Spray and forward: Efficient routing based on the Markov location prediction model for DTNs

. RESEARCH PAPER. SCIENCE CHINA Information Sciences February 2012 Vol. 55 No. 2: 433 440 doi: 10.1007/s11432-011-4345-1 Spray and forward: Efficient routing based on the Markov location prediction model for DTNs DANG Fei 1, YANG XiaoLong 1,2 &LONGKePing 2 1 Research Center for Optical Internet and Mobile Information Networks (COIMIN), University of Electronic Science and Technology of China, Chengdu 611731, China; 2 School of Computer and Communications Engineering, University of Science and Technology Beijing, Beijing 100083, China Received December 12, 2009; accepted June 13, 2010; published online August 7, 2011 Abstract Typical delay tolerant networks (DTNs) often suffer from long and variable delays, frequent connectivity disruptions, and high bit error rates. In DTNs, the design of an efficient routing algorithm is one of the key issues. The existing methods improve the accessibility probability of the data transmission by transmitting many copies of the packet to the network, but they may cause a high network overhead. To address the tradeoff between a successful delivery ratio and the network overhead, we propose a DTN routing algorithm based on the Markov location prediction model, called the spray and forward routing algorithm (SFR). Based on historical information of the nodes, the algorithm uses the second-order Markov forecasting mechanism to predict the location of the destination node, and then forwards the data by greedy routing, which reduces the copies of packets by spraying the packets in a particular direction. In contrast to a fixed mode where a successful-delivery ratio and routing overhead are contradictory, a hybrid strategy with multi-copy forwarding is able to reduce the copies of the packets efficiently and at the same time maintain an acceptable successful-delivery ratio. The simulation results show that the proposed SFR is efficient enough to provide better network performance than the spray and wait routing algorithm, in scenarios with sparse node density and fast mobility of the nodes. Keywords delay tolerant networks, spray and forward, Markov position prediction, routing algorithm Citation Dang F, Yang X L, Long K P. Spray and forward: Efficient routing based on the Markov location prediction model for DTNs. Sci China Inf Sci, 2012, 55: 433 440, doi: 10.1007/s11432-011-4345-1 1 Introduction Delay tolerant networks (DTNs) represent a class of networks where connections between wireless nodes are not contemporaneous, but intermittent over time. The disconnection makes the topology only intermittently and partially connected; thus end-to-end (ETE) communication links hardly exist. Typically, the applications of DTNs include space communications, interplanetary networks, ad hoc networks and wireless sensor networks. Due to the complexity of the DTN environment, there is no unified standard for the routing algorithm. Existing routing algorithms can be classified into two categories: forward-based routing [1 4] and floodingbased routing [5 9]. One of the forward-based routing algorithms defines a high-dimensional Euclidean Corresponding author (email: yxl@uestc.edu.cn) c Science China Press and Springer-Verlag Berlin Heidelberg 2011 info.scichina.com www.springerlink.com

434 Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 space, called MobySpace, which is constructed upon the mobility patterns of nodes in [1]. The specific MobySpace evaluated is based on how frequently the nodes visit each possible location. Jain et al. [2] proposed five kinds of forward-based routing algorithms for DTNs based on the Oracle Knowledge with different intensities, and proved that the algorithm is more efficient with more oracle knowledge. To improve the packet transmission rate, the forward-based routing needs to acquire more oracle knowledge, but this is almost impossible since the node cannot obtain timely status information from all the nodes. To improve the success delivery rate, and reduce dependence on oracle knowledge, many advanced flood-based routing algorithms have been proposed. One is the Spray and Wait Routing algorithm (SWR) [6], which randomly sprays r copies of the packet to the network, and when a forwarding node contains only one copy, it sends this copy on with direct transmission. By controlling the value of r and using direct transmission, it reduces the high cost of Epidemic routing [9], but the direct transmission decreases the data transfer rate. As an advanced routing version of the SWR, SMART [7] has the normal spray mode and companion spray mode. The normal spray mode delivers packets to the target area, while the companion spray mode sprays copies of the packet in the area and delivers packets to the destination. Although the spray mode can increase the success delivery rate by a factor of two, it also increases the network overhead. This paper proposes a hybrid approach with multi-copy forwarding to transmit the packets to combine the high transmission rate of the flooding strategy with the low network overhead of the forward strategy. The proposed spray and forward routing algorithm (SFR) will be described in detail in the next section. Then in Section 3 we run simulations to compare the performance of the proposed SFR method with the SWR method. Finally, we conclude this work in Section 4. 