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
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

The Impact Of Data Aggregation On Wireless Sensor Networks
(Cont..)

Data Centric routing - Significant
Performance Gain

Complexity of Data Aggregation
 NP-Hard Problem.
The Impact Of Data Aggregation On Wireless Sensor Networks
Sub - Titles:





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.
The Impact Of Data Aggregation On Wireless Sensor Networks
(cont..)

Data Aggregation in WSN ?
- Address-centric approach
- Data-centric approach
The Impact Of Data Aggregation On Wireless Sensor Networks
Routing Models

Address Centric
Approach
The Impact Of Data Aggregation On Wireless Sensor Networks

Data – Centric
Approach
The Impact Of Data Aggregation On Wireless Sensor Networks
Data Aggregation

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

So…NO optimal Solution
 Thus, sub-optimal solutions.
The Impact Of Data Aggregation On Wireless Sensor Networks
Data Aggregation
Section 1:

3 – suboptimal Schemes:
 Center at Nearest Source:


Shortest Paths Tree


Aggregation center: nearest node to the sink.
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.
The Impact Of Data Aggregation On Wireless Sensor Networks
(cont..)
Section 2:


Sensor Network Models:- for source placement.
1.
{ Factors affecting the performance gains of sensor network..
Position of the sources
communication network topology.}
2.


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)*S^2*n –
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:



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
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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
The Impact Of Data Aggregation On Wireless Sensor Networks
Energy Savings Due to Data Aggregation

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 >=
1. The total number of transmissions (Nd) required for the optimal
DC protocol satisfies the following bounds:



ND < = (k-1)X + min(di) ……
ND >= min(di) + (k-1)
……
X >= 1
X=1
Corollary If diameter X < min(di), then ND < 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.
The Impact Of Data Aggregation On Wireless Sensor Networks
Cont…

Result 4:


If the subgraph G” of the communication graph G induced by the
set of source nodes (S1……Sk) is connected, the optimal data
aggregation tree can be formed in polynomial time.
Corollary:

In the ER model, when R > 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 <= (k-1)X + min(di)

Nd >= (k-1) + min(di)

Result 3:
ND/NA = 1/k

Result 4:

The optimal data aggregation tree can be formed in
polynomial time.
The Impact Of Data Aggregation On Wireless Sensor Networks
Simulation Results:

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
Sensing Range

Figure 4:
- Comparison of
energy costs versus k
in the RS model.
The Impact Of Data Aggregation On Wireless Sensor Networks
Energy Savings.

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).
The Impact Of Data Aggregation On Wireless Sensor Networks
Delay due to Data Aggregation

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.
The Impact Of Data Aggregation On Wireless Sensor Networks
Conclusions:

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
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 innetwork.
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


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
X number of sensors.
V = {V1,V2,…VN} are the N variables tracked.
Q = {Q1,Q2,…QM} consisting of M sub-queries, 1 < M ≤
N and for all i : i < M, Qi Є 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 < c ≤ 1. e.g if on average an update has
to be done once every 100 queries, c = 0.01.
α 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: d=D , 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/p=N/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 σM 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) + γ, where γ = 0.57721
Euler’s 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 α <= dSM

Special case: d = 0 –Random Walk.
E(σM) 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(d+1))+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
Average Energy per Query
4000
c=0.06
3500
3000
c=0.05
c=0.07
c=0.04
2500
2000
c=0.03
1500
c=0.02
1000
c=0.01
500
0
1
3
5
7
9
11
13
15
17
19
21
23
Look-ahead Parameter (d) [N=1000, M=200]
25
27
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, oneshot 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