Top-k Monitoring in Wireless Sensor Networks

Minji Wu, Jianliang Xu, Xueyan Tang, and Wang-Chien Lee

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 19, NO. 7, JULY 2007

Outline Introduction Filter-based Monitoring Approach

FILA Overview Query Reevaluation Filter Setting (Uniform versus Skewed) Filter Update (Eager versus Lazy)

Performance Study Simulation Setup Eager versus Lazy Filter Update Performance Comparison against TAG and Range

Caching Conclusions

Introduction Top-k Query

Environmental Monitoring A top-k query is issued to find out the

nodes and their corresponding areas with the highest pollution indexes for the purpose of pollution control or research study.

Network Management A top-k query may be issued to

continuously monitor the sensor nodes with the least residual energy.

Introduction In traditional database systems

Focused on snapshot top-k queries

This paper focuses on continuously monitoring top-k queries in sensor networks. Utilize previous top-k result to obtain

a new top-k result.

Top-1 query TAG (S. Madden et al. , OSDI ’02)

BS

C

A B

t1

t1 t1

t2

t2 t2

t3

t3 t3

35

38

37

43

45

48

51

56

52

43 51

51

45 56

56

48 52

52

A total of nine messages are sent

Top-1 query Range Caching (C. Olston et al., SIGMOD’01)

BS

C

A B

t1

t1 t1

t2

t2 t2

t3

t3 t3

35

38

37

43

45

48

51

56

52

4852

48 A total of four messages are sent

[39, 47]

[47, 80]

[20, 39]

Problem Definition Consider a top-k monitoring query that con

tinuously requests the (ordered) list of sensor nodes R with the highest readings, that is

FILA Overview

(1) Filter Setting the base station computes a filter [li, ui] for each sensor

node i and sends it to the node for installation.

(2) Query Reevaluation(3) Filter update

Query Reevaluation

Sensor-initiated updates (1) Internal update (2) Join update (3) Leave update

Internal update

Leave update

Join update

Critical bound

A Simple Case

Consider a simple case where only one sensor-initiated update is received by the base station

Only n1 needs to be probed

A Simple Case

Only the sensor nodes whose currentreadings are higher than v2’ respond to the probe

General Cases

Tinternal : the set of internal updates Tjoin : the set of join updates Tleave: the set of leave updates T : the old top-k set

If |T'| = |T| - | Tleave| + | Tjoin| k the new top-k set must be a subset of T'

Otherwise, if |T'| < k the nodes that are not in T' have to be probed.

An Example of Top-3 Monitoring

Another Example of Top-3 Monitoring

Filter Setting

Uniform filter setting

It is simple and favorable when the readings of all sensor nodes follow a similar changing pattern.

Filter Setting Skewed filter setting

taking into account the changing patterns of sensor readings.

Suppose the average time for the reading of node i to change beyond is fi() 1/fi() : the rate of sensor-initiated updates by n

ode i

Filter Setting We let every node measure the average delt

a change di of their sensor readings at a fixed rate.

Skewed filter setting

Filter Update Eager filter update

If a new filtering window [li', ui'] is different from the old one [li, ui] then the new filter [li', ui'] is immediately sent to node i

Lazy filter update If a new filtering window [li', ui'] fully contains

the old one [li, ui], that is, [li', ui'] [li, ui] then the base station delays the filter update until node i’s reading violates the old filter [li, ui].

Performance Study Simulation Setup

Energy cost in transmitting a message

s : message size : distance-independent term (50 nj/b) : coefficient (100 pj/b/m2) q: distance-dependent term ( 2) d: distance

Energy cost in receiving a message is set at 50 nJ/b

Performance Study

A Sensor initiated update message: Sensor ID : 4 bytes Sensor Reading: 4 bytes

A filtering window is characterized by 8 bytes.

Network Layouts

Real Data Traces Simulated using the real traces provided by the Live from Earth and

Mars (LEM) project at the University of Washington.

Two kinds of sensor readings are used temperature (TEMP) Dew point (DEW) logged by the station at the University of Washington from August 2004 t

o August 2005

Total 500000 sensor readings Extract many subtraces starting at different dates Each subtrace contains 20000 readings The subtraces were used to simulate the physical phenomena in the im

mediate surroundings of different sensor nodes.

Real Data Traces

Evaluation Metrics Network Lifetime

the network lifetime is defined as the time duration before the first sensor node runs out of power.

Average Energy Consumption It is defined as the average amount of energy

consumed by a sensor node per time unit. Monitoring Accuracy

This is defined as the mean accuracy of monitored results against the real results.

Eager versus Lazy Filter Update(multihop, k =10)

Network lifetime. Average energy consumption.

Eager versus Lazy Filter Update

Energy consumption by layer

Performance Comparison against TAG and Range Caching(single hop, k =3)

Network lifetime.Average energy consumption.

Performance Comparison against TAG and Range Caching (single hop, k =3)

Monitoring accuracy

Performance Comparison against TAG and Range Caching(Multihop, k =10)

Network lifetime. Average energy consumption.

Performance Comparison against TAG and Range Caching(Multihop, k =10)

Monitoring accuracy

Conclusion

This paper exploited the semantics of top-k query and proposed a novel energy-efficient monitoring approach called FILA.

Two filter setting algorithms (that is, uniform and skewed) and two filter update strategies (that is, eager and lazy) have been proposed.

Filter Setting Under random walk model

0.50.5

l

The average time for the reading to change beyond can be expressed as