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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.

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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.

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• 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

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1. Average size of partitions

Timet t t t t t

= l * t

l: 幾個 intervalm: 節點數n1: 表示第 1 個 t 時間內的 partition 個數

l = 6T

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2. Distribution of partition sizes

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3. Size of partitions belonged to

Mk: Mobile node k

Q: 在 ti 時間，某 1 個節點，有不同二個 partition 的連結。

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4. Change in size of partitions belonged to

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5. Distribution of connected nodes

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6. Total number of connected nodes

7. Total number of data-reachable nodes

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7. Total number of data-reachable nodes

Cs,f,Mj denotes a set of mobile nodes that Mj have connected to during the duration from ts to tf .

Ni denotes a set of mobile nodes that Mk first connected to at the beginning of ti but have never connected to before that.

Ri denotes a set of mobile nodes that mobile nodes in Ni connected to from the beginning of ti until the end of the observation time l’ * t

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Reference Point Group Mobility

Map:2500m x 2500m Node:300Transmission range: 100m

1. Average size of partitions

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1. Group mobility2. Random Way Point

3. Manhattan Mobility

4. Random Waypoint with Locality5. Random Walk

2. Distribution of partition sizes

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1. Group mobility2. Random Way Point

3. Manhattan Mobility

4. Random Waypoint with Locality5. Random Walk

3. Size of partitions belonged to

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4. Change in size of partitions belonged to

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5. Distribution of connected nodes

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6. Total number of connected nodes

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7. Total number of data-reachable nodes

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T as 10,000,000 secondst =1, l = 10,000,000The first 1000s is removed.

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Random

Walk

Property Many 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

Property Few 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

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Manhattan Mobility

Property Many 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

Property Large 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

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Random Waypoint with Locality

Property The 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.

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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.

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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.