseudo-code of SAA in Figure 3. value of M Assuming that, there are 100 nodes which move according to Random Walk model in area E = 106 with transmission range R=10(the method to calculate EMT in Random Walk model can be found in [10]). Figure 4 shows the minimum of seeds and the corresponding depth of spray to achieve the target delay calculated by SAA.

Fig. 3: The pseudo-code of SAA

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Fig. 4: The minimum of seeds and the corresponding depth of spray to achieve the target delay

2.2.2

Seed spray algorithm

Seed Spray Algorithm (SSA) is a distributed spray algorithm which can be used to decide the spray depth dynamically based on the knowledge of current node, and disseminate seeds with binary mode. As a packet has spent some time (referred as Dcur that derived from the timestamp of packet) to reach a relay node, the Estimated Residual Delay (referred as Drw ) must be small enough to satisfy

Dcur + Drw < Dt

(4)

where, Dt derived from the head item P acket → Dt in bundles. On the other hand, it is easy to T from Theorem 2. We can get Nc through P acket → H and P acket → Seed deduce Drw = EM Nc according to Theorem 1. So we can decide whether to spray again using formula 4. If it is necessary to spray, the relay node will disseminate seeds with binary mode. We present the pseudo-code of SSA in Figure 5. In addition, the network parameters used in ASS such as the transmission range, area and scale of network may be mutative in dynamic opportunistic networks. So it is necessary to estimate M and EMT in order to enhance the adaptability of ASS. We use the samples of inter-meeting time, IMT to estimate M and EMT which was proposed in [11]. Now, we summarized the process of ASS: Spray Phases: the source node initializes K seeds at the beginning (K > 1). Each seed node A will decide whether to create a copy of the message based on Estimated Residual Delay. If necessary, such nodes will forward the copy to the next potential relay node B which does not have the same copy and disseminate seeds with binary mode. Repeat the process until node A makes sure that it is not necessary to create another copy or node A encounters the destination D. Wait Phases: the node which received copy can only perform direct transmission if (1) it is not a seed node or (2) the node makes sure that it is not necessary to spray again.


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Fig. 5: The pseudo-code of SSA

3

Performance Analysis of Adaptive Seed Spray

In this section, we analyze the performance of ASS. As a comparison, let’s present ABS (Adaptive Binary Spray) first. Adaptive Binary Spray: each node A that has copies will forward a new copy to next potential relay node B which does not have the same copy. Continue this process until node A makes sure that it is not necessary to create another copy or node A has encountered the destination D. Wait phase: the nodes which received copy can only perform direct transmission if it makes sure that it is not necessary to spray new copy based on spray decision-making algorithm. Every node which received copy decides whether to create a new copy based on formula (4). T in ABS. We can get Drw = EM 2H−1 Routing Cost: the average transmission times for each message. Routing Cost is equivalent to the number of copies disseminated in spray routing mechanisms. Obviously the Routing Cost of Adaptive Binary Spray is 2H−1 at H layer spray. The Routing Cost of Adaptive Seed Spray has been given by Theorem 1. ¯ the average ratio of the number of redundant copies divide Average copy redundancy (σ): by the minimum number of copies to satisfy the delay constraint. Denote σ¯b and σ¯s as the Average copy redundancy of Binary Spray and Seed Spray respectively. Since the number of copies is calculated in source node, we get σ¯b = σ¯s = 0 when the network condition is fixed in the period of spraying. However, more than one node has the right to spray in Adaptive Binary Spray and Adaptive Seed Spray. Their decisions for spraying are nearly coherent. On the other hand, it is not realistic to implement on-line consultation mechanism between nodes in opportunistic networks. Therefore, any form of adaptive parallel spray will produce redundant copies. Average copy redundancy of Adaptive Binary Spray and Adaptive Seed Spray were given in Theorem 3 and Theorem 4.

