gupta datamule

Rajesh K. Gupta

Department of Computer Science and EngineeringUniversity of California, San Diego

DAC 2011, San Diego.

Controlled Mobility for Scalability and Efficiency in CPS Spatio-temporal Processing

Thought-line

1. Embedded systems when scaled to societal levels become Cyber-Physical Systems

2. (Spatiotemporal) data gathering and processing are central to CPS

3. Processing and communication architectures are central to scalability and efficiency of data gathering.

2

Sensor Localization [INFOCOM 2011]

Controlled Mobility for Scale & Efficiency [TOSN 2010, TMC 2010, INFOCOM 2009]

Sensor Localization [INFOCOM 2011]

Controlled Mobility for Scale & Efficiency [TOSN 2010, TMC 2010, INFOCOM 2009]

3

Mobility Often Differentiates Sensor Networks

Habitat monitoring [Mainwaring et al., 2002]

Environmental monitoring [Batalin et al., 2004] Emergency response [Malan et al., 2004]

ZebraNet [Juang et al., 2002]

Farm management [Sikka et al., 2006]

[Kansal et al., 2004] [Todd et al., 2007][Vasilescu et al., 2005]

None Predictable Random Voluntary?

Controlled

Either sensor data moves through a network of sensor nodes (Either sensor data moves through a network of sensor nodes (MonitoringMonitoring), or ), or Sensed object moves through a network of sensor nodes (Sensed object moves through a network of sensor nodes (TrackingTracking))

4

Our Pet Project with Los Alamos National Labs

http://www.lanl.gov/projects/ei/

• SHM (Structural Health Monitoring) application– Post-event assessments for large-scale civil structures– Passive sensors; measuring peak displacement

• Data collection by UAV (Unmanned Aerial Vehicle)– Communication with sensors via ZigBee radio– GPS-based autonomous control

• Quick data collection is required– Limited fuel

5

A network is not always the best way to move data

• Sensor nodes must form a network

• Network needs to be connected

• Need non-trivial resources for the network – Networking and communications drains

nodes (some more than the others)– Need to make sure that the nodes stay alive

and stay reachable

Base station

Sensor nodes

Data Mule Advantages

• Reduced hops– Less congestion– Less retransmissions, higher network capacity– Less synchronization errors

• May even be faster to send large data over constrained bandwidths

• Simpler nodes, energy harvested sensors• DM as a resource delivery platform.

6

Base station

Data mule

Data mule approach

Sensor nodes

Optimize the motion (path, speed) of the mule to improve data delivery latency

Sensor nodes

7

About an eight-year old problem• Data Mobile Ubiquitous LAN Extensions by Shah+Roy, WSN 2003

– Random mobility

• “Mobile Router” by Kansal+Srivastava, MobiSys 2004: Controlled mobility along a fixed path– Controlled mobility on periodic routes, builds upon Directed Diffusion.

Problems

Assumptions

RemarksComm. only at node

Speed of data mule

Instant move/stop

[Zhao et al., 03] Path + Speed

Variable Heuristic algorithm

[Kansal et al., 04] Speed Variable Adaptive heuristic algorithm

[Somasundara et al., 04] Path Constant + Stop

NP-hardness; Heuristic algorithm

[Ma, Yang, 06] Path Constant Heuristic; Assume negligible comm. time

[Ma, Yang, 07] Path Constant + Stop

Heuristic; Stop to communicate

[Xing et al., 07] Path Constant + Stop

Heuristic;

MANETs with mobile nodes: Epidemic Routing, Message Ferrying, Many-to-many comms.

Data Mule Scheduling (DMS) Problem

• Path Selection Problem (1D DMS)

• Energy-latency tradeoffs with DMS

• DMS as a proxy for other important CPS problems– scale-parameterized scheduling problems

(DVFS)

8

9

B

Idea of DMS formulation

• Assumptions– Communication is possible only within the intervals– Communication takes fixed amount of time– Node location, communication range/time are given

A

C

Data mule

Communication range

Data mule's path

LocationABC

eB

eC

eA

Communication timeNode

10

Terminology and definitions

Time

A

B eB

C eC

eA

Jobs

Release time Deadline

Feasible interval

Execution timeSimple jobs

Location

A

B

C

eB

eC

eA

Location jobs

Simple location jobs

General location jobs

In real-time scheduling …

In DMS (Data Mule Scheduling) problem …

D eD

General jobs

D eD

Execution time

Release location Deadline location

Feasible location interval

11

B

Idea of DMS formulation (1/2)

