This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters

1. SYNOPSIS PRESENTATION ON
PARAMETERS BASED DATA AGGREGATION FOR
STATISTICAL INFORMATION EXTRACTION IN
WIRELESS SENSOR NETWORKS
SUBMITTED BY:
JASLEEN KAUR
13MCS7007
M.E.(CN) HONS.
BATCH:2013-2015
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2. CONTENTS
• WIRELESS SENSOR NETWORK
• OBJECTIVES
• PROBLEM DEFINITION
• DATA-AGGREGATION
• METHODOLOGY USED
• EXPERIMENTAL METHODOLOGY
• CONCLUSION
• FUTURE SCOPE
• LITERATURE SURVEY
• REFRENCES
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3. WIRELESS SENSOR NETWORKS
• Consists of large number of nodes that are able to interact with
environment by sensing and controlling the physical
parameters.
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4. APPLICATIONS
• Environmental/Habitat monitoring
• Acoustic detection
• Seismic Detection
• Disaster relief
• Military surveillance
• Inventory tracking
• Medical monitoring
• Smart spaces
• Process Monitoring
• Gathering sensing information in inhospitable locations
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5. SENSOR NODE
Sensor node Video sensor node
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6. OBJECTIVES
• To capture the effects of aggregation over
different scales.
• To efficiently and accurately obtain the
sensory data from a sensor network.
• To reduce the energy consumption and thus
increase the lifetime of a network.
• Main focus is to extract the statistical
information from sensory data(which can
answer various queries) while keeping the
communication cost low
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7. PROBLEM DEFINITION
• In Wireless Sensor Networks, accurate data extraction and
aggregation is difficult due to
▫ Significant variations in sensor readings
▫ Frequent link and node failure
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8. DATA-AGGREGATION
• Aims to reduce power consumption inWSN.
• Aims to combine and summarize the data packets of several
nodes so that amount of data transmission is reduced.
• Data aggregation reduces the number of transmissions thereby
improving the bandwidth and energy utilization inWSN.
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9. METHODOLOGY USED
SITUATION
ANALYSIS
PROBLEM
DEFINITION
DATA
AGGREGATION
ALGORITHM
COMPARED
ALGORITHMS
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10. METHODOLOGY USED
• Literature survey of WSNs and In-network
aggregation techniques for WSNs.
• Data aggregation scheme
▫ Aggregation algorithm return probability
distribution of sensory data.
▫ Link between a pair of sensor nodes is lossy.
▫ Packets can be corrupted or dropped.
▫ Routing algorithm and Protocol.
▫ Multiple predecessors and successors.
▫ Theoretical approximation and select parameters then
we have,
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11. ▫ Sensor nodes only need to transmit packets that contain
distribution parameters instead of individual values
• Aggregation Process
SINK
REMOTE
NODE
Aggregated Node
Intermediate Node
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14. AGGREGATION PROCESS continues
• Packet sent by node v contain a 2-tuple (wv, Θ)
where wv is the weight of aggregate in this packet sent
by node v.
Θ is the set of all parameters of specific Gaussian
mixture model
p is packet loss rate due to node/link failure, wireless
interference.
PREDv = { u | v ε SUCCu } is the set of immediate
predecessor of v in routing graph.
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15. • For intermediate nodes, wv includes the sum of all weights
from predecessor and its own.
• At sink s, expression Σu ε PREDs wv + 1 represents total number
of readings.
• Can also calculate the COUNT query by this value at sink.
• Provide accurate and robust approximation.
• Estimating Distribution Parameters:
▫ Problem- Input information precisely defines two values,
its own and value received from its predecessor.
▫ Precisely defined input distribution is
v’ is the neighbor of v
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16. • αv‘ = wv’ / Σ wv’ + 1
• αo = 1 / Σ wv’ + 1
These are the normalised weights
• Estimation-Maximization (EM) Algorithm to solve the
problem for finding max likelihood estimates of parameters.
• Gaussian Estimation
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17. • Space(message size) and Time complexity
▫ More parameters we use more accurate the result is.
