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Reconfiguration in Sensor Networks

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Committee. Reconfiguration in Sensor Networks. Gaurav Sukhatme (Chair) Ramesh Govindan Bhaskar Krishnamachari (Outside member). PhD Defense Karthik K Dantu. Sensor Network Deployments. Structural Health Monitoring. Forest Canopy. Marine Monitoring. Challenges. Energy - PowerPoint PPT Presentation
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Reconfiguratio n in Sensor Networks PhD Defense Karthik K Dantu Committee Gaurav Sukhatme (Chair) Ramesh Govindan Bhaskar Krishnamachari (Outside member)
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Page 1: Reconfiguration in Sensor Networks

Reconfiguration in Sensor NetworksPhD Defense

Karthik K Dantu

Committee

Gaurav Sukhatme (Chair)

Ramesh GovindanBhaskar

Krishnamachari (Outside member)

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Sensor Network Deployments

Marine MonitoringForest Canopy

Structural Health Monitoring

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Challenges

• Energy• Battery-operated• Lack of continuous energy supply• Long deployments

• Unpredictability• Dynamic network conditions• Remote deployments• Sporadic events – spatially and temporally

Sensor networks need to be adaptable!

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Thesis Contribution

Adaptability by reconfiguration

Functional(static)

Spatial(mobile)

Node

Network

Per-process energy

accounting

Robust connectivity in robot networks

Service reconfiguration in

tiered sensor networks

Route stability using cues in robot

networks

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Thesis Contribution

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

* Post-Qual* Pre-Qual

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cues

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Outline

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cues

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Accurate Per-Process Energy Accounting• Energy is a vital resource• Modern embedded systems are highly multi-

tasking running multiple tasks concurrently and numerous sensors

• Most of the tasks involve I/O operations like using storage, networking and sensors

• Observation: Resources currently used on the system need not be used by the process currently running

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LEAP2 Platform

• Representative platform for modern 32-bit embedded system

• Energy Management and Accounting Processor (EMAP2) ASIC continuous real-time energy monitoring across entire platform

• Measures energy usage in real-time for upto 16 individual subsystems

Address/Data Buffers

512MB NAND Flash

GPIO/Serial Busses

Ethernet

CF

GPS

USB Host

SODIMM 200 Connector

uWattFPGA

mPCIWLAN

CMOS Imager

Current Sensors

Sensor Power

Switching

CC2420

EMAP ASIC

LEAP2 Stacking Connector

Low Power Radio Module

LEAP2 Stacking Connector

MSP430

Current Sensors

Current Sensors

PwrC

ontr

ol

ADC IF

Mem

Bus

Low Power Radio Module (LRM)

Host Processor Module (HPM)Energy Management and Accounting Processor (EMAP2)Mini PCI Module (MPM)

Sensor Interface Module (SIM)

CMOS Imager Module (CIM)

8MB SRAM

64MB SDRAM

32MBNOR Flash

McIntire, D., Ho, K., Yip, B., Singh, A., Wu, W., and Kaiser, W. J. 2006. The low power energy aware processing (LEAP) embedded networked sensor system. In Proceedings of the 5th international Conference on information Processing in Sensor Networks (Nashville, Tennessee, USA, April 19 - 21, 2006).

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LEAP2 Software

• ARM-Linux Patch• Endoscope

• Scheduler measures energy on every scheduler tick

• Energy attributed to either user process or system

• User process energy consumption exposed to user space via the /proc/<pid>/stat

• Etop reads this file to provide realtime energy consumption info

Stathopoulos, T., McIntire, D., and Kaiser, W. J. 2008. The Energy Endoscope: Real-Time Detailed Energy Accounting for Wireless Sensor Nodes. In Proceedings of the 7th international Conference on information Processing in Sensor Networks), April 2008.

