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DATA AGGREGATION IN WSNs

Sensors And Information Fusion 6933
      Instructor: Dr.Bill Buckles

                         ...
TOPICS
•   Introduction
•   Problem Definition
•   Clustering
•   Aggregation
•   Methodology
•   Experiments
•   Performa...
WIRELESS SENSOR NETWORKS
What?                                      Applications:
• Provide new paradigm for sensing and  ...
• Environmental monitoring( a group of temperature sensors)
• Similar or even identical readings, minimal difference
DATA AGGREGATION
• Data aggregation is the process of combining similar data from multiple sources to eliminate
  such red...
PACKAGE

• Hardware
    – MicaZ mote
          • 7.38 MHz Atmel processor with a 128 KB
            program memory,
      ...
PROBLEM DEFINITION
Problems:
• Energy consumption
     – Operating on small batteries : intangible cost to lose data due
 ...
CLUSTERING
• Grouping of sensors
    •   Distance or proximity
    •   Signal Strength
    •   Logical organizing
•   Topo...
HEED- Hybrid Energy Efficient Distributed clustering
• Assumptions:                         Algorithm:
• Sensor quasi-stat...
AGGREGATION
What?
• Process of combining similar data from multiple sources
    – Eliminate redundant transmission
    – P...
METHODOLOGY
Exp 1: Clustering and Aggregation iHEEDX
• Cluster the nodes according to HEED
• Nodes sense temp, light and ...
ENERGY CONSUMPTION
•   Transmission
     – Inter Cluster power level
           • Collection Tree all nodes use this power...
CREP System




•   The smallest energy can be expressed and well-represented as a multiple of 1 uJ.
•   The points in CRE...
EXPERIMENTAL SETUP
•   Nodes placed into three groups   Metrics Collected
     – Group 1: 2 and 5, B250        • Temp, Lig...
EXPERIMENTAL SETUP (cont’d)
EXPERIMENTAL SETUP (cont’d)

          Group1
EXPERIMENTAL SETUP (cont’d)




                  Group3
EXPERIMENTAL SETUP (cont’d)




                Group2
EXPERIMENTAL SETUP (cont’d)




             Base Station
PERFORMANCE ANALYSIS
•   Effect of Data aggregation on sensed data
•   Cluster size effect on Energy Consumption and Aggre...
EFFECT OF DATA AGGREGATION
                  21.5
                                                          No.2         •...
IMPACT OF CLUSTER SIZE
                                                                                         •
        ...
OVERHEAD INCURRED
                                                                                                 •
     ...
CONCLUSIONS
•   Joint advantages of clustering and data aggregation
•   Experiment in real testbed
•   Empirical results c...
Thank you! Questions?
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Sif Project

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Transcript of "Sif Project"

