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On the Effect of Group Mobility to Data Replication in Ad Hoc Networks

On the Effect of Group Mobility to Data Replication in Ad Hoc Networks. Jiun-Long Huang and Ming-Syan Chen IEEE Transactions On Mobile Computing, May 2006 Presented by Manu Shukla CS 6204 Fall 2006. Agenda. The Problem DRAM Algorithm Allocation unit construction phase VectorCluster

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On the Effect of Group Mobility to Data Replication in Ad Hoc Networks

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  1. On the Effect of Group Mobility to Data Replication in Ad Hoc Networks Jiun-Long Huang and Ming-Syan Chen IEEE Transactions On Mobile Computing, May 2006 Presented by Manu Shukla CS 6204 Fall 2006

  2. Agenda • The Problem • DRAM Algorithm • Allocation unit construction phase • VectorCluster • Replica allocation phase • Experiments and Evaluations • Conclusions and Critique

  3. Introduction • Mobile Ad Hoc Network (MANET) is a self-organizing, rapidly deployable network of wireless nodes without infrastructure • Mobile nodes of a MANET also function as routers • Disconnection often occurs due to mobility and causes frequent network division • Disconnected partitions decrease data accessibility • Data replication can greatly improve the accessibility for a partitioned network

  4. Introduction (2) • DCG and E-DCG are two previously proposed replica allocation schemes in MANET • The two drawbacks of the schemes are: • Generation of large amounts of traffic • Negligence of group mobility

  5. Introduction (3) • Authors address the problem by exploring group mobility • Propose Scheme DRAM to allocate replicas by considering group mobility • Underlying group mobility model is assumed to be Reference Point Group Mobility model (RPGM)

  6. Description of symbols • Symbols used in formulae and equations

  7. Mobility Models • RPGM models team collaboration where mobile nodes collaborate and move as a group • In RPGM, all mobile nodes are divided into several mobility groups • Each node is assigned to virtual reference node and movement of a reference node in a time slot is called global motion vector • The vector from the position of corresponding reference node to mobile node position is random motion vector

  8. RPGM Example • We have and where PiN(k) and PiR(k) are positions of the mobile node and reference node in time T(k)

  9. System Model • m mobile nodes M1, M2,…,Mm and n data items D1,D2,…,Dn • Each data item is updated by its original host periodically with period τi • Each node is equipped with GPS device so its location is always known • Movement of each group follows a waypoint model which breaks movement of mobile node into repeating pause and motion periods

  10. DRAM Design • DRAM (Decentralized Replica Allocation with group Mobility) is decentralized algorithm to produce effective replica allocation efficiently • Executed periodically with relocation period r time slots to adapt according to the network connectivity • Two phases in relocation period • Allocation unit construction phase • Replica allocation phase • In allocation unit construction phase, all mobile nodes in network are divided into several disjoint allocation units

  11. DRAM Design (2) • In replication allocation phase, the replicas of all data items are allocated according to access frequencies of the data items

  12. Allocation Unit Construction Phase • Three mobile nodes states • INITIAL state • ZONE-MASTER and ZONE-MEMBER states • CLUSTER-MASTER and CLUSTER-MEMBER states

  13. INITIAL State • Mobile node broadcast info message to all mobile nodes in broadcast zone with a TTL • When a node receives the info message, it forwards it to all nodes that are at TTL or lesser distance from it • Each node maintains a list of its historical locations called a position list to track its pause and motion periods

  14. ZONE-MASTER and ZONE-MEMBER states • In ZONE-MASTER and ZONE-MEMBER states • Mobile nodes are classified into two groups by the lowest-id clustering algorithm • Ones with lowest host id are selected as master of their broadcast zone enter ZONE-MASTER state • Other nodes enter ZONE-MEMBER state • Node Mi in ZONE-MEMBER state joins node Mj in ZONE-MASTER state with lowest host id within broadcast zone of Mi

  15. ZONE-MASTER and ZONE-MEMBER states (2) • Each node in ZONE-MASTER state then clusters its member nodes • All nodes within a cluster are expected to have similar motion behavior • Master node re-clusters resulting clusters again by considering motion vectors