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Theorem 3: Let H denote the current depth of spray, σ¯ab denote the Average copy redundancy of Adaptive Binary Spray, then the value of σ¯ab can be given by: ( σ¯ab =

0

H62

1/2−1/2H−1 3/2+1/2H−1

H>2

(5)

Proof: When H 6 2, only one copy is forwarded in Adaptive Binary Spray and σ¯ab =0; When H>2, the total number of forwarded copies is L = 2H−1 and the number of needed copies is Lc = 2H−2 + i (i stands for the number of necessary copies at last layer, i = 1, 2, . . . , 2H−2 ). Hence we have σ¯ab =

P2H−2

(2H−1 −(2H−2 +i)) P2H−2 H−2 +i) i=1 (2

i=1

based on the definition of Average copy redundancy.

Suppose i is subjected to uniformity distribution with 1, 2, . . . , 2H−2 , then σ¯ab =

1/2−1/2H−1 . 3/2+1/2H−1

Theorem 3 indicates that σab is an increasing function of H when H > 2, and there is a maximum of σ¯ab , when H → ∞. Theorem 4: Let H denote the current depth of spray, σ¯as denote the Average copy redundancy of Adaptive Seed Spray with K seeds, then the value of σ¯as can be given by: ( σ¯as =

σ¯ab

K > 2H−2

K−1 2(2blogKc+1 +K(H−blogKc−3))+K+1

K < 2H−2

(6)

The proof of Theorem 4 is similar to the proof of Theorem 3. Theorem 4 indicates that the Average copy redundancy of Adaptive Seed Spray is equal to the Average copy redundancy of Adaptive Binary Spray when K > 2H−2 . It also implies that σ¯as is an increasing function of K ¯ = 0 when K=1, and σ¯as → σ¯ab when when K < 2H−2 , and there is a minimum of σ¯as , σasmin K → 2H−2 .

Fig. 6: Comparison for routing cost with different target delay Now, we can summarize the performance about Average copy redundancy: Adaptive Binary Spray has the most copy redundancies. The Average copy redundancy of Adaptive Seed Spray is an increasing function of the number of seeds.


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Through the analysis above, we can see that Adaptive Binary Spray will create the most redundant copies which will lead to high-cost routing in dynamic network environment. Figure 6 compares the Routing Cost of Adaptive Binary Spray and ASS. We can see that, compared with Adaptive Binary Spray, ASS is cost-efficient most of the time due to its improvement of copy redundancy. The Routing Cost of ASS reduced by 15.35% compared to Adaptive Binary Spray in average.

4

Performance Evaluation Table 1: Simulation parameters Parameters

Value

Movement model

Random Waypoint

Min speed

0.5m/s

Max speed

1.5m/s

Pause time

0

Simulation region

400, 400

Number of node

100

Size of message

500k − 1M

Transmit speed

250kbps

Traffic interval

2000 − 3000

TTL

20k

Trasmit range

10

We implemented Adaptive Multi-Copy Routing(AMR) [9] and ASS in Opportunistic Network Environment, ONE [14]. In fact, AMR is a realization of Adaptive Binary Spray. Table 1 shows the simulation parameters (some parameters will be given later in the following sections since they are related to special scenario). All the performance results presented are an average of 10 different simulation trials. The initial locations of the network in each trial are random. We calculate a 95% confidence interval for the unknown mean, and we plot these confidence intervals on the figures. It must be noted that there are two differences between AMR and ASS: (1) the estimation of EMT of ASS is fit for a number of popular mobility models like Random Walk, Random Waypoint and Random Direction, as well as more realistic, synthetic models, while AMR only apply to RWP model. (2) There is no seed in AMR. The Average copy redundancy of AMR is larger than ASS because of the difference about seed. However, ASS restricts the number of relay nodes using seed. First of all, we tested the capacity of delay constraint with different target delay, Dt (3000-8000 simulation time units). Each node created messages from time 0 to Dt for every 2000-3000 time units. The simulation ended at time 2 × Dt .

