data aggregation siddhartha sarkar roll no: 13000111128 cse-4 th year-7 th semester sensor networks...
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DATA AGGREGATIONSiddhartha SarkarRoll no: 13000111128CSE-4th Year-7th semester
Sensor Networks (CS 704D) Assignment
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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
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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
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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.
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What is Aggregation?
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C D
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HA
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Base Station
JK LM
NWireless Sensor Node
Data Transmission
Legend
v1 v2
v3
vi Sensor Measurement
f(v1, v2, v3)
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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
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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
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Message count if aggregation is used : NMessage count if aggregation is not used : Nlogb N
Why Data Aggregation in WSN? - II
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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.
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There are several aggregation techniques followed in Wireless Sensor Network , Such as
Tree based Aggregation
In network Aggregation
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Data Aggregation Process
Sensor nodes are organized into a tree hierarchy rooted at the Base Station
Non-leaf nodes act as the aggregators
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Tiny Aggregation
Distribution phaseAggregate queries are pushed down into the network
Collection phaseAggregate values are continuously routed up from children to parents
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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)
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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
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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
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A classification of aggregates:
Tiny Aggregation (TAG)-III
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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.
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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
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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
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ODI Correctness Test:
A synopsis diffusion algorithm is ODI-correct if SF and SG are order and duplicate-insensitive functions.
Synopsis Diffusion-III
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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
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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
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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
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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
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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
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Secure Aggregation
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• 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
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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
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Open Problems
Joint aggregation and scheduling problem - Spatio-temporal optimization
Multi-query optimization
Correlated source coding, compressed sensing
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Conclusion
Data-aggregation leads to bandwidth and energy efficiency
Pure flooding is wasteful
Multicast tree with large node degrees is not optimal
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THANK YOU!!