Epidemic-based Controlled Flooding and Adaptive Multicast for Delay ...

Epidemic-based Controlled Flooding and Adaptive Multicast for Delay Tolerant Networks Zhigang Jin, Jia Wang

Sainan Zhang, Yantai Shu

School of Electronics and Information Engineering, Tianjin University Tianjin, China

School of Computer Science and Technology Tianjin University Tianjin, China

Abstract—Delay Tolerant Networks (DTN) is a kind of sparse Ad Hoc networks in which no contemporaneous path exists between any two nodes most of the time. Multicasting in DTN is a desirable feature for applications where some form of group communication is in demand. In this paper, we propose a multicast protocol for DTN: ECAM (Epidemic-based Controlled Flooding and Adaptive Multicast for Delay Tolerant Networks). It limits the time and range of message transmission, so that the huge consumption of network resources caused by flooding can be saved. Meanwhile, ECAM adopts the adaptive mechanism to work well under diverse network conditions with different node densities. We study its performance through comparative simulations in the QualNet networks simulator. The simulation results show that ECAM could outperform existing epidemic approaches. Our routing strategies can achieve higher message delivery ratios and lower average message copies.

Routing in it uses a store-carry-and-forward paradigm, in which intermediate nodes carry packets while waiting for available forward links. Nodes in the network continuously exchange copies of the messages they don’t have, until the messages reach their intended destinations. Although message flooding is a robust and simple solution to DTN routing, its tremendous cost in network resource depletion is unaffordable. In this paper, we propose a novel protocol called Epidemicbased Controlled Flooding and Adaptive Multicast for DTN (ECAM). Firstly, ECAM limits the maximum times of message transmission among nodes to control flooding. Secondly, ECAM employs a hierarchical transmission approach to save the buffer, bandwidth and other network resources. In order to be suitable for a variety of network scenarios, an adaptive mechanism is also deployed in the routing procedure.

Keywords-DTN; epidemic; controlled flooding; hierarchical; adaptive

The rest of the paper is structured as follows. Section Ⅱ goes over related works on DTN multicast routing. Section Ⅲ describes our proposed approaches for multicasting in DTN. Section Ⅳ presents performance analysis of ECAM, comparing to the standard epidemic multicast routing protocol. Finally, Section Ⅴ concludes the paper.

I.

INTRODUCTION

Delay tolerant networks (DTNs) are network models based on the mobile self-organized networks such as ad hoc, WSN (wireless sensor network), satellite networks, etc. In the networks, communicating nodes would never or rarely have a stable end-to-end path so that they must bear much long delays. These networks have been deployed by many applications, such as planetary and interplanetary communication, military battlefield communication, habitat monitoring, sensor networks, and other forms of large-scale mobile networks. These applications have created many new challenges for network developer to solve, including but not limited to, network partitioning, intermittent connectivity, long-duration delays and the absence of an end-to-end path. For different kinds of DTN applications, many of them require efficient network support for multicasting. For instance, soldiers in a battlefield need to share information about their surroundings among one another. Although unicast can be used to support these group-oriented applications, it is greatly inefficient in terms of delay and resource consumption. So, multicast communication is a key technique amid the DTN researches. In 2000，Vahdat and Becker proposed the first routing protocol for DTN, named Epidemic Routing Protocol[1]. Basically speaking, epidemic applies a flooding mechanism.

II. RELATED WORK Multicast for DTN is a fundamentally different and hard problem compared to multicast for MANET due to the frequently intermittent connections. In this section, we review some of the existing research work of DTN multicasting. U-Multicast [2] is the simplest implementation of multicast routing. It does multicast data transfer by using multiple unicast services from the source node to the destination nodes. In its process, all data copy works are performed in the source node, which leads to the low efficiency of algorithm. [2] [3] and [4] proposed three tree-based multicasting algorithms. They assume that each source node of the multicast group has complete knowledge or a summary of the link states in the network. During the communication process, nodes need to maintain the multicast delivery tree all the time, which is not suitable to DTN, whose network topology changes frequently. In [5], the authors propose a CAMR scheme where nodes are allowed to use high power transmissions when the locally observed node density drops below a certain threshold. A

