Ad Hoc Now2008 Probabilistic Query Dissemination

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Talk presented at Ad Hoc Now 2008 in Nice.

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Ad Hoc Now2008 Probabilistic Query Dissemination

  1. 1. Zinaida Benenson Felix Freiling Markus Bestehorn Marek Jawurek Query Dissemination with Predictable Erik Buchmann Query Dissemination with Predictable Reachability and Energy Usage in Sensor Networks AdHoc-Now 2008, Sophia Antipolis www.kit.edu
  2. 2. Introduction – Sensor Networks A sensor network consists multiple of sensor nodes, e.g.Introduction Problem Desc. IdIdea Reachability Direct I di t MicaZ Sun SPOT Indirect Topology Information Sensor Nodes Battery-powered Equipped with sensor hardware Evaluation Setup Simulation Break Even Equipped with sensor hardware Limited computing resources Wireless communication Break-Even Deployment Conclusion Q&A Slide 2Markus Bestehorn
  3. 3. Query Processing in WSN Generic query processing in sensor networks approach: 1. Disseminate query through base station Introduction Problem Desc. Id q y g SELECT MAX(temp) FROM sensors … 2. Measure data using sensing hardware 3 Process & route query results back to base station Idea Reachability Direct I di t 3. Process & route query results back to base station Optimization Goal: Reduce energy consumption! Indirect Topology Information Sending/Receiving data most expensive! 2 15°C 17°C Evaluation Setup Simulation Break Even 2 3 4 6 17°C 21°C QQQ Q QQ Break-Even Deployment Conclusion 3 1 5 Basestation 20°C 19°C 22°C QQQ Q 22°CQ&A Slide 3Markus Bestehorn
  4. 4. Challenges for Query Dissemination Unnecessary rebroadcasts must be avoided Nodes should receive query only once Introduction Problem Desc. Id q y y Idea Reachability Direct I di t 2 4 6 Q Q ? Existing approaches Indirect Topology Information 31 5Q Q Existing approaches Topology-Based: Determine rebroadcasting nodes using accurate local topology information Evaluation Setup Simulation Break Even 2-Hop topology information is very costly Optimal Broadcast Dominating Set Problem NP-complete Probabilistic: Nodes rebroadcast with probability p Break-Even Deployment Conclusion High p high energy consumption Low p not all nodes reached How to set p? Q&A Slide 4Markus Bestehorn
  5. 5. Idea & Agenda General idea: Acquire basic topology information Introduction Problem Desc. Id q p gy does not consume as much energy Use probabilistic approach to disseminate query Set rebroadcast probability based on basic topology information Idea Reachability Direct I di t Set rebroadcast probability based on basic topology information Agenda: Indirect Topology Information Prediction framework How to predict reachability for a given rebroadcast probability p? How to set p based on prediction to reach all nodes? Evaluation Setup Simulation Break Even How to set p based on prediction to reach all nodes? Topology Discovery Possibilities to aquire required topology information? Extensive evaluation Break-Even Deployment Conclusion Extensive evaluation Simulation and real deployment results Explore tradeoff reachability vs. energy consumption A f th P di ti F k? Q&A Slide 5Markus Bestehorn Accuracy of the Prediction Framework?