• Consider communication as a location job– Location constraint: Feasible location intervals– Time constraint: Execution time

A

C

Data mule

Communication range

Data mule's path

LocationABC

eB

eC

eA

Execution timeLocation jobs

12

Idea of DMS formulation (2/2)

Location

ABC

eB

eC

eA

Location jobs

Time

A'B'C'

eB

eC

eA

Jobs

+ Time-speed profile (i.e., change of speed over time)

Set ofLocation jobs

Set of(real-time) jobs

Time

A'B'C'

eB

eC

eA

Jobs

Faster speed

• Location job is mapped to "job" when the speed is given– Real-time scheduling problem

13


• Three subproblems– Path selection

• Which trajectory the data mule follows– Speed control

• How the data mule changes the speed along the path– Job scheduling

• From which sensor the data mule collects data at certain time • Objective

– Minimize the total travel time (data delivery latency)

1-D DMS

Path selection

Communication range

node A

node B node C

Location job

Speed control

Speed

Time

Location

ABC

Execution time

e(A)e(B) e(C)

Job scheduling

Time

Execution time

A’B’C’

Job

Time

A’B’C’

e(A)e(B) e(C)

14

1-D DMS: Closer look

AB

LocationC

Location

Speed

Time

Time

ABC

Input: Set of location jobs Time-Speed profile (Solution for the problem)

Corresponding Real-time Scheduling problem

Time

Time-Location profile (determined by Time-Speed profile)

Execution time

Execution time

Location job

Job

e(A)e(B) e(C)

e(A)e(B) e(C)

15

1-D DMS: Job scheduling

• Simple jobs (i.e., one feasible interval)

– Earliest deadline first (EDF) is an optimal online scheduling algorithm [Liu, Layland, 1973]

• “Always execute the job with the earliest deadline”

• General jobs (i.e., multiple feasible intervals)

– No optimal online algorithm: Proof by an adversary argument• An adversary can make any online schedulers fail by releasing a new job

– Offline: Linear programming (LP) formulation

2z Time

1e

2e

3e

25x

11x 12x

22x 23x

13x 14x 15x

33x 34x 35x 36x

26x 27x

16

1-D DMS: Speed control

• Three different cases:– Simple cases

• Constant speed• Variable speed

– General case• Variable speed with acceleration constraint

17

Speed control: Simple cases

• “Processor demand” (for simple jobs)– Sum of execution time of the jobs that are completely contained in the

interval

• Feasibility test [Baruah et al., 1993] [Yao et al., 1995]

– Optimal offline algorithm from processor speed scaling by YDS. – Find minimum maximum speed from all ‘tight’ intervals. – Move any faster, there exists at least one infeasible interval.

timet1 t2

job 1: e1job 2: e2

job 3: e3

job 4: e4

(Const. speed, Variable speed)

Pr : set of release timePd : set of deadline

A set of simple jobs is feasible

Proc. demand for any

Feasible interval of job

18

Complexity of 1-D DMS problem

Simple jobs General jobs

Offline Online Offline Online

Real-time scheduling EDF [Liu, Layland, 1973] LP Non-existent

1-D DMS

Simple case

Constant speed

Non-existent LP Non-existent

Variable speed

(vmin = 0)

LP Non-existent

(vmin > 0) Non-existent

General case

Variable speed with acceleration constraint

Open Non-existent

(fixed k 2)

NP-hard

Non-existent(k arbitrary)

NP-hard in the strong sense

Contributions

Hard problems Design heuristic algorithm

19

Heuristic algorithm for general case (1/2)

• Simplify– Convert all general location jobs to simple location jobs– Proportionally distribute the execution time

• Maximize– Increase the speed until a tight interval is found

• Tight interval: Processor demand = interval length

Location

SpeedFull accel/decel at the maximum rate

Location

Speed

Tight interval

Accelinterval

Decelinterval

Plateau interval

Proc. demand = time duration

Generallocation job

A

Executiontime

ExecutiontimeSimple

location job

A1

A2

A3

20

Heuristic algorithm for general case (2/2)

• Trim– Eliminate all fixed intervals from remaining jobs

• Fixed interval: Intervals that the speed is already determined• Execution time of each location job is changed accordingly

• Recursion– Repeat from “Maximize” for the remaining intervals

Location

Speed

Recursively maximize

Accel intervalLocation

Decel intervalLocationLocation

Tight interval

Location

Speed

Tight interval

Accel interval Decel intervalPlateau interval

21

Example: General case

Lo

catio

n jo

b

Job

Job

Speedcontrol

Jobscheduling

(Variable speed w/ accel. constraint)

heuristicsolution

Not only works very well in practice but subject to convex optimization via SDP.Not only works very well in practice but subject to convex optimization via SDP.

