SenSys 2003 Differentiated Surveillance for Sensor Networks Ting Yan Tian He John A. Stankovic...

SenSys 2003

Differentiated Surveillancefor Sensor Networks

Ting YanTian He

John A. StankovicDepartment of Computer Science, University of Virginia

November 5, 2003

November 5, 2003ACM SenSys 2003 - Differentiated Surveillance

Outline

Problem Statement Basic Sensing Coverage Protocol Enhanced Protocol with Differentiated

Surveillance Evaluation Conclusions


The Problem

Leverage redundancy of deployment to save power and still maintain a specified degree of sensing coverage.


An Example

Even harder to decide schedules when nodes are deployedwith a random distribution and distributed decisions.


Differentiated Surveillance

> 100%

= 100%

< 100%

Most Important

Important

Less Important


Contributions

Developed one of the first protocols to address the differentiated surveillance problem

Achieved as much as 50% reduction in energy consumption and as much as 130% increase in the system half-life compared to other state-of-the-art schemes


Goals Provide an approach for nodes to decide

their sleep/work schedules: guarantee different degrees of coverage redundant nodes go to sleep to save

energy and extend system lifetime Other features

balance energy consumption minimize computation and

communication costs


Assumptions

Nodes are not mobile Localization and Synchronization Sensing area: a circle with radius r

can be relaxed Communication range > 2r

can be relaxed


Work/Sleep Schedule for a Single Point

Global period T and common starting time Point x is covered by at least one node’s

sensing area at ANY time

AB

C

Point xNode A

Node B

Node C

Awake

0 10030 70

10 60

5 45time

Asleep


Decide Single Point Schedule

Reference randomly selected from [0, T) Each node broadcasts tuple (location, reference) Work Schedule: [n*T + ref - Tfront, n*T + ref + Tend] Total work time is minimized Full coverage is still guaranteed

AB

C

Point xSchedule of Each Node for Grid Point x

refC refA refB refCt

t0 100

20 40 90 120

30 655 105

Tfront Tend


Schedules for All Grid Points

Similar procedures for other geometric points – cover the target area with a grid and calculate each node’s schedules for the grid points it can cover

Grid size selection - neither too large nor too small How to integrate schedules for all grid points on a single

node?

AB

C

Grid Point x

D

Grid Point y

Schedules for Grid Point yrefA refDt

t

refA

0 100

40 70 1405 55 105


Put them all together

Choose the UNION as the schedule of the node for ALL the grid points node A is able to cover

Self-evident that the full coverage for each grid point is guaranteed Integrated schedule may be longer than needed

Node A’s schedules for Grid Point aGrid Point bGrid Point c

Grid Point z

Node A’s integrated schedule

.

.

.

.

.

.

0 1005 65

6545

5 50

T=100


Differentiated Surveillance

200%

= 100%

< 100%

Most Important

Important

Less Important

?

?


Extension - 200% Coverage

AB

C

Point xSchedules for Grid Point x

refC refA refB refCt

t0 100

20 40 90

t0 100t0 100

We only need to double Tfront and Tend of the integrated schedules for 200% coverage - or shrink them for less than 100% coverage (multiplied by desired degree of coverage alpha)

120Node A

Node BNode C

20 120

12040

40

90

90

20


Issue: Energy Balance Energy consumption unbalance among

nodes due to random selection of reference numbers

Multi-round extension to decrease the variation Each node selects N reference numbers

with an iid distribution Get N schedules with the same algorithm Compose these N schedules

consecutively


Issue: Schedule Redundancy

An integrated schedule may be longer than needed due to the union operation

Second pass optimization to reduce redundancy


Cost Analysis – Computation and Communication

Communication Broadcast only once at the initial phase Local communication Only (location, reference) transmitted

Computation - typically 10K~100K inst. For one grid point

Calculate distance: * (#neighbor within 2r) Lay out references: c * (#neighbor)

Run it for all grid points it can cover: * (#grid) Integrate: c * (#grid)


Related Work (I)

F. Ye et. al., “ Energy-Efficient Robust Sensing Coverage in Large Sensor Networks,” UCLA technical report 2002 Each node probes a neighborhood for

working nodes each time it wakes up Cons - more communication, holes


Related Work: Sponsored Coverage

D. Tian et. al., “A Node Scheduling Scheme for Energy Conservation in Large WSNs”, Wireless Communications and Mobile Computing Journal, May 2003

underestimated “sponsored sector” per-round communication overhead


Simulation Configuration Sensing range 10m, Communication range 25m 160X160 Field, nodes deployed with an iid uniform

distribution, the inner 100X100 area measured Repeated 100 times with different random references

and node deployments, 90% CI < 10% meanTarget area

Measured area


Total Energy Consumption

The Differentiated Surveillance protocol outperforms the Sponsored Coverage scheme by as much as 50% reduction in total energy consumption.

050

100150200250300350400450

1 1.5 2 2.5 3 3.5 4

Node Density (#node/r*r)

Tot

al E

nerg

y C

onsu

med

(J

oule

s/M

in) All Working

Sponsored CoverageBasic Design

2nd Pass OptimizationIdeal Lower Bound


Half-Life of the Network

010002000300040005000

1 1.5 2 2.5 3 3.5 4

Node Density (#node/r*r)

Tim

e (m

in) Sponsored

CoverageBasic

2nd PassOptim ization

The Differentiated Surveillance protocol outperforms the Sponsored Coverage scheme by as much as 130% increase in the half-life of the network.


Differentiated Surveillance Result

0

50

100

150

200

250

1 1.2 1.4 1.6 1.8 2

Desired Degree of Coverage

Tot

al E

nerg

y C

onsu

mpt

ion

(Jou

le/M

in)

Density 5 / r*r

Density 10 / r*r

Total Energy Consumption – linearly increasing with alpha


A protocol that achieves both energy conservation and differentiated degree of sensing coverage

lower computation and communication overhead

longer network lifetime

Conclusions


Questions? Thank you!

SenSys 2003 Differentiated Surveillance for Sensor Networks Ting Yan Tian He John A. Stankovic...

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