  6. 6. Hop Set Modell (1) Task: Predict the number of reached nodes given Topology information Introduction Problem Desc. Id p gy Rebroadcast probability p Hop Set: Hop Set H[i] contains all nodes that can be reached by the base station via i hops Idea Reachability Direct I di t reached by the base station via i hopsIndirect Topology Information H[1]H[2]H[3] H[0] Evaluation Setup Simulation Break Even 2 4 6 Break-Even Deployment Conclusion 31 5 Q&A Slide 6Markus Bestehorn
  7. 7. Hop Set Modell (2) Possibilities to reach a node via broadcast Direct: Message is sent from node in H[i-1] to node in H[i] Introduction Problem Desc. Id g [ ] [ ] Indirect: Message is sent from node in H[i] to node in H[i] Backwards: Node in H[j] with j > i forwards message to node in H[i] Simplification: not considered Idea Reachability Direct I di t H[i] Simplification: not consideredIndirect Topology Information Evaluation Setup Simulation Break Even 4 H[1]H[2]H[3] H[0] QQQBreak-Even Deployment Conclusion 2 4 6 Q Q Q Q Q Q&A 31 5 Slide 7Markus Bestehorn
  8. 8. Reachability Prediction R(h,p) := number of reached nodes in Hop Set h with rebroadcast probability p Introduction Problem Desc. Id p y p R(0,p) = 1 base station is always „reached“ R(1,p) = |H[1]| base station always broadcasts H[1] Idea Reachability Direct I di t base station always broadcasts Hop Set H[1] always reached Nodes in s bseq ent Hop Sets are reached H[1]Indirect Topology Information Nodes in subsequent Hop Sets are reached Directly Direct(h,p) Example: Direct(2,p)=4 Indirectly Indirect(h p) H[1] Evaluation Setup Simulation Break Even Indirectly Indirect(h,p) Example: Indirect(2,p)=2 H[1] H[2] Break-Even Deployment Conclusion R(h,p) := Direct(h,p) + Indirect(h,p) with h > 1Q&A Slide 8Markus Bestehorn
  9. 9. Direct Reachability Prediction Basic Idea to compute Direct(h,p) Possible rebroadcasters |H[h-1]| nodes H[i]H[i-1]Introduction Problem Desc. Id | [ ]| Potential Rebroadcasters R(h-1,p) nodes Rebroadcasters R(h-1,p)·p nodes |H[h-1]| R(h 1 ) Idea Reachability Direct I di t |H[h 1]| R(h-1,p) R(h-1,p) ·p Indirect Topology Information P(„Node in H[h] directly reached“) can be computed Evaluation Setup Simulation Break Even Avg. Number of connections from H[i] to H[i-1] Connectivity[h] Detailed description in the paper Break-Even Deployment Conclusion p p p Direct(h,p) = P(reached directly)·s[h] p Rebroadcast Probability H[i] Nodes reached in i Hops Q&A Slide 9Markus Bestehorn p
  10. 10. Indirect Rechability Prediction Idea to compute Indirect(h,p): Potential Rebroadcasters Direct(h,p) Introduction Problem Desc. Id ( ,p) Rebroadcasters Direct(h,p)·p Average Number of connections within a Hop set Interconnectivity[h] Idea Reachability Direct I di t Interconnectivity[h] Indirect(h,p)=Direct(h,p)·p·Interconnectivity[h] Indirect Topology Information H[1]H[2] H[0] Evaluation Setup Simulation Break Even 4 6 Implicit Assumption: Break-Even Deployment Conclusion 3 5 Implicit Assumption: Reached nodes distributed evenly within hop sets Q&A p Rebroadcast Probability H[i] Nodes reached in i Hops Slide 10Markus Bestehorn p
  11. 11. Reachability Prediction (3) R(h,p) computes reached nodes in Hop Set h with rebroadcast probability p Introduction Problem Desc. Id p y p Computing total reachability for given p: ( )( )][min)( hHphRpR ∑= Idea Reachability Direct I di t Minimum required because Direct(h p) + Indirect(h p) > H[h] ( )( )][,,min)( hHphRpR h ∑=Indirect Topology Information Minimum required because Direct(h,p) + Indirect(h,p) > H[h] possibleEvaluation Setup Simulation Break Even Also available: Number of sent messages / rebroadcasting nodes Break-Even Deployment Conclusion Number of sent messages / rebroadcasting nodes Number of received messages Allows estimation of energy consumption! Q&A Slide 11Markus Bestehorn
  12. 12. Topology Information Required Topology Information for Reachability Prediction Set Size: Number of Nodes in each Hop Set H[h] Introduction Problem Desc. Id p [ ] Connectivity: Avg. Number of connections a node in H[h] has to nodes in H[h-1] Interconnectivity: Avg Number of connections a node in H[h] Idea Reachability Direct I di t Interconnectivity: Avg. Number of connections a node in H[h] has to other nodes in H[h] Example: Indirect Topology Information H[i]H[i-1] Set size Connectivity Evaluation Setup Simulation Break Even … i-1 i … … 2 3 … … i-1 i … … 1.5 2 … I t ti it Break-Even Deployment Conclusion Interconnectivity … i-1 i … … 0 4/3 … Q&A Slide 12Markus Bestehorn … 0 4/3 …