message ferry feature is also used within it. CAMR achieve much higher multicast delivery ratio than [3] and [4]. However, it still relies on a route discovery process that is similar to the traditional ad hoc routing approach and also relies on the ability to control node movement. [6] presented an Encounter Based Multicast Routing protocol (EBMR). It developed based on the Prophet algorithm proposed by Lindgren [7]. Since EBMR doesn’t use any route discovery mechanism, it has a wide range of applications. However, for its use of antenna equipment during the neighbor discovery process, a large amount of energy can be consumed, which may be improper for the energy-constrained DTN. EMR (Epidemic Multicast Routing) applies epidemic [1] algorithm to the multicast communication of DTN. In EMR, messages spread throughout the network just like the diffuse of virus. Owing to the simple design, EMR can be well applied to DTN. Nevertheless, due to the flooding mechanism, the efficiency of algorithm will be poor unless some improvements can be done to solve the resource problem. Distinct from the above mentioned algorithms, firstly, ECAM needs neither grasping any prior knowledge about the network condition, nor having information of neighboring nodes and group members. Secondly, by adopting different message forwarding strategies to nodes inside and outside multicast groups, ECAM can achieve more efficient use of network resources, comparing with the standard epidemic routing protocols. Finally, the introduction of adaptive mechanism making ECAM has a better adaptability to different network environments. III.

PREPARE YOUR PAPER BEFORE STYLING

In our ECAM, the controlled flooding mechanism is adopted to limit the maximum times that a message can be forwarded, so that the consumption of network bandwidth can be reduced and the network load balancing can be achieved. In addition, by using the hierarchical transmission mechanism, the message spreading among group and non-group members are treated distinctly in ECAM. Such scheme enables ECAM to substantially lower overhead by eliminating redundant data transmissions. And lastly, ECAM deploys an adaptive mechanism, with which every node can adjust the protocol parameters to the actual condition of network, and as a result of it, the protocol could show a better adaptability to diverse network environments. The remainder of the section describes the major parts of each protocol mechanism. A.

Flooding Control Mechanism In the standard epidemic routing protocol, nodes broadcast ‘Hello’ messages to detect neighbor nodes periodically. Each node encapsulates the summary of data messages in its buffer in ‘Hello’ for changing messages with other nodes. We call the summary as digest. For DTNs in which disruption might occur at any time, once two nodes get into each other’s communication range, they should make full use of the network bandwidth to transmit business flow immediately. Therefore, the length of ‘Hello’ packet (with digest inside) should not be too long so that the waiting time before sending data packets can be short. In addition, the digest that is on

behalf of a full buffer might be too long to be transmitted in one packet. As a result, IP fragmentation may occur, which can cause a longer delay. For all of these reasons, ECAM selectively pick up messages that will be put into the digest from the node buffer. So, the problem becomes how to choose the messages. For this purpose, ECAM sets a field named ‘digestCnt’ for each buffer node in order to record the number of times that the message has been encapsulated in the digest. Table 1 shows all fields involved in a buffer node. Table 1 Buffer Node packet format

The meaning of fields are as follows ： Message:

pointer to the message address

id:

unique index of the message

timestamp:

the store time of the message

grpAddr:

the multicast destination address of the message

digestCnt:

number of times that the message has been encapsulated in the digest

pre:

pointer to the former node

post:

pointer to the next node

At the beginning of algorithm, the value of digestCnt for each message in a node buffer is cleared to 0. For each broadcast round, the node traverses all of the digestCnt field in its data buffer and chooses the ids of the messages whose digestCnt value are less than a pre-defined threshold value called Th. Then, these message ids will be put into the digest and the digestCnt fields of these messages will be added by 1. For those messages whose digestCnt fields equal to or greater than Th are marked as deletable and will be removed from the buffer later. From the above description we can see that the threshold determines the number of rounds for which a packet can be kept in the data buffer. Figure 1 shows the digest production process of node A when it encounters node B. In this example, the value of threshold Th is set to 6. After coming across node B, node A scans its data buffer to compare the digestCnt of each message with Th. Then it knows that the digestCnt of messages with id 5, 27 and 35 are less than Th. So, these message ids are added to the digest and will be sent to node B with the broadcasting of hello message. Meanwhile, the digestCnt of these messages are increased by 1. Besides, the remaining messages are marked as deletable and will be deleted from the buffer.

when a node finds itself in an intensive network, it should contribute to the avoidance of network congestion through prolonging the interval of Hello message broadcasting as well as reducing the value of threshold Th, to decrease the number of message transmissions. In contrast, it should reduce the interval of Hello message broadcasting and increase the value of threshold Th to improve the probability of message transmissions.

