Cluster-Based Data Transmission Protocol in Delay-Tolerant Mobile Networks

Ha Dang and Hongyi WuThe Center for Advanced Computer Studies

University of Louisiana at LafayetteLafayette, LA 70504

Email: {yxs4862,wu} @cacs.louisiana.edu

1 IntroductionBesides its original focus on space communications, the

Delay-Tolerant Network (DTN) has been introduced intoterrestrial mobile wireless networks. DTN is fundamen­tally an opportunistic communication system, where com­munication links exist temporarily, making it impossible toestablish end-to-end connections. Therefore, most of con­ventional communication protocols developed for well con­nected networks simply fail here. Various new approachessuch as SWIM [1], DFT-MSN [3], and ProPhet [2] havebeen investigated, where routing is largely based on nodalcontact probabilities. They can be classified as more or less"flat", where every node plays a similar role in routing. Theflat architecture is simple and effective in small networks,but not scalable to large size DTNs.

Clustering has long been considered an effective ap­proach to improve network scalability. Although variousclustering algorithms and protocols have been investigatedin the context of mobile ad hoc networks, we notice thatnone of them can be applied directly to DTNs, becausethey are designed for well-connected networks and requiretimely information exchange among mobile nodes.

In this research, we investigate a distributed clusteringscheme and a cluster-based routing protocol for Delay­Tolerant Mobile Networks. The basic idea is to distribu­tively group mobile nodes that have similar mobility patterninto a cluster, which can then interchangeably share theirresources such as buffer space for overhead reduction andload balancing. Due to the lack of continuous communica­tions and possible errors in the estimation of nodal contactprobability, convergence and stability become major chal­lenges in such cluster formation. In our algorithm, an ex­ponentially weighted moving average (EWMA) scheme isemployed for on-line updating the nodal contact probability,with its mean proven to converge to the true contact prob­ability. Based on the local information, a set of functionsincluding Sync(), Leave(), and Join() is devised for clus­ter formation and gateway selection. Finally, the gatewaynodes exchange network information and perform routing.

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2 Distributed Clustering

2.1 Estimation of Contact Probabilities

We adopt a simple and effective approach, named ex­ponentially weighted moving average (EWMA), for onlineleaning contact probabilities. The basic idea of EWMA isto apply a weighting factor for decreasing old data, givingmore importance to recent observation.

2.2 Meta-Information

Beside its ID and its cluster ID, each node maintains acluster table, and a gateway table.

The cluster table consists of four fields: Node ID, Con­tact Probability, Cluster ID, and Time Stamp. Each entryin the table is inserted/updated upon meeting with anothernode, by using the aforementioned online updating scheme.The gateway table, used for routing, consists of four fields:Cluster ID, Gateway, Contact Probability, and Time Stamp.

2.3 Distributed Clustering Algorithm

The algorithm is event-driven, where the key part lies onthe meeting event between any pair of nodes. The set offunctions in the algorithm including Sync, Leave, and Joinis outlined below.

Sync The Sync() process is invoked when two clustermembers meet and both pass the membership check. It isdesigned to exchange and synchronize two local tables. Thesynchronization process is necessary because each nodeseparately learns network parameters, which may differfrom nodes to nodes. The Time Stamp field is used for the"better" knowledge of the network to deal with any conflict.

Leave If they don't pass the membership check, the nodewith lower stability must leave the cluster. The stability of anode is defined to be its minimum contact probability withcluster members. It indicates the likelihood that the nodewill be excluded from the cluster due to low contact proba­bility. The leaving node then empties its gateway table andreset its Cluster ID.

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Join The Join() procedure is employed for a node to joina "better" cluster or to merge two separate clusters. Anode will join the other's cluster if (i) it passes member­ship check of all current members, and (ii) its stability isgoing to be improved with the new cluster. By joining newcluster, it will copy the gateway table from the other nodeand update its cluster ID accordingly.

3 Cluster-based Routing

In the following discussions, we assume that Node i hasa data message to Node j.

Intra-cluster Routing If Nodes i and j are in the samecluster, they have high chance to meet each other, thus Nodei will transmit the data message to Node j directly upontheir meeting. No relay node is necessarily involved.

One-hop Inter-cluster Routing If they are not in thesame cluster, Node i looks up gateway information to Nodej's cluster in its gateway table. If an entry is found, Node isends the data message to that gateway. Upon receiving thedata message, the gateway will forward it to any node, e.g.,Node k, in Node j's cluster. Node k, which in tum deliv­ers the data message to Node j via Intra-cluster Routing. Ifno gateway entry is found, Node i proceeds the Multi-hopInter-cluster Routing as to be discussed next.

Multi-hop Inter-cluster Routing If Node i does not haveany information about Node j, the data transmission needsa multi-cluster routing scheme. Given the low connectivityenvironment, on-demand routing protocols, with extremelyhigh packet dropping probability, will not work effectivelyhere. However, any table-driven routing algorithm such asthe following link-state-like protocol can be employed.

In the protocol, every gateway node builds a ClusterConnectivity Packet (CCP), and distributes it to other gate­ways in the network. The CCP of a Gateway comprises itscluster ID and a list of clusters to which it serves as gate­way along with corresponding contact probabilities. Suchinformation can be readily obtained from the gateway table.

Once a gateway node accumulates a sufficient set ofCCP's, it constructs a network graph. Each vertex in thegraph stands for a cluster. A link connects two vertices ifthere are gateways between these two clusters. The weightof the link is the contact probability of the correspondinggateway nodes. Based on the network graph, the shortestpath algorithm is employed to establish the routing table.Each entry in the routing table consists of the ID of a desti­nation cluster and the next-hop cluster ID. Once the routingtable is obtained, the routing is performed from a cluster toanother cluster via One-hop Inter-cluster Routing and Intra­cluster Routing.

Load Balancing Load balancing is an effective enhance­ment to the proposed routing protocol. The basic idea isto share traffic load among the nodes in the same cluster.Specifically, a node performs load balancing when its queueexceeds a pre-defined threshold, by randomly transmittingas many packets as possible to any node it meets, until theirqueues are equally long.

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4 Simulations

We have developed a stand-alone simulator to evaluateour cluster-based routing protocol. In the simulations, weadopt the community-based mobility model [2,5]. The sim­ulation results show that our protocol achieves higher deliv­ery ratio and significantly lower overhead and end-to-enddelay in most simulation scenarios, compared with its non­clustering counterpart, as shown in the above figure.

References

[1] T. Small and Z. Haas, "The Shared Wireless Infosta­tion Model: A New Ad Hoc Networking Paradigm (orWhere there is a Whale, there is a Way)," in Proc. ofMobiHOC, pp. 233 - 244, 2003.

[2] A. Lindgren, A. Doria, and O. ScheIn, "Probabilis­tic Routing in Intermittently Connected Networks," inProc. ofMobiHoc, pp. 19-20, 2003.

[3] Y. Wang, H. Wu, F. Lin, and N.-F. Tzeng, "Protocol De­sign and Optimization for Delay/Fault-Tolerant MobileSensor Networks," in Proc. ofICDCS, pp. 7, 2007.

[4] M. Kim, D. Kotz, and S. Kim, "Extracting A MobilityModel from Real User Traces," in Proc. ofINFOCOM,pp. 1-13,2006.

[5] T. Spyropoulos, K. Psounis, and C. Raghavendra,"Performance analysis of mobility-assisted routing," inProc. ofMobiHoc, pp. 49-60, 2006.

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