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Per-Process Energy Accounting: Caveats• System operation dominated by I/O operations

• Userspace processes get switched out due to delay• Latency of execution in kernel

• Kernel needs to track such interactions• System call interface (SCI) in *nix kernel used

for userspace-kernel transition• Single point of entry/exit in kernel

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Function call

SCI functioning in Linux (ARM)

TIME

process LibraryCall

kernelentry.S

System call

Flash

fwrite()

r7 = __NR_writeswitch to kernel (swi)

write()

vector_swicall

sys_call_table[r7]

sys_write()

donereturn

ret_from_syscall

return numbytes

writesuccessful

Processblocked

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Modifications to SCI

• Implement two new system calls sys_initiate and sys_terminate

• Call sys_initiate on invocation of every system call • Record the process id, timestamp and system call

number• The original system call is then invoked• On completion, control returns to the kernel where we

invoke sys_terminate• sys_terminate calculates the energy spent for this

system call and accurately allocates that to the corresponding process

• State for that call is then purged

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Resource Accountant

sys_initiate()(keep state)

Modifications to SCI

TIME

kernelentry.S

System call

Flash

done

return

ret_from_syscall

… call sys_initiate

vector_swicall

sys_call_table[r7] …

Resource Accountant

sys_terminate()(energy accounting)

call sys_terminate()

write(<params>)

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Modifications to SCI: Discussion

• There are a lot of system calls invoked• Eg: At bootup, about 7000 system calls before login

• For efficiency, we are currently only monitoring a small subset (sys_open, sys_close, sys_read, sys_write)

• These system calls account for majority of the system calls placed and energy consumed during the experiments

• Need to maintain state across system calls to keep track of file and socket identifiers

• Compared against our implementation of Endoscope

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Experimental Setup

• LEAP2 running a copy (cp) of a large file from compact flash to compact flash

• Concurrently running calc1, compute intensive CPU-bound process that performs some computation and goes to sleep

• Multiple system services running in the background• Ext2 filesystem and virtual memory tuned to minimize

buffering• Show energy consumption of Processor (PXA), Memory

(SDRAM) and Compact Flash (CF)

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cp and calc1: endoscope v2

Processor Speed = 104 MHz

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cp and calc1: Resource Accountant (RA)

Processor Speed = 104 MHz

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Effect of Processor Frequency Scaling

• Bottleneck is read/write from and to compact flash

• CPU can drive reading/writing even at lowest processor frequency (104 MHz)

• Processor energy consumption increases at higher frequencies

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cp and calc1: endoscope v2

Processor Speed = 312 MHz

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cp and calc1: Resource Accountant

Processor Speed = 312 MHz

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cp and calc1: endoscope v2

Processor Speed = 624 MHz

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cp and calc1: Resource Accountant

Processor Speed = 624 MHz

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Resource Accountant: Discussion

• Effective when I/O interaction takes some timeEg: GPS and other high latency sensors, flash writes

etc.• Intensive to monitor every system call

• System needs to be tailored for monitoring the significant operations only

• Does not account for optimizations at lower levels of kernel (eg: Delayed writes by ext2 fs, dirty page caching by VM, DMA, non-blocking drivers)

• Need driver support for end-end accuracy

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Related Work

Authors Work

Banga et al. Resource containers: a new facility for resource management in server systems (OSDI 1999) 

Zeng et al. ECOSystem: managing energy as a first class operating system resource (ASPLOS 2002)

Rajkumar et al. Resource Kernels (MMCN 1998)

Fonseca et al. Quanto: Tracking energy in networked embedded systems. (OSDI 2008)

Stathopoulos et al. The Energy Endoscope (IPSN 2008)

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Outline

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cuesKarthik Dantu and Gaurav S. Sukhatme, "Rethinking data-fusion based services in sensor networks," In the Third IEEE Workshop on Embedded Networked Sensors (Emnets), 2006.

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Motivation – Tiered Sensor Networks

Mote-class sensor node

Microserver node

Base Station

Transit Network

Sensor node

Gateway

Gateway

Sensor node

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Tiered Sensor Networks

• Microservers: More powerful processor and radio

• Tradeoff true distributedness for saving on communication

• Data collection from motes followed by computation at microserver

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SDP Formulation of Localization

Eijrxx jiij ),(|||| 22

Rkiik Eikdxx ),(|||| 22

Connectivity-based constraints

Range-based constraints

Pratik Biswas, Yiyu Ye, “Semidefinite programming for ad hoc wireless sensor networks localization”, In

Proceedings of conference on Information Processing in Sensor Networks (IPSN) 2004.

