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  • 1. DATA AGGREGATION IN WSNs Sensors And Information Fusion 6933 Instructor: Dr.Bill Buckles Ning ‘Martin’ Xu Kalyan Pathapati Subbu Shijun Tang
  • 2. TOPICS • Introduction • Problem Definition • Clustering • Aggregation • Methodology • Experiments • Performance Analysis & Results • Conclusions
  • 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. • Environmental monitoring( a group of temperature sensors) • Similar or even identical readings, minimal difference
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. EXPERIMENTAL SETUP (cont’d)
  • 16. EXPERIMENTAL SETUP (cont’d) Group1
  • 17. EXPERIMENTAL SETUP (cont’d) Group3
  • 18. EXPERIMENTAL SETUP (cont’d) Group2
  • 19. EXPERIMENTAL SETUP (cont’d) Base Station
  • 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. 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. 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. 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. 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. Thank you! Questions?