DATA AGGREGATIONSiddhartha SarkarRoll no: 13000111128CSE-4th Year-7th semester

Sensor Networks (CS 704D) Assignment

OVERVIEW Introduction

Basic Idea

Why Data Aggregation in WSN?

What is Aggregation?

System Model

Data Aggregation Process

Tiny Aggregation

System Diffusion

Energy consumption

Aggregation Queries

Secure Aggregation

Conclusion

WSN nodes perform sensing of a physical environment. The sensed data from multiple sources is collectively used to make inferences.

Large amount of raw data

Correlated data

Communication in the network is significantly reduced by:

Elimination of redundant data

Accumulation and processing at intermediate nodes

INTRODUCTION

Basic Idea..To exploit the data redundancy

Packets from different nodes, are combined in – network.

ImplementationWho carries the data with redundancyData-centric routing

DifferencesData-centric routingBased on contents of the packets.Address-centric routingRouting based on an end-to-end manner.

What is Aggregation?

I

C D

B

E

HA

F

G

Base Station

JK LM

NWireless Sensor Node

Data Transmission

Legend

v1 v2

v3

vi Sensor Measurement

f(v1, v2, v3)

Communication is the most energy consuming functionality

Energy consumed in transmitting one bit over 100m 1000× Energy consumed per instruction execution

Efficient bandwidth utilization

Why Data Aggregation in WSN? – I

Cost of computing an aggregate such as AVERAGE on a binary tree:

Message count per query response without aggregation in a binarytree of depth d: S = 2(1 20 + 2 21 + 3 22 + : : : + d 2d-1) = (d - 1)2d+1 + 2 N lgN;N is the number of nodes.In general, S grows as N logb N, b is the branching factor.

Why Data Aggregation in WSN? - II

Message count if aggregation is used : NMessage count if aggregation is not used : Nlogb N

Why Data Aggregation in WSN? - II

System Model

Tree with N nodes and a sink.Time-slotted and synchronized network.Aggregated event data needs to reach sink within

a deadline.Arbitrary set of source nodes.Sink requires aggregated form of data:

Symmetric Functions – f(x , y) = f(y , x). Function value does not depend on sensor

identity. Aggregation functions supported – MIN, MAX,

Sum, Mean, Variance, Higher order statistics etc.

There are several aggregation techniques followed in Wireless Sensor Network , Such as

Tree based Aggregation

In network Aggregation

Data Aggregation Process

Sensor nodes are organized into a tree hierarchy rooted at the Base Station

Non-leaf nodes act as the aggregators

Tiny Aggregation

Distribution phaseAggregate queries are pushed down into the network

Collection phaseAggregate values are continuously routed up from children to parents

Routing: via a tree rooted at the sink. Routing tree formation algorithm (invoked periodically):

Do upon receiving message M(n; l) from node n at level l

if this node's level > l + 1 this node's level = l + 1

this node's parent = n broadcast M(this node's id, l + 1)

Detection of a leaf node n0: n0 does not hear any message of the form M(n’ , .)

Tiny Aggregation (TAG)

Query Model: Single table called sensors Append-only relational tabl eOne attribute per sensing functionality

Form of queries:SELECT {agg(expr), attrs} FROM sensors WHERE {selPreds} GROUP BY {attrs} HAVING {havingPreds} EPOCH DURATION iExample:SELECT {MAX(temperature),building} FROM sensors WHERE block = ALL GROUP BY building HAVING MAX(temperature) > 100 EPOCH DURATION 60s

Tiny Aggregation (TAG)-II

The aggregation clause:

An initializer i

A merging function f

An evaluator e

The aggregated record <Z> = f(<x>, <y>), where <x> and <y> are partial records.Example: AVERAGEA partial record is the tuple <SUM, COUNT>i SUM) = <SUM, 1>f (<SUM1, COUNT1>, <SUM2, COUNT2>) = <SUM1+SUM2,COUNT1+COUNT2>e(SUM, COUNT) = SUM/COUNT.

Tiny Aggregation (TAG)-III

A classification of aggregates:

Tiny Aggregation (TAG)-III

Synopsis Diffusion

Motivation: If trees are used for data aggregation, such as in TAG, a link failure leads to loss of data from an entire sub-tree.