22

1-D DMS

Path selection

Communication range

node A

node B node C

Location job

Speed control

Speed

Time

Location

ABC

Execution time

e(A)e(B) e(C)

Job scheduling

Time

Execution time

A’B’C’

Job

Time

A’B’C’

e(A)e(B) e(C)


• Three subproblems– Path selection

• Which trajectory the data mule follows– Speed control

• How the data mule changes the speed along the path– Job scheduling

• From which sensor the data mule collects data at certain time • Objective

– Minimize the total travel time (data delivery latency)

23

Path selection problem

• Objective:– Find a path s.t. the induced 1-D DMS problem has the minimum travel time– Problem: Difficult to find such a path

• Interrelation between path and travel time is unclear• Infinite choices of path

• Idea: Simplify the problem as a graph problem– Find the shortest tour that covers all labels

s

1

2 3

4

5

Location12345

start end

Location12345

endstart

Location12345

start end

24

Example (40 nodes)

r = 100 r = 200 r = 300

r = 1 r = 20 r = 50

25

Experiments

• Methods and parameters– 20 nodes randomly deployed in circular area (radius: 500m)– Each node has data that needs 10 secs for transmission– Data mule movement: m/s– Average of 100 trials

Proposed algorithm successfully exploits larger communication rangeProposed algorithm successfully exploits larger communication range

To

tal t

rave

l tim

e (

sec)

Communication range

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120

No remote communication, constant speede.g., [Ma, Yang, 2006], [Xing et al., 2007]

Remote communication, constant speed, stop to transmite.g., [Ma, Yang, 2007]

Remote communication, visit all nodes, variable speede.g., [Zhao et al., 2003]

Label Covering TSP Formulation [Sugihara TOSN 2010]

26

Energy-Latency Tradeoff

• Objective: – Add flexibility to energy-latency trade-off

• Idea: – Combine multihop forwarding and data mule approach

Energy consumption

Data delivery latency

Data mule

Multihop forwarding

27

Hybrid approach

• “Forwarding” subproblem – Determine where to gather data under energy constraint– Objective:

• Find a forwarding strategy s.t. the induced DMS problem has the minimum travel time

– Difficult

• Simplified: Forward as close to the base station as possible

28

Results: Connected network

0

50

100

150

200

250

300

350

0 5 10 15 20

Energy consumption limit

Tim

e (s

ec)

Travel time

Lower bound of forwarding time

29

Preliminary result: Disconnected network

0

100

200

300

400

500

600

700

0 5 10 15 20

Tim

e (s

ec)

Travel time

Lower bound of forwarding time

Energy consumption limit

30

DMS with uncertainty

Location

Known comm. range

Unknown comm. range

Data mule’s path

• Idea: Semi-online scheduling– Assumption: “Communication is possible in the vicinity of

node”• ... plus unknown communication range

– Strategy• Offline scheduling with known communication range• Opportunistically exploit unknown communication range

– As in online scheduling

Idea of 2-D Semi-online Algorithm

A

B C

DE

P

Node 1

Known comm. range

Unknown comm. range

Data mule’s path

Node 2

Job execution in offline schedule

Actual job execution

BS

Example(Simulation on Matlab, 20 nodes)

Closing Thoughts

• DMS provides a framework for solving spatiotemporal data collection problems

• Scale-parameterized scheduling– Real-time scheduling where some parameters are

scaled by a factor• Discretize location, speed and time• Find an appropriate step size for each of these three that

guarantees an approximation ratio• In the discretized configuration space, use dynamic

programming to find the optimal trajectory• Similarity with speed scaling

– Inverse relationship between data mule's speed and processor speed

• Ongoing work addresses assumptions made…33

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