1

Prediction Assisted Single-copy Routing in Underwater Delay Tolerant Networks

Zheng Guo, Bing Wang and Jun-Hong Cui

Computer Science & Engineering Department, University of Connecticut, Storrs, CT, 06269

IEEE Globecom 2010

2

Outline

Introduction Network Model Aggressive Chronological Projected Graph (ACPG) Prediction Assisted Single-Copy Routing (PASR) Performance Evaluation Conclusion

3

Introduction

Many routing protocols have been proposed to deal with the lack of contemporaneous end-to-end paths in delay tolerant networks (DTNs).

Due to node mobility and sparse node deployment, UWSNs can be treated as DTNs. Limited bandwidth High power consumption Mobility patterns

4

Introduction

UWSNs are extremely resource stringent since acoustic communication.

Furthermore, the mobility patterns in an UWSN can vary dramatically over time depending on the environment.

These two characteristics render existing multi-copy based DTN routing protocols unsuitable for UWSNs. Waste Energy

5

Goal

Proposed a generic scheme, prediction assisted single-copy routing (PASR), for UWSNs.

PASR can be instantiated to efficient single-copy routing protocols under different mobility models.

6

Network Model

Consider a data collection underwater sensor network, which consists of M layers.

Surface

Layer 1

Layer 2

Layer M

Super Source

……

Sink

Sensor

Current Link

Water Currents

Each Sensor：

Buffer

Battery

7

Aggressive Chronological Projected Graph (ACPG)

Proposed a greedy algorithm to capture the network mobility properties and the common characteristics of near optimal routes.

ACPG compresses the evolving network topology and connectivity to a single graph ( , ) chronologically𝐺 𝑉 𝐸 , efficiently finds routes from the graph in a slot by slot manner.

8

Aggressive Chronological Projected Graph

Construction G(V, E) Vertex vij V is the ∈ jth node in the ith layer.

Edge (vij, vkl ) ∈ E represents the connection between these two nodes during a certain time slot .𝑡

Layer 1

Layer 2

Layer 3v31 v32 v33 v34

v21 v22 v23 v24

v11 v12 v13 v14

(v21, v12) vd

vs

(u,v,t,C)

u

v

the capacity of that connection

9

Aggressive Chronological Projected Graph

A. Construction G(V, E) Vertex vij V is the ∈ jth node in the ith layer.

Edge (vij, vkl ) ∈ E represents the connection between these two nodes during a certain time slot .𝑡

Node v ∈ V can be in two status: inactive or active.

Node v Uv ： The upstream node.

Iv(i) ： The maximum number of packets

that can be transmitted or received

during the ith slot. Cv(i)： The available storage in the ith slot

Pv ： The residual power for transmissions

v31 v32 v33 v34

v21 v22 v23 v24

v11 v12 v13 v14

vd

vs

Layer 1

Layer 2

Layer 3

10

Aggressive Chronological Projected Graph

B. Operations of ACPG The operations of ACPG at each slot t, t > 0, include two

routines: 1.Edge projection

during which connections in a

time slot are projected to G as edges

2.Routes reservation and graph update during which routes are discovered

and G is updated.

Layer 1

Layer 2

Layer 3v31 v32 v33 v34

v21 v22 v23 v24

v11 v12 v13 v14

vd

vs

(u,v,t,C)

u

v

(v32 ,v23,2,5 )(v32 ,v23,5,4 )

11

Aggressive Chronological Projected Graph

An example of projected graph for 12 nodes in 3 layers.

Layer 1

Layer 2

Layer 3v31 v32 v33 v34

v21 v22 v23 v24

v11 v12 v13 v14

vd

vs

Inactive node

Active node(1,5) (2,7)

(3,5)/(7,9) (4,6)/(8,5)

(5,3)(6,6)(7,7)

(9,10)

(vd ←v12 ← v22 ← v31 ← vs)

Capacity： 5 Packets

Delay： 8 slots

(vd ←v12 ←v22 ← v23 ← v33 ← vs)

Capacity： 2 Packets

Delay： 7 slots(7,4)

(7,2)

(9,5)

12

Performance of ACPG

Evaluate the performance of ACPG by comparing it with optimal solutions from integer linear programming (ILP).

