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1.The Impact Of Data Aggregation in Wireless

Sensor Networks.2.The ACQUIRE Mechanism for

Efficient Querying In Sensor Networks.

- By
- Kinnary Jangla
- Rishi Kant Sharda

Date 04-19-06

The Impact of Data Aggregation in Wireless Sensor

Networks

- Paper By
- - Bhaskar Krishnamachari
- - Deborah Estrin
- - Stephen Wicker
- Presented By
- - Kinnary Jangla
- - Rishi Kant Sharda

Basic Idea..

- To exploit the data redundancy
- Packets from different nodes, are combined in

network. - Implementation
- Who carries the data with redundancy
- Data-centric routing
- Differences
- Data-centric routing
- Based on contents of the packets.
- Address-centric routing
- Routing based on an end-to-end manner.

The Impact Of Data Aggregation On Wireless Sensor

Networks

Overview

- Sensor Network Models
- Event-Radius Model
- Random Source Models
- Impact of
- Source-Destination Placements
- Communication Network Density
- On
- - Energy Costs
- - Delay

(Cont..)

The

Impact Of Data Aggregation On Wireless Sensor

Networks

- Data Centric routing - Significant

Performance Gain - Complexity of Data Aggregation
- NP-Hard Problem.

Sub - Titles

The Impact Of Data Aggregation On

Wireless Sensor Networks

- Introduction.
- Routing Models.
- AC
- DC
- Data-Aggregation
- Optimal Suboptimal Aggregation
- Sensor Network Models
- Energy Savings
- Theoretical Results
- Simulation Results
- Delay

Introduction.

?

??

?

?

- Concepts.
- Sensor Network ?
- Sensor Node ?
- Unattended Operation ?
- Data Aggregation ?
- Data Redundancy !
- Wireless Sensor Network.
- Applications.
- Network Topology of a Sensor Network.

Introduction cont..

- Network Topology of a Wireless Sensor Network.

(cont..)

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Data Aggregation in WSN ?
- - Address-centric approach
- - Data-centric approach

Routing Models

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Address Centric Approach

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Data Centric Approach

Data Aggregation

The Impact Of Data Aggregation On

Wireless Sensor Networks

- Result 1
- - The optimum number of transmissions required

per datum for the DC protocol is equal to the

number of edges in the minimum steiner tree in

the network which contains the node set (s1, . ,

Sk, D). - - Hence, assuming an arbitrary placement of

sources and a general network graph G, the task

of doing DC routing with optimal data aggregation

is NP-Hard. - - Steiner Tree?
- - NP-Hard Problem?

Optimal Data Aggregation

- The optimal data aggregation problem is NP-Hard.
- An optimal multicast problem
- A well-known problem
- A minimum Steiner tree problem NPC
- SoNO optimal Solution
- Thus, sub-optimal solutions.

Data Aggregation

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Section 1
- 3 suboptimal Schemes
- Center at Nearest Source
- Aggregation center nearest node to the sink.
- Shortest Paths Tree
- Shortest path routing with data aggregation in

the overlap nodes. - Greedy Incremental Tree
- Node closest to the tree connects to the path and

forms a new tree until all the source nodes are

vertices.

(cont..)

The Impact Of Data Aggregation On

Wireless Sensor Networks

- Section 2
- Sensor Network Models- for source placement.
- Factors affecting the performance gains of

sensor network.. - Position of the sources
- communication network topology.
- Event Radius Model.
- Random Sources Model.

The Impact Of Data Aggregation On

Wireless Sensor Networks

- Event Radius Model.
- Location of an event.
- Sensing Range, S.
- (Pi)S2n average number of sources.

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Random Sources Model.
- Sources not clustered.
- K random nodes, that are not sinks,are chosen to

be sources

Energy Savings due to data aggregation

- Notations
- di the distance of the shortest path from

source i to - the sink
- NA the total number of transmissions required

for the optimal - address-centric protocol
- ND the total number of transmissions required

for the optimal - data-centric protocol
- X the diameter of the graph formed by a set of

connected nodes - K the number of the sources in the RS model
- R communication range
- S sensing range in the ER model

Energy Savings Due to Data Aggregation

The Impact Of Data Aggregation On

Wireless Sensor Networks

- Main performance gain ? When sources are far away

from the sink. - NA d1 d2 . Dk sum (di)
- Diameter X max of pairwise shortest paths.
- Theoretical Results
- Result 2
- If the source nodes S1, S2, , Sk have a

diameter X gt 1. The total number of

transmissions (Nd) required for the optimal DC

protocol satisfies the following bounds - ND lt (k-1)X min(di) X gt 1
- ND gt min(di) (k-1) X 1
- Corollary If diameter X lt min(di), then ND lt NA.