Figure 1 The digest production process of node A

B.

Hierarchical Transmission Mechanism In terms of the standard epidemic multicast routing protocol mentioned in [1], members of a group exchange packets within their group, as well as members of other groups. It is not hard to see that when no resource constraints are present, the Epidemic routing scheme achieves the lowest delay and highest delivery ratio among all possible routing schemes for DTN. However, DTN is a kind of network whose resources are quiet limited. Therefore, CAMR adopts a hierarchical transmission mechanism to distinguish the data transmission within and without the multicast group.

Specifically, we assume that there are two communication nodes: A and B. For each data message, there is a time-to-live (TTL) field connect to it to determine the number of hops a message can travel. Before forwarding a data message to B, A checks if B belongs to the destination multicast group of the message. If the result is true, A will send message to B directly. Otherwise, the TTL field will be reduced before the message is sent to B. With such mechanism, the number of data messages that are forwarded to non-group members can be managed. As a result, the flooding of messages among nodes is controlled and the most part of network bandwidth could be used in effective data message transmissions. As to the varied application needs, by carefully adjusting the decreasing rate of TTL when sending message to a non-group node, the desired packet deliver ratio can be achieved together with the saving of network resources. C.

Adaptive Mechanism According to the massive simulation experiments in [8], many factors can affect the performance of MANET routing protocol dramatically such as data transmission frequency, data transmission velocity and so on. Among all of these factors, the nodes density of network contributes most to the function of routing protocol. The reason is obvious, since all network members participate in data delivery, high density increases the traffic load that nodes expose, while low density results in poor network connectivity and hence in adequate data delivery [8]. In order to well accommodate to the complex communication conditions, we attach a adaptive mechanism to CAMR, with which the nodes in network can adjust its routing parameters in light of the current network status. Specifically,

To achieve that, CAMR provides a local concept named ‘surrounding node density’ to touch the distribution status of network, so that the nodes could adjusts the protocol parameters automatically. For the standard epidemic protocol, it merely detects the neighbor nodes through the transmission of Hello and Reply messages, rather than maintaining any neighbor or group information by using some mechanisms. A node considers the senders of both messages to be its neighboring nodes. Here, we assume that each node is responsible for maintaining the information of nodes that they recently meet by using an ‘encounter list’. In each period of Hello broadcasting, the node could calculate the density of local nodes by traveling the ‘encounter list’. What’s more, in order to avoid the oscillations, a node calculates the local node density several times to get the average of these pre-calculated values before making any parameter changes. As shown in Formula 1, den1… denn denotes the local density through n time calculations. A node could get the local density through the mean value of such n values. With such local node density, a node could got the density status for the current network and change the protocol parameters automatically. localNodeDensity = (den1 + den2 + … + denn) / n

（1）

Table 2 lists a possible set of values that a node may take within the adaptive mechanism. Table 2 Possible parameters used with adaptive mechanism localNodeDensity parameters

LOW

MEDIUM

HIGH

Hello Interval Hello Augmenter Hello Upper Limit Th (Threshold)

1 0.05 3 220

1 0.1 4 200

1.5 0.15 5 180

IV.

PERFORMANCE EVALUATION

In this paper, we simulate our algorithm and evaluate its performance using QualNet 4.0 simulator under Red Hat Linux 9.0 platform. In order to show the performance of ECAM, two metrics are taken in the experiments, which are the average message delivery ratio and the average message copy. The average message delivery ratio is the ratio of number of data packets successfully delivered to the number of data packets generated. The average message copy is calculated as the mean value of total message copies in the network. The standard Epidemic routing protocol is also implemented as the contrast. In our simulation scenario, there exist four MCBR data flows. Every source node sends 2560 packets each with a size

of 1460 bytes. The transmission interval is 0.004s. Our simulation area is 2000m*2000m where there are 50 nodes. Each node moves between 3m/s and 8m/s and the movement model is Random Way Point. Other parameter settings are given in Table 3.

