Utility-Driven Spatiotemporal Sampling using Mobile Sensors

Yang Yu, Loren J. RittlePervasive Platforms and Architectures Lab, Application

Research Center, Motorola LabsINFOCOM 2008

2009.05.25 Junction

Outline

• Introduction• Contribution• System model• Utility-Driven Mobility Scheme• Simulation Results• Conclusion

Introduction

• Mobile Sensor Nodes– Enable better sensing coverage with a relatively small

number of nodes.– Enable a re-configurable network for better event

sampling with a limited number of nodes

Contribution

• Propose a parameterized sampling utility function to measure the sampling quality of an event– Event: priority and spatiotemporal properties– Sampling utility: Captures the information entropy of

gathered sensor data as a concave function

• Propose a community-based distributed protocol for mobility scheduling Maximizing the overall sampling utility in a network

System Model• Nodes are uniformly and

independently distributed• Events ei:

• Utility function– ei occurring at time duration

– Pi: the set of nodes covering ei over Ti

– Ci(t): the covered area of ei by Pi

1

2

3

4n

n mobile nodes { }nimi ,...,2,1

r

events { },...2,1iei pi: importance levelli: location of ei

ai: event areadi: time duration (exponential distribution with mean τi )

ii dT ,0

)()( iiiii IfpPU id

ii dttCI0

)( is an increasing, concave function capturing the information entropy of Ii, with The utility is maximized when enough nodes fully cover ai throughout Ti

)(if0)0( if

System Model

• Realistic Examples of Utility Function– f(I) depends on the joint entropy of samplings from

multiple nodes.– Let κ denote the distance between two nodes

3 classes of f(I):E1: when the correlation coefficient is , f(I) scales as as E2: when the correlation coefficient is , f(I) scales as asE3: when samplings of sensor nodes are independent to each other, f(I) scales as as

2e

e)(ln IO I

I)ln( IIO

I)(IO

Utility-Driven Mobility Scheme

• Stable neighbors (s-neighbors)– If the expected link quality between them is above a pre-

specified threshold – Maximum Neighboring Distance (MND)

Utility-Driven Mobility Scheme

• Assumptions– All mobile nodes have unique ID and are capable of

localizing and synchronizing themselves– All nodes are aware of the geographical shape of the field

and the average time duration, τi , for every event.– All mobile nodes can move omnidirectionally, in a speed

with expected value v.– Nodes covering an event are capable of evaluating the

importance level and utility function of the event.– The wireless communication range is larger than the

sensing range.

Utility-Driven Mobility Scheme

• Based on community– Behaves as a basic operation unit changing information

• Consist of two fundamental operations:– Discovery operation– Recruit operation

Discovery Operation

• DIS packet (location, sensor reading)• Disperse (MND, event boundary)• Form community (leader)– collecting sensor readings – location information– Derive the utility function

12

34

n

community

Recruit Operation

• Broadcast RM (Recruit Message)– A leader decides that the c-event is not partially covered– )(,,,,,),(, s

isi

diiiiii tIttalzfp

the time when the event is first discovered

the time when the RM is sent out from Pi

The amount of data samples gathered till time ti

s

si

di

t

t

ii dttCI )(

RM

sit

rit

si

ri tt 1

v

2

Estimating PMU• PMU (Potential Marginal Utility)– Movement is only consider when c-event is partially

covered, i.e.,– Probability for ei to still exist by time ti

s+δ (δ=δ1+δ2)2r

az ii

iedttdttd idi

sii

di

siii

PrPr

Given: utility function The size of the community zi at time ti

s

: number of nodes may also reach ei during δ

-> estimate PMU to cover ei as if all these qi nodes will cover ei by time ti

s+δ

– assuming the size of Pi increases linearly from zi to zi+qi

– expected data samples gathered for ei at time tis+δ is

– then, PMU is estimated as

– Receive RMs from multiple communities, it estimates the PMU for each community and choose one with probability

)(Ifp ii

2iq

2

2)()( r

qztItI iisi

si

))((),( ' siiii tIfpaiG

If G(I,a) > Gth: cover ei

j

iaia ajG

aiGejoinsm

),(

),(Pr

Impact of Distance

Operation Detail

• Adaptation to Event Dynamics– Updated RMs and re-evaluate PMU

• Joining the Target Community– Broadcast a hello message and coordinate with other members to

improve the coverage

• Balance of Event Sensing and Network Coverage– Certain opportunistic cost is paid by switching to sense existing

events– Gth as a way of modeling the opportunistic cost

• Efficient Propagation of RMs– To avoid overwhelming communication cost– Counter-based flooding technique TTL

• Post-event Movement

Simulation Results

• Implementation of UMD– Modify the packet-level TOSSIM to support both

synchronized node mobility and customized ADC sensing interface.

– The link quality between nodes was re-established using the TOSSIM’s empirical model

– Use ADC channels to distinguish various event types– All nodes performed synchronized ADC sensing at every

second

Simulation Results• Events: k=30, start time was uniformly chosen between

[0,200]• Time duration: exponential distribution with τi=30

• Gth=0.1

UDM has significant utility improvement: 22-114% over stationary13-56% over random mobility

Simulation Results• Up to 13%, 45% and 128% improvement over the stationary deployment• Up to 7% 20% and 63% improvement over the random mobility scheme• For E1, E2, E3 respectively

Conclusion

• Propose a parameterized utility function to model the spatiotemporal sampling quality of events.

• Provide a utility-driven mobility scheme, UDM– Distribute computing and autonomous decision making– Robustness to node and communication failures

• Simulation results demonstrate significant utility improvement of UDM over both stationary and random mobility schemes.