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SDP Formulation of Localization

jiEji kjkjjiEji ijij RR ,,,,

)()(min

Eji kjjiEji ij ,,,

1);0;1();0;1( 00 ZTs.t. 1);1;0();1;0( 00 ZT

2);1;1();1;1( 00 ZT^

2)();();( ijijijijT

ij deZe 00R

kjkjkjijT

ij EjkdeZe ,)();();(^

200

jiEjireZe ijijijT

ij ,,)();();(^

200

0,,,,,,, kjkjijijkjkjijij

Pratik Biswas, Yiyu Ye, “Semidefinite programming for ad hoc wireless sensor networks localization”, In Proceedings of conference on Information Processing in Sensor Networks (IPSN) 2004.

0Z

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Hierarchical Overlapped Coordination

Consider an optimization problem of the form -

min f (x)0)( Xh0)( Xg

subject to

Where h and g are functions and X

Rn

Nestor Michelena, Hyungju Park, Panos Papalambros and Devadatta Kulkarni, “Hierarchical Overlapping coordination under non-linear constraints”, American Institute of Aeronautics and Astronautics, 1998.

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Functional Dependence Table

hi

hN1

h1

gi

g1

gN1

x1

x i

xn

1 0 1 …

.

.

.

Columns correspond to the variables in the problem Rows correspond to constraints

Table with entries such that (i,j)th entry is 1 if constraint i

involves variable j, and zero otherwise

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Rearrange FDT

B1

A1

B2

A2

B p

A p

h1

g1

h2

g2

h p

g p

x0

x1

x p. . .

.

.

.

.

.

.

0 0

0

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Problem reformulation

min f i (d ,X i)

h i(d ,X i

) 0

g i (d ,X i) 0

subject to

i1,..., pFor each

X0d

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HOC Algorithm

1. Fix linking variables and solve problem by solving independent sub problems

2. Fix linking variables to their values determined in step 1 and solve problem by solving sub problems

3. Go to step 1 with fixed values of -linking variables determined by step 2

4. Repeat until convergence is achieved

X0

p

X 0

p

Nestor Michelena, Hyungju Park, Panos Papalambros and Devadatta Kulkarni, “Hierarchical Overlapping coordination under non-linear constraints”, American Institute of Aeronautics and Astronautics, 1998.

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Partitioning the Nodes

Motes Microservers

1

111

1

1 1 1

11

1 11

222

2

22

2222

2

2 2

1,2

1,2

1,2

1,2

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Partitioning: The Basic Idea

Motes Microservers

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SDP Relaxation for Localization

Pratik Biswas, Yiyu Ye, “Semidefinite programming for ad hoc wireless sensor networks localization”, In Proceedings of conference on Information Processing in Sensor Networks (IPSN) 2004.

jiEji kjkjjiEji ijij RR ,,,,)()(min

Eji kjjiEji ij ,,,

1);0;1();0;1( 00 ZTs.t.1);1;0();1;0( 00 ZT

2);1;1();1;1( 00 ZT ^2)();();( ijijijij

Tij deZe 00

Rkjkjkjij

Tij EjkdeZe ,)();();(

^200

jiEjireZe ijijijT

ij ,,)();();(^

2000,,,,,,,

kjkjijijkjkjijij

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Simulation Setup

• Nodes distributed uniformly randomly in a square of side 100 units

• Radius of communication assumed to be 20 units• Semi-centralized simulations had four master

nodes

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Centralized - Error vs. Iterations

Error vs. Iteration Centralized Formulation (Degree = 8)

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Distributed – Error vs. Iterations

Error vs. Iteration in Semi-Centralized Formulation (Degree = 8)

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Multiple masters

Error vs. Number of Master Nodes in Semi-Centralized Formulation

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Error – Edge Effects

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Service Reconfiguration - Summary

• Framework adaptable to services that can be framed as an optimization problem

• Spatial distribution of sensor nodes lends itself to partitioning automatically

• Partitioning can be done with a simple broadcast from master nodes

• Formulation for lifetime-aware routing presented

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Related Work

Authors Work

Niculescu et al. Localized Positioning in Ad hoc Networks (Adhoc Networks 2003)

Pratik Biswas Semidefinite Programming Approaches to Distance Geometry Problems (Phd. thesis 2007)

Savvides et al. Dynamic Fine-grained Localization (MOBICOM 2001)Aspnes et al. Theory of Network Localization (IEEE TMC 2006)