However, if aggregation of duplicate sensitive aggregates, such as COUNT, is done on a graph, one needs to solve the problem of making the process insensitive to duplicate messages.

Synopsis is a digest of data

Partial aggregates are represented by synopses

Order and duplicate insensitive (ODI)

synopses

The aggregation process:Let i denote sensor data, s denote synopsis and a denote the desired aggregate.

Synopsis generation function SG : i s

Synopsis fusion function SF : (s1; s2) s Synopsis evaluation function SE : s a

Synopsis Diffusion

Finding ODI synopses and their fusion functions is the main difficulty.We look at two cases, one trivial and the other not so trivial!

Example 1: ODI synopsis for MAX. Let X be the variable.

Synopsis : X (the number itself)SG() = XiSF(Xi; Xj) = The larger of Xi; XjSE(Xi) = Xi

Synopsis Diffusion-II

ODI Correctness Test:

A synopsis diffusion algorithm is ODI-correct if SF and SG are order and duplicate-insensitive functions.

Synopsis Diffusion-III

ODI Correctness TestA synopsis diffusion algorithm is ODI-correct if SF and SG are order and duplicate-insensitive functions.

Define a projection operator q : Multiset of sensor readingsordered set of values. SG preserves duplicates: 8r1; r2 2 R : q(fr1g) = q(fr2g) ) SG(r1) = SG(r2). The same synopsis is generated for all duplicates.

SF is commutative: 8s1; s2 2 S : SF(s1; s2) = SF(s2; s1).

SF is associative: 8s1; s2; s3 2 S : SF(s1; SF(s2; s3)) = SF(SF(s1; s2); s3).

SF is idempotent: 8s 2 S : SF(s; s) = s.

Synopsis Diffusion-III

Energy Consumption

Time v. Current Draw During Query Processing

0

5

10

15

20

0 0.5 1 1.5 2 2.5 3Time (s)

Cu

rre

nt

(mA

) Snoozing

Processing

Processingand Listening

Transmitting

Declarative Queries for Sensor Networks

Examples:

SELECT Nodeid, lightFROM sensorsWHERE light > 400EPOCH DURATION 1s

Epoch Nodeid Light Temp Accel Sound

0 1 455 x x x

0 2 389 x x x

1 1 422 x x x

1 2 405 x x x

Sensors

• Time is partitioned into epochs of duration i A single aggregate value is produced to combine the readings of all devices during the epoch

1

Aggregation Queries

SELECT roomNo, AVG(sound)

FROM sensors

GROUP BY roomNo

HAVING AVG(sound) > 200

EPOCH DURATION 10s

Rooms w/ sound > 200

3

2 SELECT AVG(sound)

FROM sensors

EPOCH DURATION 10s

Epoch AVG(sound)

0 440

1 445

Epoch roomNo AVG(sound)

0 1 360

0 2 520

1 1 370

1 2 520

Topology Maintenance and Recovery

How to address the unreliable nature of WSNs in TAG?

Each node maintains a fixed size of its neighbors – Select a better parent nodeIf a node does not hear from its parent for some time, it assumes that its parent has failed

Secure Aggregation

• It is challenging to design suitable security mechanisms for Wireless Sensor Networks (WSNs)

― Stringent resource constraints on energy, processing power, memory, bandwidth, etc.

• WSNs need lightweight secure mechanisms

• We introduce an LCG-based secure aggregation scheme― Efficiency and simplicity

Secure Aggregation

Wireless Controller Area Network (CAN) BasedPrioritized MAC using Bit-wise arbitrationo Used in Bosch’s CAN 2.0o Extended to wireless channel

0 1 1 0

1 0 1 0

1 1 0 0

The highest priority packet gets transmitted first

Compute MAX by using data as the priority

Compute MIN by using complement of data as the priority

Excellent time-complexity for MAX/ MIN in single broadcast

domains

Limitations for other aggregates

Open Problems

Joint aggregation and scheduling problem - Spatio-temporal optimization

Multi-query optimization

Correlated source coding, compressed sensing

Conclusion

Data-aggregation leads to bandwidth and energy efficiency

Pure flooding is wasteful

Multicast tree with large node degrees is not optimal

THANK YOU!!