- Proof
- data aggregation tree consists of
- (k - 1) sources sending their packets to the

remaining - source which is nearest to the sink.
- This tree has no more than (k-1)X min(di)

edges, - Next result is obtained by considering the

smallest - possible Steiner tree which would happen if the
- diameter were 1.
- The shortest path from the source node at

min(di) must be part of - the minimum Steiner tree, and there is exactly
- one edge from each of the other source nodes to
- this node.
- Conclusion The optimum data-centric protocol

will perform strictly - better than the Address-centric

protocol.

Cont

- Result 3

ND/NA 1/k

- DC Protocol gives k-fold savings.

Cont

The Impact Of Data Aggregation

On Wireless Sensor Networks

- Result 4
- If the subgraph G of the communication graph G

induced by the set of source nodes (S1Sk) is

connected, the optimal data aggregation tree can

be formed in polynomial time. - Corollary
- In the ER model, when R gt 2S, the optimal data

aggregation tree can be formed in polynomial

time.

- Proof
- The tree is initialized with the path from the

sink to the nearest source. - At each additional step of the GIT, the next

source to be connected - to the tree is always exactly one step away

(such a source is guaranteed to exist since G is

connected). - At the end of the construction, the number of

edges in the tree is therefore - dmin (k - 1).
- Therefore, the GIT construction runs in

polynomial time w.r.t. the number of - nodes .

Summary

- Result 1
- The number of transmissions for the DC protocol

number of edges in the minimum Steiner tree. - Result 2
- Nd lt (k-1)X min(di)
- Nd gt (k-1) min(di)
- Result 3
- Result 4
- The optimal data aggregation tree can be formed

in polynomial time.

ND/NA 1/k

Simulation Results

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Figure 1
- - Comparison of Energy costs versus R in the

ER model. - Figure 2
- - Comparison of energy costs versus R in the RS

model

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Figure 3
- Comparison of energy costs versus S in the ER

model - Figure 4
- - Comparison of energy costs versus k in the

RS model.

Sensing Range

Energy Savings.

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Summary of experiments
- Energy Savings due to data aggregation can be

quite significant, particularly when there are a

lot of sources (large S or large k) that are

many hops from the sink - (small R).

Delay due to Data Aggregation

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Tradeoff
- Greater Delay !!
- Data from sources have to be held back at an

intermediate node in order to be aggregated. - Worst Case- Latency due to aggregation will be

proportional to the number of hops between sink

and the farthest source.

The Impact Of Data Aggregation On Wireless

Sensor Networks

- Figure 5
- Max(di) and Min(di) versus R in the ER Model
- Figure 6
- Max(di) and Min(di) versus S in the ER Model.

Conclusions

The Impact Of Data Aggregation On Wireless

Sensor Networks

- The formation of an optimal data aggregation tree

is NP Hard. - Energy Gains possible with data aggregation.
- Large when
- - number of sources large
- - Sources located close to each. Other

and far from sink - Aggregation Latency (Delay) non-negligible

Thank You.

The ACQUIRE Mechanism for Efficient Querying in

Sensor Networks

- Written By
- Narayanan Sadagopan
- Bhaskar Krishnamachari
- Ahmed Helmy
- Presented By
- Rishi Kant Sharda
- Kinnary Jangla

The Basics

- A sensor network is a computer network of many,

spatially distributed devices using sensors to

monitor conditions at different locations, such

as temperature, sound, vibration, pressure,

motion or pollutants. - Each device is equipped with a radio transceiver,

a small microcontroller, and an energy source,

usually a battery. The devices use each other to

transport data to a monitoring computer. - Usually these devices are small and inexpensive,

so that they can be produced and deployed in

large numbers, and so their resources in terms of

energy, memory, computational speed and bandwidth

are severely constrained. - Therefore not feasible to collect all

measurements from each device for centralized

processing.

Introduction

- Best to view them as distributed databases.
- Central querier/data sink issues queries.
- Due to energy constraints it is desirable for

much of the data processing to be done

in-network. - This leads to the concept of data centric

information routing i.e. queries and responses

are for named data.

Categories of Queries

- Continuous Queries
- e.g Report the measured temperature for the next

7 days with a frequency of 1 measurement per

hour. - One-Shot Queries
- e.g Is the current temperature higher than 70?
- Aggregate Queries
- e.g Report the calculated average temperature of

all nodes in region X. - Non-Aggregate Queries
- e.g What is the temperature measured by node x?
- Complex Queries
- e.g What are the values of the following

variables X, Y , Z? - Simple Queries
- e.g What is the value of the variable X?
- Queries for Replicated data
- e.g Has a target been observed anywhere in the

area? - Queries for Unique data

Flooding-based query mechanisms (Directed

Diffusion data-centric routing scheme)

Expanding Ring Search

Why ACQUIRE?