Chang et al. Maximum Lifetime Routing in Wireless Sensor Networks (ToN 2004)

Michelena et al. Hierarchical Overlapping coordination under non-linear constraints (AIAA 1998)

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Functional Reconfiguration - Summary• Per-Process Energy Accounting

• Demonstrated problem of accurate energy accounting in current embedded systems

• Designed resource accountant in the OS to perform energy accounting

• Prototype implementation on LEAP2/Linux shows accurate results

• Service Reconfiguration in Tiered Sensor Networks• Proposed semi-centralized framework for reconfiguring

services in tiered sensor networks• Evaluation done on two services – Localization and Power-

aware routing

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Outline

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cues

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Robot Networks

Marine observation Urban search and rescue

Formation control Collaborative taskingTarget tracking

Robust connectivity between the robots!

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Robot Network Biconnectivity Problem

Given a connected robot network, can they reassemble themselves with minimum movement to form a biconnected robot network? Minimum total sum distance: NP-

Hard!

Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May 2009.

Karthik Dantu and Gaurav S. Sukhatme, Biconnected Robot Networks, In submission to Transactions in Robotics Oct 2009.

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Definitions: Biconnectivity

Biconnected Graph: Given a graph G(V, E), every vertex in V has atleast two vertex disjoint paths to every other node in V

Biconnected Component: Subgraph of a given graph that is biconnected

Articulation Point:Vertices in a connected graph that connects two biconnected components. This edge or vertex is the only path between the two biconnected components

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Achieving Biconnectivity

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Biconnectivity Algorithm: Introduction• Conservative approach: Move one robot at a

time• Iterative: Improves connectivity of the network

with iteration• Each iteration potentially adds one new edge• Only needs bearing measurement to neighbors• Three phases in each iteration

• Compute biconnected components• Identify potential neighbors • Command movement

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Biconnectivity Algorithm

•Phase I – Distributed biconnected components computationE.J.H. Chang. Echo algorithms: Depth parallel operations on general graphs. IEEE Transactions on Software Engineering, 8(4):391–401, 1982.• Phase II – Identify nodes

to move• Articulation points compute bearing of

all neighbors• Identify nodes to move

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Biconnectivity Algorithm - II

• Phase III - Move nodes• Articulation points

negotiate for least expensive move

• Least expensive node is chosen

• Command is sent based on relative angle – according to table shown

Node 1

Node 2 Q1 Q2 Q3 Q4

Q1 -al - 45

135 - ah

-al

180 - ah

45 - al

ah - 135

al - 270

90 - ah

Q2 45 - al

135 - ah

90 - al

270 - ah

135 - al

315 - ah

Q3 135 - al

315 - ah

180 - al

360 - ah

Q4 225 - al

415 - ah

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Biconnectivity Algorithm: Simulation

• Simulations done using Player/Stage simulator

• Studied various randomly generated topologies (connected)

• Results shown for 16-node and 20-node networks

• Results averaged over 25 simulation trials

actual

computed• Study error in

• Relative Bearing• Translation

executed

• Turn angle

commanded

executed

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Robustness to Bearing Error

Gaussian error with zero mean, deviation shown on x-axis

Algorithm robust to large bearing error (upto 30°)!

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Robustness to Odometry Error

Gaussian error with zero mean and deviation as a percentage of the

command given

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Robustness to turn angle error

Gaussian error with zero mean and deviation as shown on x-axis

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Robot Experiments

• iRobot Create with e-boxes• Antenna raised for better

signal strength readings• Use radio to compute

relative bearing• OLSR routing protocol• Neighbor list obtained from

routing table

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Robot Experiments - bearing computation• Sample signal strength in the robot

neighborhood according to pattern• Measure 100 samples at each point • Perform 3-D Principal Component

Analysis• Assume major axis as the direction of

neighbor • Experimented with wifi and zigbee

radios• Varied step size to study effect on

bearing estimation• Chosen step size 2 m

Step size

Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May 2009.

