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Using Movement Prediction To Reduce Energy Consumption in Wireless Communication

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### Using Movement Prediction To Reduce Energy Consumption in Wireless Communication

David K. Y. Yau

Department of Computer Science

Purdue 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 model
- Wait for pause_time seconds
- Pick a random new destination
- Pick a random velocity
- Move steadily to the chosen destination
- 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:

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

A

t

Problem With BD HeuristicToo 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:

- 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:

- 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

t

MD Heuristic - Example- 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:

- 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.

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.

A

t

Use of One Bounce RuleIf a node is moving away from the target, average keeps decreasing at each time step and finally we choose the worst alternative.

- 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:

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 MD Heuristic

- With random waypoint mobility model.

Performance of MD Heuristic

- With regular waypoint mobility model.

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.

Observed Delay vs. Maximum Allowable Delay

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

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.

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