2 Spray and forward routing Since the location of the destination node is unknown, flood-based routings send copies of packets randomly, resulting in a low successful-delivery ratio and a high network overhead. Based on the simulation results of the various routing algorithms, ref. [10] proved that the SWR algorithm has a better network performance than other algorithms. To further enhance the network performance of the SWR algorithm, the historical information of the node is utilized and an efficient SFR based on the Markov location prediction model is proposed. The SFR works as follows. The node records its historical positions, and then builds a database. The tables are passively updated and the databases are shared among the nodes. By using the Markov location prediction model with the destination s historical locations in the database, the source node predicts the destination location and forwards packets to this location. Since the SFR forwards packets in a certain direction, it is able to improve the network performance. In the rest of this section, we introduce the Markov location prediction model and present our SFR routing algorithm. 2.1 Markov location prediction model Because of the continuity of the node s mobility, its next position is not only relevant to the current position but also to the previous position. Therefore, it is feasible to use an n-order Markov to predict the node s next position. It was proved in [11] that in an n-order Markov forecast, the best is the second-order, with which the next position is predicted. Assuming that the current position X of the node is a random variable, and the random variable sequence X i constitutes a Markov process, X i meets the following requirements: P {X n+1 = a X(1,n)=L} = P {X n+1 X n X n 1 = a n a n 1 }, (1) P {X n+1 = a X n X n 1 = a n a n 1 } = P {X k+1 = a X k X k 1 = a n a n 1 }, (2) where L = a 1 a 2,...,a n denotes the n sampled historical position queue, ΔT is the sampling interval, and a i is the node position at the ith time slot.

Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 435 According to the average speed of the node,thesamplingintervalδt is calculated by ΔT = R/(αv), (3) where R is the shortest distance between the specific nodes in the network, v is the average speed of the node, and α is a factor of the sampling accuracy. ΔT is related to the sampling frequency, and each node should calculate a suitable sampling interval and record the positions in equal time durations. The second-order Markov location prediction mode is a technique for predicting the node s location at the next moment, according to the current position (a curr ) and previous position (a prev ). The core of the prediction model is to establish the transition probability matrix M in Eq. (4), where the row vector provides the context locations (a prev a curr ) of length 2, and its column vector is the location at the next moment (a next ) when the node may appear. M = a 1 a j a m a 1 a 2 P 11 P 1j P 1m.......... a i a i+1 P i1 P ij P im.......... a m 1 a m P n1 P nj P nm, (4) where n = m 2. Each entry of the matrix represents the probability that a node moves from the corresponding row to the column. The transfer matrix is calculated by N(ca, L) P (X n+1 = a L) = N(c, L), (5) where c denotes the previous and current positions (a n 1 a n ), ca denotes the previous, current and next positions (a n 1 a n a), N(c, L) denotes the number of c appearing in L, and P (X n+1 = a L) representsthe probability that the node moves to a next time, depending on the current situation. From the target row a i a i+1 in the matrix M, the node finds the largest entry in this row, for which the corresponding column is the prediction location at the next moment. 2.2 Spray and forward The location information of the nodes is updated periodically, and then the destination location is predicted. The SFR forwards the packet by a multi-path greedy forward mechanism, which consists of two stages, i.e. routing table update stage and data forwarding stage. When the two nodes meet, the tables are updated and then the packets are transmitted. Figure 1 is the flowchart of the SFR, which is detailed below. 2.2.1 Routing table update stage Node i carries two routing tables: the historical position table (Ht i)andtheprediction position table (P t i). Ht i is a location vector that records the n sampled historical positions between itself and the others; Pt i records node i s next position and the prediction position of the destination D. According to Ht i,node i establishes the transfer matrix M by Eq. (5) such that the next position and the destination position can be obtained from M, which will be recorded in Pt i. The network region is divided into location points. Node i establishes the location library by sampling its positions and writes the location vector to Ht.ThenP i t i can be established according to Ht. i During the routing table update stage, each node broadcasts a control message to its neighbors containing the time list of location vectors in H t and the list of locations in P t.whennodeimeets node j, nodei compares node j s time list with itself, and sends the location vectors that j needs to update H j t, and at the same time node i writes the list to Pt i. Node j experiences the same as node i. The prediction locations are stored in P t of the tables, which determines the forwarding of packets. The overdue information in the database is then deleted.