Average Message Copy

Table 3

14000

Simulation Parameters

Parameters SIMULATION-TIME LINK-BANDWIDTH LINK-PROPAGATION-DELAY PROPAGATION-PATHLOSS-MODEL PHY-MODEL PHY-RX-MODEL PHY-NOISE-FACTOR TRANSMISSION RANGE

Value 30M 112000 50MS TWO-RAY PHY802.11b PHY802.11b 7.0 200M

12000 10000 8000

CAEM EPIDEMIC

6000 4000 2000 0 0

500

1000 1500 2000 2500 3000 Buffer Size

Figure 3

Average message copies

V. The comparison of average message delivery ratio Figure 2 presents the result of average message delivery ratio comparing ECAM with EPI. The initial value of TTL used by ECAM is 16, half of which will be cut down when a message is transmitted to a non-group member. Generally, the average message delivery ratio raises for both of the routing protocols with the buffer size increasing. However, given a certain buffer size, ECAM shows its superiority in average message delivery ratio over the pure flooding pattern. Moreover the larger buffer size, the more advantage of ECAM. When the buffer size comes to 3000, the average message delivery ratio in ECAM can reach 100% while that is only about 72% in standard Epidemic.

Delivery Ratio

A.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

CAEM EPIDEMIC

This paper investigated the problem of efficient message forwarding mechanism in Delay Tolerant Networks. A novel scheme, called Epidemic-based Controlled Flooding and Adaptive Multicast for Delay Tolerant Networks is proposed. By means of using the flooding control and hierarchical transmission mechanism, the spread of messages among nodes are well managed so that the consumption of network resources cased by flooding can be reduced dramatically with ECAM. Besides, with the adaptive mechanism, ECAM can show its flexibility under different network conditions. ACKNOWLEDGMENT This research was supported in part by the National Natural Science Foundation of China (NSFC) under grant No. 90604013, the Tianjin Natural Science Foundation under grant No. 08JCYBJC14200, the National 863 Program of China under grant No. 2007AA01Z220. REFERENCES [1] [2]

0

500

1000 1500 2000 2500 3000 Buffer Size

Figure 2 Average message delivery ratio

[3] [4]

[5] [6]

The comparison of average message copies Figure 3 shows the result of average message copies comparing ECAM with the standard Epidemic routing algorithm. For using the flooding control and hierarchical transmission mechanisms, the spread of message among nodes are well managed in ECAM. So, the average message copies of ECAM are far below that of the standard Epidemic routing protocol.

CONCLUSION

B.

[7]

[8]

Vahdat A, Becker D. Epidemic Routing for Partially Connected Ad Hoc Networks. Duke Technical Report[R], 2000. YE Q,CHENG L,CHUAH M,et al. Performance Comparison of Multicast Approaches in Disruption Tolerant Networks.LU- CSE- 06020, 2006. ZHAO W, AMMAR M, ZEGURA E. Multicasting in delay tolerant networks: semnatics models and routing algorithms [R], 2005. YE Q, CHENG L,CHUAH M,et al. On- demand situation- ware multicasting in DTNs. Proceedings of IEEE Vehicular Technology Conference: [C] , 2006. YANG P, CHUAH M. Context- Aware Multicast Routing Schemes for DTNs. CSE Technical Report [R] , 2007. CHUAH M,LI Y. An Encounter- Based Multicast Scheme for Disruption Tolerant Networks:CSE Technical Report [R] , 2007. LINDGREN A, DORIA A, SCHELN O. Probabilistic Routing in Intermittently Connected Networks [J]. Sigmobile, Mobile Computingand Communications Review, 2003,7(3) :19- 20. Oznur Ozkasap, ZulkufGenc, EmreAtsan. Epidemic-based Reliable and Adaptive Multicast for mobile ad hoc networks. Computer Networks[J], Volume 53, Issue 9, 25 June 2009, Pages 1409-1430.