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Fig. 7: Performance comparison with different delay constraint The simulation results depicted by figure 7 show that Epidemic Routing which is lack of control mechanisms for message copies performed significantly more transmissions than AMR and ASS, though it has minimal delivery delay. AMR and ASS can satisfy all different target delay from 3000 to 8000. As an implement of Adaptive Binary Spray, AMR can achieve the best spray delay among all spray-based routing protocols. On the other hand, Theorem 1 has proved that Seed Spray is equivalent to Binary Spray when there are enough seeds. It implies that ASS and AMR have the same capacity of delay constraint since the expect delivery delay of Adaptive Seed Spray is equal to the expect delivery delay of Seed Spray. In addition, ASS performs fewer transmissions than AMR since ASS has fewer redundant copies in average which can increase the transmission times. The transmissions of ASS reduced by 17.04% compared to AMR in average.

Fig. 8: Performance comparison with different network scale Secondly, we evaluated the expansibility with different network scale (20-100 nodes). As shown in figure 8, all protocols can achieve the delay constraint with increasing scale of network. Most importantly, ASS exhibits great expansibility. Although ASS didn’t show superiority in average delivery delay, it is clear that ASS outperforms all protocols in terms of transmissions. The transmissions of ASS reduced by 21.23% compared to AMR in average. Note that the purpose of ASS is to achieve the delay constraint with low cost in dynamic circumstances. Further more, we can see from figure 6 that the increasing extent of transmissions of AMR is greater than ASS. The Routing Cost of Adaptive Binary Spray is an exponential form with the depth of spray which varies more significantly than ASS since the number of copies in adaptive spray depends on the depth of spray. This also brings about the differences in terms of Average


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copy redundancy. In opportunistic networks, network connectivity will change sometimes. For example, the changes of work pattern for special applications, low-power transmission mode due to lower energy, sleep scheduling mechanism, etc. To evaluate the performance with dynamic connectivity, we decreased the transmission range from 10m to 2m at Dt /2. Figure 7 depicts the performance of all routing algorithms in terms of average number of transmissions and average delivery delay in this scenario. As can be seen here, the average transmissions of spray protocols increased when the transmission range decreased. This is because EMT and corresponding delivery delay will increase since the transmission range decreased. This implies that it is necessary to add extra copies to meet target delay. On the contrary, the transmissions of Epidemic Routing decreased due to the specific value of TTL. ASS still shows great adaptability compared to other protocols and converges quickly. AMR estimates EMT only by the relative speed and can not apperceive the change of transmission range. Since BSW (Binary Spray & Wait) decides the number of copies in source node, it also can’t adapt the change of transmission scope. Therefore, BSW, AMR and Epidemic do not have good ability to adapt the changes of networking conditions, and the change of average transmissions of BSW, AMR and Epidemic are small. Especially, the delivery delay of AMR and BSW exceed the target delay later and need more time (> 22000) to make new decisions for spraying.

Fig. 9: Performance comparison with dynamic connectivity level

5

Conclusion

In this paper, an adaptive multi-copy routing protocol based on seed spray was proposed according to dynamic opportunistic networks. ASS calculates the number of seeds and estimates the routing cost in source node, and makes spray decisions in relay nodes which have the latest information of networking conditions. ASS utilizes only a small quantity of seed nodes to spray message copies in order to reduce redundant copies in large degree. Simulation results show that ASS has prominent superiority in terms of routing cost and adaptability, and that it is an efficient delay-bounded multi-copy routing protocol for opportunistic networks especially in dynamic conditions. Though ASS can balance the deliver delay and routing cost efficiently, it is necessary to look into some details about seed spray schemes. First, we intend to investigate the impact on seed spray schemes made by limitations of bandwidth and storage capability, and research the seed spray schemes under the conditions of restricted bandwidth and storage capability. Secondly, the

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precise equation for expected delay of ASS is necessary to be researched which is important to ascertain the quantitative relations among seed number, spray depth, routing cost and delivery delay.

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