  1. 1. DATA AGGREGATION IN WSNs Sensors And Information Fusion 6933 Instructor: Dr.Bill Buckles Ning ‘Martin’ Xu Kalyan Pathapati Subbu Shijun Tang
  2. 2. TOPICS • Introduction • Problem Definition • Clustering • Aggregation • Methodology • Experiments • Performance Analysis & Results • Conclusions
  3. 3. WIRELESS SENSOR NETWORKS What? Applications: • Provide new paradigm for sensing and • Health applications disseminating information • Environmental and Structural • Collection of micro mechanical devices monitoring • Each device capable of wireless Design Aspects: communication and signal processing • Deployment, mobility Features • Topology, Density • small size • Robustness • Self configuration • large area coverage • Security • enhanced monitoring precision
  4. 4. • Environmental monitoring( a group of temperature sensors) • Similar or even identical readings, minimal difference
  5. 5. DATA AGGREGATION • Data aggregation is the process of combining similar data from multiple sources to eliminate such redundant transmission and provide fused information to the base station • Compression can be performed combining multiple data packets into one to reduce overhead of control information (as opposed to data)in the transmission • Results of arithmetic operations on the data set, such as the average, maximum and minimum, can be sent instead of the original data • Data In Feature Out form of fusion
  6. 6. PACKAGE • Hardware – MicaZ mote • 7.38 MHz Atmel processor with a 128 KB program memory, • 4 KB RAM and 512 KB non-volatile storage. • Chipcon SmartRF CC2420, with 2.4GHz frequency – MTS310 sensor board – Mib520 programming board • Software – TinyOS: OS for wireless sensor networks. – nesC: programming language for TinyOS.
  7. 7. PROBLEM DEFINITION Problems: • Energy consumption – Operating on small batteries : intangible cost to lose data due to battery depletion • Computational Costs • Storage constraints Solution for Energy Efficient operation: • Topology control – CLUSTERING • Efficient data collection – AGGREGATION
  8. 8. CLUSTERING • Grouping of sensors • Distance or proximity • Signal Strength • Logical organizing • Topology control approach • Load balancing, network scalability • Types of clustering • Static: local topology control • Dynamic: changing network parameters • Single hop and multi hop • Homogeneous and heterogeneous
  9. 9. HEED- Hybrid Energy Efficient Distributed clustering • Assumptions: Algorithm: • Sensor quasi-stationary • Cluster head selection • Links are symmetric • Factors: • Energy consumption non-uniform for • Primary- residual energy all nodes • Secondary-communication cost • Nodes-location unaware • Number of rounds of iterations • Processing and communication • Tentative CHs formed capability-similar • Final CH until CHprob=1 • Different power levels used for intra and inter-cluster communication
  10. 10. AGGREGATION What? • Process of combining similar data from multiple sources – Eliminate redundant transmission – Provide fused information to the base station How? • Sum, Average, Maximum and Minimum Scenario • Environmental monitoring: – Group of temperature sensors within the vicinity of one another – Moreover, readings from a single sensor – minimal difference during a certain period of time in the day. – Primary interest  reducing the redundancy coming from different sensor sources – Average might be sufficient for a small region
  11. 11. METHODOLOGY Exp 1: Clustering and Aggregation iHEEDX • Cluster the nodes according to HEED • Nodes sense temp, light and send to respective CHs • CH performs aggregation and sends to Base station Exp 2: No Clustering and No Aggregation Collection Tree • Individual nodes sense temp and light • All nodes directly send to Base station
  12. 12. ENERGY CONSUMPTION • Transmission – Inter Cluster power level • Collection Tree all nodes use this power level • iHEEDX : Only CHs use this power level – Intra Cluster power level • All non CH nodes use this power level • Aggregation – CH performs Averaging operation on the readings received – Energy calculated for number of instructions executed by processor • CREP : Credit point system
  13. 13. CREP System • The smallest energy can be expressed and well-represented as a multiple of 1 uJ. • The points in CREP are therefore assigned: The battery capacity is – Battery capacity 23,760,106 points – other components 70,380 points – transmission 860 points/packet – reception 90,000 points – radio idle state 171 points.
  14. 14. EXPERIMENTAL SETUP • Nodes placed into three groups Metrics Collected – Group 1: 2 and 5, B250 • Temp, Light – Group 2: 4 and 7, B245 • Overhead_Agg – Group 3: 1, 3 and 6, B251 • Overhead_NoAgg • Different places chosen • Packets_Recvd at BS – Variation in sensed values • Packets_Recvd_Org at Indv nodes • Packets_Count sent by Indv nodes
  15. 15. EXPERIMENTAL SETUP (cont’d)
  16. 16. EXPERIMENTAL SETUP (cont’d) Group1
  17. 17. EXPERIMENTAL SETUP (cont’d) Group3
  18. 18. EXPERIMENTAL SETUP (cont’d) Group2
  19. 19. EXPERIMENTAL SETUP (cont’d) Base Station
  20. 20. PERFORMANCE ANALYSIS • Effect of Data aggregation on sensed data • Cluster size effect on Energy Consumption and Aggregation • Overhead comparison for Aggregated and Non-Aggregated scenarios
  21. 21. EFFECT OF DATA AGGREGATION 21.5 No.2 • Readings of individual nodes No.5 Agg 2 and 5 • 21 Averaged readings from CHs Temperature (C) 20.5 • Similar data, reduced redundancy, ENERGY 20 SAVED! 19.5 0 20 40 60 80 100 Time (seconds)
  22. 22. IMPACT OF CLUSTER SIZE • 680 Cluster size ranging from 1 to 7 nodes • 660 Without Aggregation With Aggregation More the number aggregation Energy Consumption (points) 640 620 performed, ENERGY SAVED! 600 580 560 540 1 2 3 4 5 6 7 Cluster Size
  23. 23. OVERHEAD INCURRED • 5 x 10 5 Fewer transmissions, lesser transmission 4.5 With Aggregation 4 Without Aggregation power for intra cluster communication, ENERGY SAVED! Energy Consumption(points) 3.5 3 2.5 2 1.5 1 0.5 0 0 100 200 300 400 500 600 700 Number of packets transmitted
  24. 24. CONCLUSIONS • Joint advantages of clustering and data aggregation • Experiment in real testbed • Empirical results confirm energy conservation Hurdles: • Steep Learning curve - TinyOS • Hardware issues Future Work: • Data aggregation with no prior knowledge • Outdoor experimentation
  25. 25. Thank you! Questions?
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