Using Movement Prediction To Reduce Energy Consumption in Wireless Communication

David K. Y. YauDepartment of Computer SciencePurdue University

Objective

Reducing energy consumption of battery powered devices, e.g., Laptops and Handhelds, in wireless networks. Wireless communication is power

intensive. Can we exploit node movement to

reduce energy use in communication?

Presentation Outline

Basic observation Power saving strategy System model Heuristics Simulation results Conclusion Future work

Motivation

Wireless networks are getting popular. Increasing interest in mobile ad hoc networks

Easy and low cost deployment Mobility No infrastructure Highly dynamic

Problems Routing – nodes keep moving in and out of the network. Security – selfish, malicious, uncooperative nodes. Scalability. Limited battery life.

Network communication – major energy drainer. For handhelds over 50% of the battery life can be consumed by network interface card!

Improvements in battery technology - lifetime has increased. However, not to the extent to keep up with the increased energy requirement.

Needs software level energy saving strategies.

Movement Prediction

Observation: Reduced distance between communicating peers ⇒ Reduced transmission power requirement ⇒ Energy saving.

Assuming network interface has transmission power control capability.

Single hop communication – obvious Multi hop communication – expected

Power Saving Strategy

If likely to move closer to the target, postpone communication for a future time. Assuming application can tolerate

some delay k. Needs movement prediction

Based on movement history.

Network Structure

Mobile nodes are moving within a rectangular plane.

We divide the network into virtual grids. Each grid has a unique grid ID.

Assumptions

Each node knows it's position – GPS. Each mobile host maintains a

sequence of n previous grid IDs. Initial assumption –

target is fixed. Every mobile node knows the target’s

location. Relax the fixed target assumption –

Both communicating peers are mobile.

Mobility Model

Defines a stochastic process which tells us how a mobile node moves in a network.

Random waypoint mobility model1. Wait for pause_time seconds2. Pick a random new destination3. Pick a random velocity4. Move steadily to the chosen destination5. Upon reaching the destination, repeat the steps 1 through

4 Regular waypoint mobility model

Introduce regularity Home – work – home model with occasional diversions Choose new destination – not completely randomly Two parameters –

Regularity r Periodicity T

Terminology

History of node h:Sh = {x1, x2, …, xn}

A window of size l (for i ≤ n-l+1):W(i,i+l-1) = {xi, xi+1, …, xi+l-1}

W(i,i+l-1) is a subsequence of Sh.

Distance between two grids i and j: d(i,j).

Binary Distance (BD) Heuristic

Calculate the probability p that a mobile node will be in grid ID y within the next k time units as follows:

)1/()(

)),1(Pr(

+−=++=

knycontainingksizeofSinwindowsofnumberycontainsknnWp

h

Communicate immediately if p is less than some probability threshold pth. Else, postpone communication.

Problem With BD Heuristic

t

A

A

Too coarse granular idea of distance – Counts only when the communicating node is in the same grid as the target.

Binary Markov Distance (BMD) Heuristic

Based on order-m Markov model. Calculate the probability that a mobile node

will be in grid ID y within the next k time units as follows:

),,,|Pr()Pr( 2211111

1

21

∑∑∑ ====== −+−++==

+

N

imnmnmnn

N

i

N

in

m

ixixixyxyx LL

Problems:

• Higher computational overhead.

• Same coarse granularity problem as BD.

Markov Distance (MD) Heuristic

Let R be the set of all possible routes that can be taken by the mobile node in the next k time units

Let R1, where R1 R, contain those routes in R that have at least one location closer to the target than the current distance.

Then, we calculate the probability that a mobile node will move closer to the target as:

∑∑

∈

∈=

R

R

routethetakingofyprobabilit

routethetakingofyprobabilit

p

ρ

ρ

ρ

ρ

)(

)(11

1

If p ≥ pth, then we postpone the communication, else we communicate immediately.

• Higher computational overhead

• Distinguishes the distance between the node and the target on a finer level

MD Heuristic - Example

1ρ 2ρ

t

A3ρ

Consider three possible paths of node A:

• ρ1 moves closer to the target in the next two time steps.

• ρ2 and ρ3 do not move closer to the target in the next two time steps.

• If, these were the only options and A takes any of these paths with equal probability, then the probability that A will move closer to the target is: 1/3.

