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Toward Optimal Data Aggregation in Random Wireless Sensor Networks

David Trigos Professor : Waltenegus Dargie Lecture:Wireless Sensor Networks ( WS 2007-08 ) ‏. Toward Optimal Data Aggregation in Random Wireless Sensor Networks. Contents. Background Motivation Previous Work Description Results References. Background. Issues in WSN Routing Protocols

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Toward Optimal Data Aggregation in Random Wireless Sensor Networks

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  1. David Trigos Professor : Waltenegus Dargie Lecture:Wireless Sensor Networks ( WS 2007-08 )‏ Toward Optimal Data Aggregation in RandomWireless Sensor Networks

  2. Wireless-Sensor-Networks: WS: 07-08 Contents Background Motivation Previous Work Description Results References

  3. Wireless-Sensor-Networks: WS: 07-08 Background Issues in WSN Routing Protocols Energy efficiency during data dissemination, processing and relay. Focus on data-centric vs. node-centric. Use data aggregation to eliminate redundancy. Solution: Use a novel Protocol to solve issues. + Other Routing Protocol Examples: Multihop- Better than direct transmission. SPIN- data centric,saves energy through data negotiation and meta-data instead of redundant data. (Problem with meta-data flooding.)‏ TEEN-sensor nodes sense the medium continuously, and save energy on data transmission. Depending on a threshold. (But if threshold are not received, no data is sent). NOTE : Classification of Net Layer Protocols

  4. Wireless-Sensor-Networks: WS: 07-08 Data Aggregation Issues Data Aggregation Techniques Nodes are aware of each other, communicate to form a path to the sink. Shifts focus from traditional address-centric approaches. Focus on many-to-one information flows. Performs some function to relay only relevant information. • Examples: • Duplicate Suppression- Nodes ignore duplicate data. • Directed Diffusion-combine data from sources enroute by eliminating redundancy. Interests, Gradient. • Data Aggregation Problem: • “Maximize the achievable data rate possible from a group of nodes to a sink under power constraint”[1] • Investigate optimal limit and a technique to achieve it.

  5. Wireless-Sensor-Networks: WS: 07-08 Cooperative Processing • “ Signal processing technique to reduce energy cost by nodes working together…” • Multiple nodes in the network cooperate to transmit information to the sink. • Uses Space-Time Signal Processing. • Needs very strong synchronization. • Rely on timing and position information. • Used to find an optimal solution to the data aggregation problem. • “Find the maximum data rate of transfer from a node to a sink under power constraint [under random topology]…”

  6. Wireless-Sensor-Networks: WS: 07-08 Cooperative Time Reversal “A routing and scheduling protocol devised to solve the data aggregation problem...” • Generalization of cooperative beam-forming • Enhance the source signal. • Uses relative time delays of arrival of a signal to each node. • Each node calculates DOA. (Needs Strong synchronization) • Sends signal to sink is in time-reverse fashion. • All Nodes cooperate to send the signal from one node to the sink. • All signals arrive at the sink at the same time and their energy will be summed. The Optimal rate of O(log (n)/n) is achievable using cooperative TRC.

  7. Wireless-Sensor-Networks: WS: 07-08 TRAINING PHASE  t   t  t

  8. Wireless-Sensor-Networks: WS: 07-08  t     t  t Cooperation Phase

  9. Wireless-Sensor-Networks: WS: 07-08 Cooperative Time Reversal Principle of Superposition. Summation of energy of signal sent by all nodes detected at the sink: (Sum of autocorrelation functions)‏

  10. Wireless-Sensor-Networks: WS: 07-08 Objective: Obtain Optimal Data Aggregation Rate Contributions of this paper: • Consider Path Loss Exponent α to calc. rates • Consider Power Constraint of individual nodes in Mathematical Proofs (Pmax)‏ • Consider an extended random network. (Poisson distribution, Voronoi Tesselation)‏ STEPS for Analysis Obtain Poisson Distribution for nodes (random). Obtain optimal Voronoi Tesselation for Physical Configuration. Obtain Rates using using formulas Obtain Upper Bound and Compare with: 1.-Multihop 2.- TRC Derive Optimal rates for each case: 1.- 2<α <4: 2.- α >4:

  11. Wireless-Sensor-Networks: WS: 07-08 Derive Physical Configuration To conduct the proofs, we make several assumptions about the physical layer: Create a Poisson process to place the nodes randomly in the field The Voronoi Field is derived so as to know the maximum number of nodes in each area of the network and consider the network an uniform transmitting field. The plane is partitioned into polygons, where each partition consists of all the points in the plane closer to one particular point than any other point… (Objective: Treat 2D surface as an uniform plane and signal field)‏

  12. Wireless-Sensor-Networks: WS: 07-08 Obtain different Rates Equation to model TRC: Equation to Model Multi-Hop Communication: Numerator: Coherent aggregation of peak signal at receiver. Sum in denominator: Aggregated interference Ai=√(Pi)= Average Amplitude of signal (summed) ρ- distance from node i to j T=set of Cooperative Nodes Result: O(log(n)/n)‏ • B= Bandwidth all nodes in the net. • P= Transmitting Power of each node. Sum in denominator: Aggregated interference • Result: r= O(1)

  13. Wireless-Sensor-Networks: WS: 07-08 CTR and Multi-Hop Compare upper bound: Depending on distance to sink Data Aggregation Rate is optimal with power loss exponent: 2<α<4 Use CTR otherwise use MultiHop To achieve optimal rates, we divide the network into Areas. [ α < 2 ]: Area I The distance to the sink is very close. It does not benefit from TRC [ 2 < α < 4 ] Area II: Use TRC for optimal data aggregation. [ α > 4 ] Area III The distance is too great to use TRC.

  14. Wireless-Sensor-Networks: WS: 07-08 Results Use MultiHop for [ α < 2 ] or [ α > 4 ] : 1is a tight bound with constant rate -bounded ONLY by capacity of sender closest to sink. For CTR[ 2 < α < 4 ] : logn/n Derived from previous equation. Why? 1)Because of the distance if power loss exponent >4, the CRT d.a.r equation degenerates into the multihop case. #of nodes

  15. Wireless-Sensor-Networks: WS: 07-08 References [1] “Toward Optimal Data Aggregation in Random Wireless Sensor Networks” Richard J. Barton, Rong Zheng Dept. of Electrical and Computer Eng. Dept. of Computer Science University of Houston Houston, TX 77205, USA [2] “Routing Techniques in Wireless Sensor Networks: A Survey” Jamal Al-Karaki, Ahmed E. Kamal Dept. of Electrical and Computer Engineering Iowa State University, Ames, Iowa 50011 [3] “Order-Optimal Data Aggregation in Wireless Sensor Networks.” Richard J. Barton, Member IEEE, and Rong Zheng, Member IEEE. [4] "The capacity of wireless networks" P. Gupta and P.R. Kumar. Capacity of wireless networks. Technical report, University of Illinois, Urbana-Champaign, 1999.

  16. Wireless-Sensor-Networks: WS: 07-08 Thank You Questions?

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