- Earlier Flooding-based query methods such as

Directed Diffusion data-centric routing scheme

are well suited only for continuous-aggregate

queries. - One-size-fits-all approach unlikely to provide

efficient solutions for other types. - If it is not continuous then flooding can

dominate the costs associated with querying. - Similarly in data aggregation duplicate responses

can lead to suboptimal data collection in terms

of energy costs.

Example Bird Habitat Monitoring

Example Continued

- Task Obtain sample calls for the following

birds in the reserve Blue jay, Nightingale,

Cardinal, Warbler - Complex
- One-shot
- For replicated data

ACQUIRE

LEGEND Active Query Complete Response Update

Messages Sensor

Analysis of ACQUIRE

- Basic Model and Notation
- Local update
- Forward
- Steps to Query Completion
- Local Update Cost
- Total Energy Cost
- Optimal Look Ahead

Basic Model and Notation

- X number of sensors.
- V V1,V2,VN are the N variables tracked.
- Q Q1,Q2,QM consisting of M sub-queries, 1 lt

M N and for all i i lt M, Qi ? V. - Let SM be the average number of steps taken to

resolve a query consisting of M sub-queries. - d Look ahead parameter
- Size of a sensors neighborhood f(d)
- Assumed that all queries Q are resolvable by this

network. - x be the querier which issues the query Q.

ACQUIRE Process

- Local Update
- If current information not up-to-date, x sends

request to all sensors d hops away. - Request forwarded hop-by-hop.
- Sensors who get the request then forward their

information to x. - Let the energy consumed in this phase be Eupdate
- Forward
- After answering the query based on information

received. - x forwards the remaining query to a randomly

chosen node d hops away.

ACQUIRE Process 2

- Since updates are triggered only when the

information is not fresh, it makes sense to try

and quantify how often such updates will be

triggered. - We model this as amortization factor c.
- An update is likely to occur at any given node

only once every c queries. - c such that 0 lt c 1. e.g if on average an

update has to be done once every 100 queries, c

0.01. - a denotes the expected number of hops from the

node where the query is completely resolved to x

ACQUIRE Process 3

- The average energy consumed to answer the query

of size M with look-ahead d can be expressed as - Case dD , where D is the diameter of the

network. - Case d too small.
- SM ? when d ?
- Eupdate ? when d ?

Steps to Query Completion

- If there are M queries to be resolved the

probability of success in each trial is p M/N

and failure is p (N-M)/N. - Expected number of trials till 1st success

1/pN/M. - The whole experiment can be repeated with one

less query and time to answer another query is

N/(M-1) and so on. - Let sM be the number of trials till M successes

i.e complete resolution. Then

Steps to Query Completion 2

- H(M) is the sum of the first M terms of the

harmonic series. - H(M) ln(M) ?, where ? 0.57721 Eulers

constant, thus - and

Local Update Cost

- Eupdate Energy spent in updating the

information at each active node. - The number of transmissions needed to forward

this request is the no. of nodes within d-1 hops,

f(d-1). - N(i) Number of nodes at hop i.

Total Energy Cost

- If the response is returned along the reverse

path i.e a lt dSM - Special case d 0 Random Walk.
- E(sM) steps to resolve and return the query.

Optimal Look-ahead

- Ignoring boundary effects, it can be shown that

N(i) 4i and - f(d) (2d(d1))1 for a grid of sensors, each

node having 4 immediate neighbors. - Combining expression for SM, Eupdate, Eavg , N(i)

and f(d) we get

Optimal Look-ahead 2

- We determine the value of the look-ahead

parameter which minimizes this energy cost by

taking the derivative with respect to d and set

it equal to 0, we get d by - In general the lower c is, higher will be the

look ahead parameter d

Optimal Look-ahead 4

Optimal Look-ahead 5

COMPARISON

Conclusions

- Proposed ACQUIRE as a scalable protocol for

complex, one-shot queries for replicated data in

sensor networks. - Developed an analytical comparison of ACQUIRE,

FBQ and ERS. - With optimal parameter settings ACQUIRE

outperforms all other schemes for complex,

one-shot queries. - Optimal ACQUIRE performs many orders of magnitude

better than flooding-based schemes. - Can reduce energy consumption by more than 60.

Future Work

- The efficiency of ACQUIRE can also be improved if

the neighborhoods of the successive active nodes

in the query trajectory have minimal overlap. - Guided trajectories may also be helpful in

dealing with non-uniform data distributions - Taking into account that receptions can also

influence energy consumption. This is the case

especially for broadcast messages.

THANK YOU

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