• Experimental Methodology
Communication cost
Histogram that results from
our algo and q-digest
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18. Performance comparison of the Algorithms in the presence of
link failures
Error rates
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19. METHODOLOGY OVERVIEW
1. Situation Analysis
2. Problem Definition
3. Taking Assumptions
4. Creating Network Model
5. Aggregation Scheme
6. Theoretical assumption in aggregation process
7. Estimating distribution parameters using EM algorithm
8. GMM Estimation
9. Compared Algorithms
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20. CONCLUSION
• Technique is scalable.
• Robust against link failures.
• The network communication cost is reduced.
• Preserves the accuracy of estimation.
• Avoid loss of valuable statistical information.
• It is the first study on using the “Mixture
Models”.
• There are several directions in future.
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21. FUTURE SCOPE
• To design a more efficient algorithm by using prediction to
exploit the temporal correlation of sensory data.
• To use geometry algorithms to facilitate information extraction
algortithm.
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22. WORK DONE
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34. LITERATURE SURVEY
• Survey of data-aggregation techniques using soft computing
▫ Types of Aggregation Techniques
Centralised Approach
In-network Aggregation
Size reduction
Without size reduction
Tree-based approach
Cluster-based approach
▫ Data aggregation using soft computing method
Fuzzy-based data aggregation
Swarm-based data aggregation
Neural-based data aggregation
GA-based data aggregation
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35. LITERATURE SURVEY continued..
• Literature Survey on wireless sensor networks
▫ Differences between ad-hoc and sensor networks
▫ Design factors like fault tolerance, scalability, topology, hardware
constraints, transmission media etc
▫ Physical Layer
▫ Data Link Layer
SMACS and EAR
CSMA-Based Medium Access
Hybrid TDMA/FDMA CSMA-Based Medium Access
▫ Network Layer
Flooding
Gossiping
SPIN
LEACH
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36. LITERATURE SURVEY continued..
▫ SAR
▫ Directed Diffusion
• Routing Protocols
▫ Rumor routing algorithm
▫ Energy-efficient communication protocol for wireless micro
sensors
• MAC layer
▫ Must be energy efficient
▫ Allows fair bandwidth allocation to all nodes
▫ CSMA strategy
• Energy concerns
▫ When node is in listening mode, energy expenditure is minimal
▫ Difficulty in defining when the network dies?
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37. LITERATURE SURVEY continued..
• Security
▫ Solution- Resurrecting Duckling security policy model
▫ Talking to strangers
• Open research issues
▫ Development of a new transport protocol
▫ Development of a new routing protocol
▫ Survey on routing protocols for sensor networks
▫ Interaction with other networks as well as cooperation among
sensor networks
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REFERENCES
• [1] Hongbo Jiang,Member,IEEE,Shudong Jin,Member,IEEE, and
Chonggang Wang, Senior Member,IEEE,“Parameter-Based Data
Aggregation for statistical Information Extraction in Wireless
Sensor Networks” in IEEE TRANSACTIONS ON VEHICULAR
TECHNOLOGY, VOL. 59, NO. 8,OCTOBER 2010.
• [2]http://www.academia.edu/890321/Wireless_Sensor_Networks_Is
sues_Challenges_and_Survey_of_Solutions
• [3] http://en.wikipedia.org/wiki/Secure_Data_Aggregation_in_WSN
• [4]Hevin Rajesh Dhasian, Paramasivan Balasubramanian “Survey
of data aggregation techniques using soft computing in wireless
sensor networks” in IET inf.Secur.,2013,Vol.7, Iss.4,pp.336-342
• [5]ELENA FASLO AND MICHELE ROSSI, DEI, UNIVERSITY
OF PADOVA;JORG WIDMER,DOCOMO EURO-LABS;
MICHELE ZORZI,DEI,UNIVERSITY OF PADOVA,“ In-network
aggregation techniques for wireless sensor networks:A survey” in
IEEE Wireless Communications April 2007

39. REFERENCES continued..
• “Literature survey on wireless sensor networks” on July
16,2003
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