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Biconnectivity: 5-node Topology

Trial 1 Trial 2Edge Actual

angleMeasured angle(T1)

Bearing error (T1)

Measured angle(T2)

Bearing Error (T2)

3-4 5 9.3 4.3 -12.4 17.4

3-2 120 148.1 28.1 166.8 46.8

3-1 -160 -147.2 12.8 -129.1 30.9

4-3 170 202.5 32.5 148.2 21.8

4-5 -30 -18.3 11.7 -4.3 25.7

5-4 150 172.1 22.1 175.4 25.4Average bearing error is 24.32°

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Related Work - Biconnectivity

Authors Work

Ahmadi et al. Keeping in Touch: Maintaining Biconnected Structure by Homogeneous Robots (AAAI 2006)

Poduri et al. Using Local Geometry for Tunable Topology Control in Sensor Networks (IEEE TMC 2009)

Tarjan Depth first search and Linear Graph Algorithms (SIAM J Computing 1972)

Frederickson et al. On the Relationship between the Biconnectivity Augmentation and Travelling Salesman Problems (TCS

1981) 

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Thesis Contribution

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cues

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Robot Networks

Marine observation Urban search and rescue

Formation control Collaborative taskingTarget tracking

Temporal robustness of routes using mobility cues

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Route Stability: Problem

• Control algorithms expect a stable graph • Routing protocol optimizes data forwarding

path• Path stability not a requirement for routing• Frequent route switches confuse control

algorithm• Requirement unique to robot networks

Karthik Dantu and Gaurav S. Sukhatme. Connectivity vs. control: Using directional and positional cues to stabilize routing in robot networks. In International Conference on Robot Communication and Coordination (ROBOCOMM) April 2009.

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Route Stability: Example

B

A

C DE

F

G

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Route Stability: Example

B

A

C DE

F

G

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Route Stability: Example

B A

C

DE

FG

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Route Stability: Example

BA

C

DE

F

G

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Route Stability: Example

B

A

C DE

F

G

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Route Stability: Our solution

• Robot networks have information about direction of movement and possibly position

• This can be used to choose routes that are more likely to be stable

• Modify OLSR - a popular adhoc routing protocol

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Stability Metric: Direction Cue

Direction Cue

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Stability Metric: Location and Direction Cue

• Stability metric is equal to the link duration

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Modifying OLSR: Route computation

• Added direction information base and position information base

• Modified Hello messages to incorporate direction, position and stability

• New routes are picked based on the best stability value instead of shortest path

• If a route exists, it is not modified unless the stability value falls below a threshold

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Simulation Setup

• Ns-2 with OLSR implementation from U Murcia, Spain• Deploy robots in 500x500 area• Mobility of robots is based on random waypoint mobility

model• Radius of communication is approximately 50 units –

propagation model is two ray ground• Results are averaged over 10 trials each• Percentage decrease in number of route switches is

measured as the success metric

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Effectiveness – Direction Cue Only

Speed of travel vs. Route Stability

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Effectiveness – Direction and Location Cue

Speed of travel vs. Route Stability

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Effectiveness – Direction and Location Cue

Density vs. Route Stability

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Effect of error in location cue

Gaussian error with zero mean and deviation as a percentage of the radius of

communication

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Effect of error in direction cue

Gaussian error with zero mean and deviation in degrees on x-axis

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Simulation Results

• Directional cues improve stability by about 8%-10%

• Direction and position together improves route stability by about 15%-20%

• Error in position is not very detrimental to the route stability unless the error is high

• Error in direction has a greater impact on route stability

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Related Work – Route Stability

Ko and Vaidya Using Location Information to Improve Reactive Routing in MANETs by Using Directional Flooding(Wireless Networks 2000)

Karp and Kung Greedy Perimeterless State Routing for Wireless Networks (Mobicom 2000)

Ramachandran et al.

Route Stability in Static Mesh Networks(PAM 2007)

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Thesis Contribution

Functional ReconfigurationA. Per-Process Energy AccountingB. Reconfiguration of essential services

in a sensor network

Spatial ReconfigurationA. Go from a connected network to

a biconnected networkB. Achieve route stability using

directional and positional cues

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F i g h t O n !