Each sensor has Buffer size=30 packets Transmission range=100 m

3rd layer generating packets from the 500th second to the 1000th second with the rate of one packet per second.

600m

600m

90m

Layer 1

Layer 2

Layer 3

13

Performance of ACPG

Comparison of ILP and ACPG.

14

Prediction assisted single-copy routing (PASR)

A. How to works PASR? Propose prediction assisted single-copy routing (PASR), that

utilizes ACPG in a training period to capture the characteristics of the mobility pattern, and provide guidance on route selection. Historical information Guidance from ACPG Predict the future

15

Historical information

If the mobility pattern is stable for a long time, the history can tell the future. The most widely used historical information includes. Recent trajectory Average contact duration Average inter-contact duration Last contact time Contact frequency

16

Guidance from ACPG

The following properties of routes and node contacts, which are closely related to the underlying mobility pattern, can be captured by ACPG: Geographic preference Contact periodicity Contact probability

17

Predict the future

After ACPG characterizes the mobility pattern, it suggests what historical information can be used for prediction.

18

Prediction assisted single-copy routing (PASR)

B. Instantiating PASR Consider three mobility models in an underwater sensor

network. UWSN in Regular Currents UWSN in Currents with Randomness UWSN in Irregular Currents

Each sensor has Buffer size： 100 packets Transmission range： 50m Transmission rate： 50 packet/s Power capacity： 300 to 30 Slot duration： 10s

800m

800m

40m

Layer 1

Layer 2

Layer 3

19

UWSN in Regular Currents

1)Guidance from ACPG:Focus on two properties: 1.Geographic preference 2.Contact periodicity.

Layer 1

Layer 2

Layer 3

Sink

Super Source

Geographic preference

20

UWSN in Regular Currents

2) Protocol following ACPG:

Based on the above guidance, proposed a specific PASR for this network, energy efficient history prediction assisted routing (EEHPA).

21

UWSN in Regular Currents (EEHPA)

2) Protocol following ACPG

This scheme includes two essential operations:

1. Prediction update Each node u maintains its own prediction vector (PV), which is a vector

of tuples (i,v,Dv).

i is the prediction slot v is the best relay in this slot Dv is the expected delay through this relay to the sink

2. Per-contact forwarding decision (1) Dv ∈ [Dv’ , Dv’ + δ1)

(2) Dv ∈ [Dv’ + δ1, Dv’ + δ2] and node v is in the upper layer.

PV

δ1,δ2 are called prediction error tolerances

u

v

22

UWSN in Currents with Randomness

The randomness models the impact from environment, which may lead to estimation errors and prediction errors in real systems.

PASR can tolerant these errors to some extent since ACPG just captures the general properties of the majority of nodes, who exhibit similar mobility patterns.

(EEHPA)

23

Assume that nodes in the first two layers will be affected by an irregular water current.

Modify EEHPA to obtain a new PASR, named iEEHPA, according to the new guidance.

Two guidance from ACPG:

(1) A node in the same layer is preferred

(2) Only predict for nodes in the same layer

UWSN in Irregular Currents (iEEHPA)

Layer 1

Layer 2

Layer 3

Sink

Super Source

Irregular water current

Regular current

10sAnchored node

24

Performance Evaluation

Each sensor has Buffer size=100 packets Transmission range=50m Transmission rate=50 packet/s Power capacity = 300 to 30 Slot duration = 10s

Bottom layer randomly generate 300 packets from the 500th second with the total generation rate of one packet per second.

800m

800m

40m

Layer 1

Layer 2

Layer 3

Sink

Super Source

25

Performance Evaluation EEPA : without kinematic modelFC : First ContactEpidemic : A flooding based scheme

26

Performance Evaluation

27

Performance Evaluation

28

Conclusion

Present a generic scheme prediction, assisted single-copy routing (PASR), for UWSNs.

Design online heuristic protocols by choosing appropriate historical information and forwarding criteria based on the guidance from ACPG.

Investigate an UWSN with various mobility patterns and randomness using two instantiated PASR schemes, EEHPA and iEEHPA.

Simulation results show that ACPG captures the properties of various mobility patterns and provides corresponding guidance, and the instantiated PASR schemes outperform others.

29

Thanks for your attention

Happy Lunar New Year