1. 1 1 Quantifying Impact of Mobility on Data Availability in Mobile Ad Hoc Networks Takahiro Hara IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 9, NO. 2, FEBRUARY 2010.

2. 2 2 Mobility model 1. Random Walk (RW) –At every unit of experimental time, each MH randomly determines a movement direction and speed from 0 to V [m/s]. 2. Random WayPoint (RWP) –MHs selects a random destinations with the speed. After reaching the destination, it again pauses, and then, repeats this behavior. 3. Manhattan Mobility (MM) –MM emulates the node movement on streets where nodes only travel on the pathways in the map. 4. Reference Point Group Mobility (RPGM) –Each group has a logical “center” called a reference point. MHs moves to the reference point (nearby) based on the RWP model. 5. Random Waypoint with Locality (RWP-L) –The concept of Home area. –MHs choose a random destination insider the home area with high prob. H and one outside the region with prob. 1-H. H: the home area ratio.

3. 3 For Data availability (data storage capacity) –1. Average size of partitions (Network) –2. Distribution of partition sizes (Network) –3. Size of partitions belonged to (Node) –4. Change in size of partitions belonged to (Node) –5. Distribution of connected nodes (Node) For Data distribution (data replication) –6. Total number of connected nodes (Direct) –7. Total number of data-reachable nodes (Indirect) metrics

4. 4 1. Average size of partitions Time tttttt = l * t l: 幾個 interval m: 節點數 n 1 : 表示第 1 個 t 時間內的 partition 個數 l = 6 T

5. 5 5 2. Distribution of partition sizes

6. 6 6 3. Size of partitions belonged to M k : Mobile node k Q: 在 t i 時間，某 1 個節點，有不同二個 partition 的連結。

7. 7 7 4. Change in size of partitions belonged to

8. 8 8 5. Distribution of connected nodes

9. 9 9 6. Total number of connected nodes 7. Total number of data-reachable nodes

10. 10 7. Total number of data-reachable nodes C s,f,Mj denotes a set of mobile nodes that M j have connected to during the duration from t s to t f. N i denotes a set of mobile nodes that M k first connected to at the beginning of t i but have never connected to before that. R i denotes a set of mobile nodes that mobile nodes in N i connected to from the beginning of t i until the end of the observation time l’ * t

11. 11 Reference Point Group Mobility Map:2500m x 2500m Node:300 Transmission range: 100m 1. Average size of partitions

12. 12 1. Group mobility 2. Random Way Point 3. Manhattan Mobility 4. Random Waypoint with Locality 5. Random Walk 2. Distribution of partition sizes

13. 13 1. Group mobility 2. Random Way Point 3. Manhattan Mobility 4. Random Waypoint with Locality 5. Random Walk 3. Size of partitions belonged to

14. 14 4. Change in size of partitions belonged to

15. 15 5. Distribution of connected nodes

16. 16

17. 17 6. Total number of connected nodes

18. 18 7. Total number of data-reachable nodes

19. 19 T as 10,000,000 seconds t =1, l = 10,000,000 The first 1000s is removed.

20. 20

21. 21 Random Walk PropertyMany small partitions Mobility is very low in the long term Not good in terms of storage capacity and data distribution Data distribution Protocol should not rely on data sharing with a large number of nodes but should data with a small number of connected nodes. Data diffusion Protocol should consider effective data disseminations to reduce the traffic Random Way Point PropertyFew large partitions Mobility is middle Good in storage capacity when the node belongs to a large partition Data distribution Which nodes it shares data by considering several factors such as the number of neighboring nodes and stability of wireless links Data diffusion Designer does not need to be very nervous for the performance in terms of distribution rapidness. It should address the reduction of unnecessary data redistribution to reduce excessive data traffic

22. 22 Manhattan Mobility PropertyMany small partitions but much fewer isolated nodes than other models. Data distribution It is effective to share data among nodes in the same partition. Data diffusion Reduce data redistribution because maximum capacity of partitions is small. Reference Point Group Mobility PropertyLarge partition (Best in storage capacity) Very high connectivity among nodes in the same group Data distribution Share data among nodes in the same group. Data diffusion Address the reduction of unnecessary data redistribution

23. 23 Random Waypoint with Locality PropertyThe node whose home area is the center of the entire area gave better performance than the node whose area is a corner of the entire area. Data distribution Total number of connected nodes is greatly affected by the increase in node density because the increased number of nodes can bridge isolated partitions. Data diffusion Similar to Random Walk model, it should aggressively disseminate data to connected mobile nodes.

24. 24 Related work In [8] –Network wide metrics: Number of partitions Average size of partitions Average partition change rate –Node-centric metrics: Node partition change rate Node separation time –The first two metrics represent the capacity of data storage (memory space) of each partition –The larger the partition is, the more data can be stored in it. –The other three metrics just represent how frequently members of each partition change or how long before each pair of two nodes disconnects. [8] J. Hahner et.al. “Quantifying Network Partitioning in Mobile Ad Hoc Networks,” Proc. Int’l Conf. Mobile Data Management, pp. 174-181, 2007.

25. 25 Motivation –More specifically, these metrics cannot distinguish whether only one node disconnects from the partition or the partition is split into two partitions with the same size. –Also, they do not represent how many nodes each node connects with at a certain interval. Thus, they do not truly represent the dynamism of partitions. –In this paper, we propose new metrics to represent the dynamism of partitions in MANETs.