Trojan Cricket Club

SC Badminton

Armando

UCLA Ascent Lab

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Publications (Thesis-related)JOURNAL • Karthik Dantu, and Gaurav S. Sukhatme. Biconnected robot networks. In

Submission to Transactions on Robotics, October 2009.• Karthik Dantu and Gaurav S. Sukhatme. Service Reconfiguration in Tiered

Sensor Networks, In preparation.CONFERENCES AND WORKSHOPS• Karthik Dantu, and Gaurav S. Sukhatme, “Per-Process Energy Accounting:

Handling Asynchrony in Embedded Systems”, In Submission Dec 2009. • Karthik Dantu and Gaurav S. Sukhatme. Connectivity vs. control: Using

directional and positional cues to stabilize routing in robot networks. In International Conference on Robot Communication and Coordination (ROBOCOMM) April 2009

• Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May 2009.

• Jesse Butterfield, Karthik Dantu, Brian P. Gerkey, Odest C. Jenkins, and Gaurav S. Sukhatme. Autonomous biconnected networks of mobile robots. In IEEE Workshop on Wireless Multihop Communications in Networked Robotics (WMCNR), Apr 2008.

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Publications (Thesis-related)

5. Dustin McIntire, Timothy Chow, Karthik Dantu, Mansi Shah, Thanos Stathapoulos, Gaurav S. Sukhatme, William J. Kaiser The Low Power Energy Aware Processing (LEAP) Software Applications - Poster and Demonstration, In IPSN-SPOTS ’07: Proceedings of the 5th international conference on Information processing in sensor networks.

6. Karthik Dantu and Gaurav S. Sukhatme. Detecting and Tracking level sets of scalar fields using a robotic sensor network. In IEEE International Conference on Robotics and Automation (ICRA), pages 3665–3672, Apr 2007.

8. Karthik Dantu and Gaurav S. Sukhatme. Rethinking data-fusion based services in tiered sensor networks. In IEEE Emnets ’06: Third Workshop on Embedded Networked Sensors,May 2006.

9. Karthik Dantu, Mohammad H. Rahimi, Hardik Shah, Sandeep Babel, Amit Dhariwal, and Gaurav S. Sukhatme. Robomote: enabling mobility in sensor networks. In IPSN-SPOTS ’05: Proceedings of the 5th International Conference on Information Processing in Sensor Networks.

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Publications (Other)

1. Anand Joshi, Mi Zhang, Ritesh Kadmawala, Karthik Dantu, Sameera Poduri, and Gaurav S. Sukhatme OCRdroid: A Framework to Digitize Text Using Mobile Phones In ICST International Conference on Mobile Computing, Applications and SErvices (MobiCASE),October 2009

2. Avinash Parnandi, Ken Le, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri and Gaurav S. Sukhatme, “Coarse In-Building Localization with Smartphones”, to appear in ICST International Workshop on Innovative Mobile User Interactivity (IMUI ’09) alongwith MobiCASE 2009, October 2009, San Diego, USA.

3. Dheeraj Kota, Neha Laumas, Urmila Shinde, Saurabh Sonalkar, Karthik Dantu, Sameera Poduri and Gaurav S. Sukhatme, “deSCribe: A Personalized Tour Guide and Navigational Assistant”, to appear in ICST International Workshop on Innovative Mobile User Interactivity (IMUI ’09) alongwith MobiCASE 2009, October 2009, San Diego, USA.

4. John Caffrey, Ramesh Govindan, Erik Johnson, Bhaskar Krishnamachari, Sami Masri, Gaurav S. Sukhatme, Krishna K. Chintalapudi, Karthik Dantu, Sumit Rangwala, Avinash Sridharan, Ning Xu, and Marco Zuniga. Networked sensing for structural health monitoring. In International Workshop of Structural Control Jun 2004.

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Publications (Non-thesis related)

5. Krishna Chintalapudi, Jeongyeup Paek, Omprakash Gnawali, Tat S. Fu, Karthik Dantu, John Caffrey, Ramesh Govindan, Erik Johnson, and Sami Masri. Structural damage detection and localization using netshm. In IPSN-SPOTS ’06: Proceedings of the 5th international conference on Information processing in sensor networks.

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Lifetime Aware Routing

j iBij Sj

AjijiqQijq:

),(),(),(

kBi0),( ijq

Max Tsystem

and for every i V

Mkikq 0),( and

s.t.

i iBj Bj

irt Eijqejiqe ),(.),(.

where

i iBj Bjirt

ii Eijqejiqe

ET),(.),(.

Page 89: Reconfiguration in Sensor Networks

calc1 and scp: Resource Accountant


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