436 Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 Start Update the tables Waiting for fowarding Number of copies n>1 N Greedy forward Y Binary spray L i F L 1 A 1 A 1 F L F d A n d d d D L D A n L n End Figure 1 Flowchart of spray and forward. Figure 2 Greedy forward phase. 2.2.2 Data forwarding stage After the routing tables update stage, the node enters the data forwarding stage. The data forwarding stage consists of the following two phases: 1) Binary spray phase: For every message originating at a source node, N message copies are initially sent out. When node B is in the range of A (source or relay) that contains n (N n >1) copies, A sends n/2 copies to B and keeps n/2. 2) Greedy forward phase: When a node has only one packet copy (n=1), it enters the Greedy forward phase. As shown in Figure 2, node F has one copy, and its neighboring nodes are A 1,..., A n. The arrows show the direction of movement of the nodes. L 1, L 2, L i (1 i N) arethelocations at the next moment in P t, LF is the next location of F, and L D is the destination position. Comparing the distances d between the prediction positions and the destination position, we let d min =min{d(l F,L D ),D(L 1,L D ),D(L 2,L D ),...,D(L n,l D )}. (D(L 1,L D ) denotes the distance d between L 1 and L D ). If d min = D(L n, L D ), F would forward the packets to A n,andifd min = D(L F,L D ), then F will continue carrying the packets. According to the number of copies n, a node enters the corresponding phases. When n>1, the node enters a binary spray phase, and when n = 1, it enters the greedy forward phase. The greedy forward phase with predictions can select better carriers that are closer to the destination location. A control field is added in the packet format that stores the historical positions of the node. When H t does not contain the destination information, the node can share the position information from the control field. If neither node F nor its neighbors contain the destination information, then F transmits the packet directly to the destination. When a node with the destination information appears, F enters the greedy forward phase. When the destination receives a packet, it sends back an ACK to its corresponding transmitter, which then deletes the copies of this packet and progressively sends back ACKs to preceding nodes until it reaches the source node. Each packet carries a survival timer (TTL); when the TTL value is zero, the packets are discarded. For a certain level of network latency, the least number of copies N min the SWR needs were calculated in [6], where the scalability of the SWR was also proved. When the ratio (N/M) of the number of copies to the number of nodes remains unchanged, the network average delay decreases with increasing M. Let N/M equal 1/4. Figure 3 shows the average delay against the number of nodes for both the SWR and the proposed SFR. We can see that the SFR always outperforms the SWR in terms of average delay, and it has a significant advantage with an increasing number of nodes. This shows that the proposed SFR has good scalability.

Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 437 Average delay (s) 0.34 0.33 0.32 0.31 0.30 0.29 0.28 0.27 0.26 0.25 0.24 0.23 18 20 22 24 26 28 30 32 34 36 38 40 Number of nodes Figure 3 Average delay vs. number of nodes. Table 1 Simulation parameters Simulation environment parameters Default value Simulation area 2000 m 2000 m Number of nodes 20, 25, 30, 35, 40 Transmission range 250 (m) Bandwidth 1 (M bit/s) Max speed of the node 0, 5, 10, 15, 20, 25, 30 (m/s) number of copies 4, 8, 12, 16, 20, 24 ΔT 2(s) Buffer size 256000 (bits) Simulation duration 300 (s) 3 Evaluation 3.1 Simulation settings We evaluated the performance of the proposed protocol using OPNET. Nodes were randomly distributed in a 2000 m 2000 m flat area, which was divided into 20 20 smaller square areas. The node position sampling time ΔT was set at 2 s. Community Mobility Model [12] was used, and its stop time was set to 5 s. A summary of all simulation parameters is presented in Table 1. We compare our SFR with the SWR in terms of the successful-delivery ratio, routing overhead and average delay, which are defined as follows: i number of packets received by the node i (1) successful-delivery ratio= i number of packets sent by the node i ; (2) routing overhead= i number of packets sent by node i; i j ETE delay of packet j received by node i (3) average delay = i number of packet received by node i. 3.2 Simulation results 1) Number of nodes: We show the effects of the number of nodes M when the average speed v is 20 m/s and the number of copies N is 12. The more nodes in the network, the more likely there is an end-to-end link. We consider a network scenario with a lower node density than other studies [4,8], and each node has a number of 0 1 neighbors on average.