Average Distance (AD) Heuristic

Calculate the average distance between a mobile node and the target over all windows of size k in the mobile node's movement history as:

∑ ∑+−

=

+−

=

=1

1

1

),(1kn

j

kj

jii yxd

kavg

If the current distance between the mobile node and the target is greater than avg, then the mobile node decides to postpone the communication, or else it communicates immediately.

• Less Computational overhead

• Takes into consideration the actual distance

Analogy With Secretary Problem

Secretary problem: one must make anirrevocable choice from a number of applicants whose values are revealed only sequentially.

Our problem: we must choose one time step when a node communicates and once it communicates it is done.

Solutions to the secretary problem might help designing solutions to our problem.

37% Rule and The Least Distance (LD) Heuristic

Best-choice(r) Algorithm: reject the first r-1 candidates. Then accept the next candidate whose relative rank is 1 among the candidates seen till now. Accepts the best candidate with probability 1/e ≈

0.368. Optimal solution.

Choose the time when the distance is the minimum seen till now.

LD Heuristic: find Minimum as:

Postpone communication if current distance is greater that dmin, else communicate immediately.

),(min yxdMindhSx∈∀

=

Single Threshold Solution

Select the first candidate whose valueexceeds a pre-specified threshold value.

Applicable only to the full information problem.

Parameters can be estimated from partial observation.

Average Distance heuristic – threshold is the average seen till now.

One-bounce Rule

Keep checking values as long as theygo up. As soon as they go down we stop postponing any more and take thecurrent value.

postpone as long as the distance between the mobile host and the target is decreasing, and communicate as soon as the distance starts increasing. Ignores the history other than the last value. Use this idea along with AD heuristic.

Use of One Bounce Rule

If a node is moving away from the target, average keeps decreasing at each time step and finally we choose the worst alternative.

t

A

A

Solution: Directional Average Distance Heuristic

• Take direction of movement into consideration.

• If at any point of time, moving away from the target, communicate immediately.

Moving Target Simple modifications to the heuristics proposed

works for moving target. Assume a mobile host s with location history Ss =

{x1, x2, …, xn} wants to communicate with node r with location history Sr = {y1, y2, …, yn}.

MD heuristic: just define R and R1 with respect to Sr instead of y.

AD heuristic: Define average as:

LD heuristic: define minimum distance as:

∑ ∑+−

=

+−

=

=1

1

1

),(1kn

j

kj

jiii yxd

kavg

),(min yxdMindhSx∈∀

=

Preliminary Experiments

Number of Grids: 3 x 3 Cost of single communication C(d) for distance d is d2.

10000 repetitions. Target Location: Randomly

chosen for each run.

Performance of BD Heuristic

• Poor performance.

Performance of BMD Heuristic

Performance of MD Heuristic

• With random waypoint mobility model.

Performance of MD Heuristic

• With regular waypoint mobility model.

Performance of AD Heuristic

Performance Comparison of BD, BMD, MD and AD.

Simulation Experiments

Network size: 1500m x 1500m Number of Grids: 3 x 3 Number of nodes: 20 Maximum speed: 10 m/s Simulation time: 20000 seconds Routing protocol: DSR Propagation model: Two-ray ground. Target Location: fixed at the center of

the network.

Performance of MD Heuristic for Varying Probability Threshold

Performance of MD Heuristic for Varying Regularity

Performance of AD Heuristic for Varying k

Performance of AD and LD Heuristics for Varying Regularity

Performance of LD Heuristic for Varying k

Result for Mobile Target – Single hop Communication

Result for Mobile Target – Multi hop Communication

Observed Delay vs. Maximum Allowable Delay

• We get higher energy saving by setting k higher, but without increasing the observed delay significantly.

Energy Consumption Due to CPU Processing

Comparison Among Heuristics

Summary

Our strategy predicts a good time for communication, when some amount of delay is tolerable.

We postpone the communication until that point and then communicate.

Simulation results show significant energy saving.

Conclusion

Wireless networking is rapidly emerging as the future communication technology

The components of an ad hoc network are mostly battery-powered handheld devices.

Limited battery life is an important issue in wireless networking.

We address the issue of exploiting node movement to reduce the energy cost of communication. We can save more that 50% of the communication

cost. Computation cost of movement prediction

algorithm is low.

Future Work

Location information for moving target in ad hoc networks.

Considering transmission duration in predicting a good time for communication.

Optimal way to divide the network into grids.

Thank you!