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!!
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