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Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs. José Brustoloni, Sherif Khattab , Christopher Santamaria, Brian Smyth, and Daniel Mossé C S @ P I T T. Mobile Ad-Hoc Networks (MANETs). Cell phone. Mobile Ad-Hoc Networks (MANETs).

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integration of application layer scheduling and routing in delay tolerant manets

Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs

José Brustoloni, Sherif Khattab, Christopher Santamaria, Brian Smyth, and Daniel Mossé

CS@PITT

communication of first responders
Communication of First-Responders
  • Connecting handheld devices used to exchange data, images, video, voice, work orders, etc.
need for manets
Need for MANETs
  • MANETs are

needed if traditional

communication

infrastructure is

damaged

network partitioning
Network Partitioning
  • MANETs may get partitioned:
    • Field characteristics
    • Noisy environments

Packethop.com

delay tolerant networking
Delay-Tolerant Networking
  • DTN research deals with routing of messages across network partitions
  • Our work proposes a new approach to DTN routing
example scenario
Example Scenario

Main Partition

Subordinate Partition

Leader

EOC

example scenario1
Example Scenario

Main Partition

Subordinate Partition

Work Order

Leader

EOC

example scenario2
Example Scenario

Main Partition

Subordinate Partition

Work Order

Courier

Leader

EOC

work order model
Work Order Model

Execution Time

Deadline

Pre-emption

Deadline Miss

Time

work order model1
Work Order Model

Each task associated with a location

Deadline

courier selection problem
Courier Selection Problem

Main Partition

Subordinate Partition

?

?

?

Work Order

Courier

Leader

EOC

metrics
Metrics
  • Percentage of missed deadlines =
  • Average traveled distance per node

Number of deadlines missed

Total number of work orders

state of the art
State-of-the-art
  • Dedicated mobile elements
    • Message Ferries [@GeorgiaTech] handle only message delivery
    • Minimize average delay
  • Trajectory modification of mobile users
    • @Dartmouth [Mobicom’00]
    • Minimize detour distance
our hypothesis
Our Hypothesis
  • We can achieve better trade-off between missed deadlines and traveled distance if application-layer demand is taken into consideration in courier selection
highest slack courier selection
Highest-Slack Courier Selection

Main Partition

Subordinate Partition

Maximum Leeway

compared schemes
Compared Schemes
  • Closest
    • Select courier closest to work order destination
  • Dedicated
    • Set of nodes dedicated for message delivery (don’t execute any work)
  • Random
common assumption
Common Assumption
  • Leader aware of current position of main-partition workers
    • GPS-enabled devices
    • Landmarks
simulation parameters
Simulation Parameters
  • Rate of work orders (load)
    • main and sub-ordinate
    • default = 60%
  • Distance between partitions
    • default = 1200m
  • Number of dedicated couriers
    • default = 1
  • Speed of dedicated couriers
    • default = 5 m/s (18 km/h)
distance between partitions
Distance between Partitions

100%

Dedicated

Random

Closest

Highest-Slack

80%

60%

Deadlines Missed

40%

20%

1200m

200m

400m

800m

subordinate partition load
Subordinate-partition Load

100%

Random

Closest

Dedicated

Highest-Slack

80%

60%

Deadlines Missed

40%

20%

20%

40%

60%

80%

slide23
Why?

Main Partition

Subordinate Partition

subordinate partition load1
Subordinate-partition Load

16km

12km

Random

Traveled Distance Per Node

8km

Closest

Highest-Slack

4km

Dedicated

20%

40%

60%

80%

main partition load
Main-partition Load

100%

Dedicated

Random

Closest

Highest-Slack

80%

60%

Deadlines Missed

40%

20%

20%

40%

60%

80%

dedicated courier speed
Dedicated-courier Speed

100%

Random

Closest

Dedicated

Highest-Slack

80%

60%

Deadlines Missed

40%

20%

54 km/h

18 km/h

25 km/h

number of dedicated couriers
Number of Dedicated Couriers

100%

Random

Closest

Dedicated

Highest-Slack

80%

60%

Deadlines Missed

40%

20%

5

1

10

15

20

conclusions and future work
Conclusions and Future Work
  • Courier Scheduling in partitioned ad-hoc networks
    • Integrated application- and network-layer scheduling
  • More realistic
    • models of work orders
    • metrics (e.g., rate of casualties)
    • frequency and structure of network partitions
  • Comparison with other schemes
    • communication bridges
work order parameters
Work Order Parameters
  • Average Deadline = 440 sec
  • Execution Time = 0.5 * Deadline
  • Enough to run back and forth across a 500m partition and still meet deadline
simulation time
Simulation Time

Cool-down

Statistics Gathering

Warm-up

10000

(~ 2.5 Hrs)

1000

10

9500

Time (Seconds)

when to return home
When to return home?

Main Partition

Subordinate Partition