  16. Lemmas • With help of lemmas, we have two heuristics

  17. Lemmas (2) • In a mobility group, an actual motion vector is close to the global motion vector if it has • the maximal number of neighbors in angle with maximal difference θ • Maximal number of neighbors in length with maximal difference 2ε • Develop algorithm VectorCluster in accordance with above heuristics

  18. VectorCluster • VectorCluster consists of two major procedures • ClusterByAngle • ClusterByLength • After executing VectorCluster, each zone master will select one cluster master for each resulting cluster • The selected mobile nodes will enter the CLUSTER-MASTER state, and other nodes will enter CLUSTER-MEMBER

  19. VectorCluster (2) • Result of VectorCluster in given example

  20. CLUSTER-MASTER and CLUSTER-MEMBER states • CLUSTER-MASTER and CLUSTER-MEMBER states • Tasks of nodes in this state consist of two steps • Cluster maintenance • Cluster merge

  21. Cluster Maintenance • Cluster member sends a status message to its cluster master • Cluster master checks if the moving behaviors similar to one another • It clusters motion behaviors in status messages • Dominating cluster is one with most nodes • It sends reject messages to nodes not in dominating cluster and they return to INITIAL state

  22. Cluster Merge • Merging clusters which tend to be connected in the near future improves data accessibility • Two allocation units Ci and Cj can be merged into a new allocation unit if they are cluster wiseconnected in T(k) and potentially cluster wiseconnected in T(k+r)

  23. Cluster Merge (2) • Here cluster-wise connected and potentially cluster-wise connected are defined as shown • In replica allocation construction, each cluster master will broadcast a merge message containing cluster master id and current and estimate bounding rectangles

  24. ClusterMerge Procedure • Cluster Merge can be performed by following process below

  25. Replica Allocation Phase • Objective is to • identify data items to be replicated • locations to replicate them for each allocation unit in order to maximize data accessibility • Allocation weight of data item Dj in allocation unit Cx in T(k) is • All data items are allocated in Cx according to their allocation weights in Cx in descendent order • If the candidate set of Dj in Cx is not empty, Dj will be allocated to Mi, where fij is the largest in allocation candidate set of Dj • Allocation process completes if all mobile hosts in Cx is full

  26. Procedure ReplicaAllocation • Each master unit then executes ReplicaAllocation procedure

  27. Complexity • Complexity of VectorCluster is O(|V|log|V|) where |V| is the number of input vectors • Complexity of ReplicaAllocation is O(m/|c|+n)

  28. Integration with other algorithms • Li and Wang proposed RVGM (Reference Velocity Group Mobility) • Yin and Cao proposed scheme RN to balance the tradeoff between data accessibility and query delay • Each mobile node shares only part of its storage with neighbors • A mobile node Mi only cooperates with neighbors which tend to be directly connected to it in future • Easy to integrate these concepts into scheme DRAM

  29. Performance Evaluation • Compare DRAM with E-DCG • Use event driven simulator in C++ with SIM Evaluated the performance of DRAM based on several parameters • Assume 120 mobile nodes in a 50mx50m flatland and each node owns 20 data items • Use data accessibility as measure of performance • Accessibility=Number of successful requests/Number of issued requests

  30. Performance Evaluation (2) • Use produced network traffic to evaluate cost of schemes • Effect of relocation period below • Shorter relocation period means more executions of relocation schemes making both schemes adapt quickly to relocation behavior of mobile nodes

  31. Performance Evaluation (3) • Comparison based on effect of number of Mobility Nodes and number of Mobility Groups • More nodes for same number of mobility groups means more nodes can share their storage by constructing larger allocation units

  32. Performance Evaluation (4) • Effect of Number of Replicas per Node • Effect of Update Period • Effect of Precision of Location Information • Effect of Packet Loss Rate

  33. Performance Evaluation (5) • Effect of Value of Time-to-Live

  34. Conclusions • Partitions in MANET frequent problem • Mobility of nodes important consideration for data replication • DRAM algorithm efficient in allocating replicas by considering group mobility • DRAM also produces less network traffic than prior algorithms along with producing higher data accessibility

  35. Critique • Introduction to MANET and few examples of disruptive nature of partitioning not adequate • Experiments performed only on simulated data • Lack of real world applications of DRAM and no complexity and performance analysis on real application data a drawback • Number of nodes in simulation relatively small • Consider clustering of moving object techniques similar to ones used in spatial moving objects

  36. Q/A? Thank You!

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