438 Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 Delivery ratio 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 (a) 18 20 22 24 26 28 30 32 34 36 38 40 42 Average delay (s) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 (b) 0.1 18 20 22 24 26 28 30 32 34 36 38 40 42 Number of nodes Number of nodes Figure 4 Delivery and delay vs. number of nodes. (a) Delivery; (b) delay. Delivery ratio 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 (a) 0.02 2 4 6 8 10 12 14 16 18 20 22 24 26 Copies of packet Average delay (s) 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 (b) 0.6 2 4 6 8 10 12 14 16 18 20 22 24 Copies of packet Figure 5 Delivery and average vs. number of copies. (a) Delivery; (b) delay. Figure 4(a) shows that, through prediction, the SFR is able to achieve a higher delivery rate than the SWR (60% better). As shown in Figure 4(b), the average delay is around 0.2 s lower than for the SWR. With an increasing number of nodes, both routing algorithms have a higher delivery rate with a lower average delay. However, when there are more than 35 nodes in the network due to the limited resources, network performance (i.e. success delivery rate) is degraded due to link congestion and competition. Simulation results show that the SFR is less susceptible to this negative effect than the SWR. 2) Number of copies: In the simulation, we let M =25, and node max speed v=20 m/s. The more copies of a packet that are sent to the network, the higher the probability that this packet will be successfully delivered to the destination, and the greater will be the overhead caused at the same time. Figure 5(a) shows that the successful delivery ratio of the two algorithms increases with an increasing number of copies, and the proposed SFR scheme always outperforms the SWR in terms of successful delivery ratio. When the number of copies is 16, the SFR s delivery ratio reaches its peak value, instead of the 20 needed by the SWR to reach its peak value. This means that the SFR needs fewer copies than the SWR to achieve the same successful delivery ratio. Figure 5(b) shows that the average delay of our SFR routing is around 0.5 s lower than the SWR. When the number of copies is 16, both routing algorithms achieve the minimum delay. When the number of copies increases to 24, the copies in the network reach the requirements of Epidemic Routing [9]; however, because the latency time in the node buffer also becomes longer, the average delay increases. By choosing specific points in Figure 5(b) and using the average delays of these points as the maximum expected delays, we can obtain the least number of copies required by the two routing algorithms. Figure 6 shows the routing overhead against the least number of copies, from which we can see that the

Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 439 Routing overhead (message) 3200 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Delivery delay (s) Figure 6 Routing overhead vs. delay. Delivery ratio 0.35 0.30 0.25 0.20 0.15 0.10 0.05 (a) 0 5 10 15 20 25 30 Speed (m/s) Average delay (s) 0.38 0.36 0.34 0.32 0.30 0.28 0.26 0.24 0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 (b) 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Speed (m/s) Figure 7 Delivery and delay vs. the node speed. (a) Delivery; (b) delay. routing overhead of the two algorithms decreases with the increase in the expected delay. Besides, our SFR always requires less overhead than the SWR under certain delay constraints. From the above analysis, we can see that in the same network scenario the SFR is superior to the SWR, and able to achieve a better delivery ratio with fewer copies while at the same time decreasing the network overhead. 3) Node speed: We let M =25 and the number of copies N=12. Nodes which move faster can increase the dissemination of packets and affect the success delivery rate. Figure 7 shows that when the node moves at a speed of 10 m/s, both the delivery ratio and average delay reach optimal values. The delivery ratio of the SFR outperforms the SWR by 63%, and its average delay is 0.1 s shorter. Figure 7 also shows that the node speed has a significant influence in a situation of low node density. For the SFR and SWR the maximum delivery rate achieved was 0.32 and 0.18, and the minimum delays were 0.18 and 0.07 s. However, when the node moves at a speed of higher than 10 m/s, the delivery rate drops and the network delay increases for both routing algorithms. This is because the disconnection of links is more likely to happen when the nodes moves faster. Simulation results demonstrate that no matter how the node speed changes, the SFR always performs better than the SWR. All the above simulation results and analyses verify that the SFR outperforms the SWR. Specifically, the node speed can significantly affect the network performance, and the node mobility can greatly increase the probability of communication between nodes. When a network has a low node density, the proposed scheme is able to achieve a higher delivery ratio with lower average delay than the SWR.

440 Dang F, et al. Sci China Inf Sci February 2012 Vol. 55 No. 2 4 Conclusions In this paper, we proposed an efficient routing algorithm based on the Markov location prediction for DTNs. By combining the binary spray with greedy forward and spraying packets in a specific direction, our proposed SFR is able to improve the success delivery ratio efficiently and at the same time reduce the network delay and overhead where there is sparse node density. Simulation results show that the SFR is a scalable routing algorithm that achieves better performance than the SWR in terms of successful-delivery ratio and network overhead. In future work we will investigate in detail the scenarios where the nodes have a limited buffer and battery life. To implement our scheme in practice, we will further improve the spray and forward routing algorithm. Acknowledgements This work was supported by National Natural Science Foundation of China (Grant No. 60873263), National Basic Research Program of China (Grant No. 2007CB310706), National High-Tech Research & Development Program of China (Grant No. 2009AA01Z215), and New Century Excellent Talents in University (Grant No. NCET-09-0268). References 1 Leguay J, Friedman T, Cunan V. DTN routing in a mobility pattern space. In: Proceeding of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking. New York: ACM Press, 2005. 276 283 2 Jain S, Fall K, Patra R. Routing in a delay tolerant network. In: Proc. ACM SIGCOMM, 2004 3 Lu X F, Hui P, Towsley D, et al. LOPP: A location privacy protected anonymous routing protocol for disruption tolerant network. IEICE Trans Inf Syst, 2010, E93-D: 503 509 4 Yoon H, Kim J W, Ott M, et al. Mobility emulator for DTN and MANET applications. In: Proceedings of the 4th ACM International Workshop on Experimental Evaluation and Characterization. Beijing, China, 2009. 51 58 5 Costa P, Mascolo C, Musolesi M, et al. Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks. J Select Areas Commun, 2008, 26: 748 760 6 Spyropoulos T, Psounis K, Raghavendra C S, et al. Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In: Proceedings of ACM SIGCOMM Workshop on Delay-Tolerant Networking (WDTN), 2005. 252 259 7 Tang L, Hong X Y, Zheng Q W, et al. SMART: A selective controlled-flooding routing for delay tolerant networks. In: International Conference on Broadband Communications, Networks, and Systems (Broadnets 2007), Raleigh, NC, 2007. 356 365 8 Yang P, Chuah M. Performance evaluations of data-centric information retrieval schemes for DTNs. Computer Networks: The Int J Comput Telecommun Netw, 2009, 53: 541 555 9 Zhang X, Neglia G, Kurose J, et al. Performance modeling of epidemic routing. Comput Netw, 2007, 51: 2867 2891 10 Keränen A, Ott J. Increasing reality for DTN protocol simulations. Technical Report, Helsinki University of Technology, Networking Laboratory, July 2007 11 Song L B, Kotz D, Ja I R, et al. Evaluating location predictors with extensive Wi2Fi mobility data. In: Proceedings of INFOCOM. Hong Kong: IEEE, 2004. 1414 1424 12 Li Z, Shen H Y. Utility-based distributed routing in intermittently connected networks. In: 37th International Conference on Parallel Processing, Portland, Oregon, USA, 2008. 604 611