  13. 13. Acquiring Topology Information Several options to get required topology information: Echo Algorithm Introduction Problem Desc. Id g Expansion Wave: Explore network by initiating a flooding at the base station Contraction Wave: Aggregate topology information towards base Idea Reachability Direct I di t gg g p gy station Drawback: Energy consumption, Scalability Gossiping: Nodes attach routing information to messages Indirect Topology Information Gossiping: Nodes attach routing information to messages Advantage: No extra messages Drawback: Routing information disperses slowly Routing Protocol Extraction: Extract topology information Evaluation Setup Simulation Break Even Routing Protocol Extraction: Extract topology information from data structures of routing protocol Drawback: Only possible for some protocols (AODV) N t Break-Even Deployment Conclusion Note: Even for Echo Algorithm Prediction pays off after a few query disseminations! Q&A Slide 13Markus Bestehorn q y
  14. 14. Evaluation - Setup Network: 125 to 425 nodes Node Degree: 4 – 16 Introduction Problem Desc. Id g Different Topology Types used, e.g. Uniform: Nodes are placed uniformly around basestation G i G i di ib i f d d b i Idea Reachability Direct I di t Gaussian: Gaussian distribution of nodes around basestation 100 topologies per topology type, 40 queries per topology Energy prediction based values measured on MicaZ Indirect Topology Information Energy prediction based values measured on MicaZ Criteria for success: Evaluation Setup Simulation Break Even Accurate Prediction for Reachability and Energy Optimization of probabilistic rebroadcast parameter p to reach ALL nodes with query Break-Even Deployment Conclusion to reach ALL nodes with query without rebroadcasting at each node Exploration of rebroadcast probability – reachability tradeoff Q&A Slide 14Markus Bestehorn
  15. 15. Evaluation – Simulation Results Result for node degree 16, 425 nodesIntroduction Problem Desc. Id Uniform Gaussian Idea Reachability Direct I di t p0 Indirect Topology Information Evaluation Setup Simulation Break Even Findings: Break-Even Deployment Conclusion Reachability & energy prediction accurate For most experiments, there exists a p0<1: Increasing p beyond p0 does not pay off regarding reachability! Q&A Slide 15Markus Bestehorn p0 p y g g y energy savings without reducing reachability
  16. 16. Break Even Point Exemplary computation: Uniform topology Introduction Problem Desc. Id p gy 425 nodes, node degree 16 Assuming T l di i h E h Al i h Idea Reachability Direct I di t Topology discovery using the Echo Algorithm Energy consumption values measured on MicaZ Indirect Topology Information Topology Discovery consumes 722 mAs Query dissemination with simple flooding (p=1) consumes 370 A Evaluation Setup Simulation Break Even 370 mAs Using prediction framework for 99% reachability p=0.6 220 mAs Break-Even Deployment Conclusion p Result: Topology Discovery pays off after 5 queries! Q&A Slide 16Markus Bestehorn
  17. 17. Evaluation – SPOT Deployment 17 SPOTs + Basestation deployed 10 Queries were disseminated into the Introduction Problem Desc. Id 10 Queries were disseminated into the network using Simple flooding (p=1) P b bili ti fl di Idea Reachability Direct I di t Probabilistic flooding Prediction algorithm was used to reach All nodes Indirect Topology Information At lowest possible rebroadcast prob. p Result: Broadcast Reached Sent Msg Received Evaluation Setup Simulation Break Even Broadcast Algorithm Reached Nodes Sent Msg. Received Msg. Simple 16.3 16.3 63.8 Probabilistic 15 4 10 2 34 Break-Even Deployment Conclusion Probabilistic Rebroadcast Optimization ~30% less sent messages Probabilistic 15.4 10.2 34 Q&A Slide 17Markus Bestehorn almost 50% less received messages
  18. 18. Summary Explored relations between Reachability Introduction Problem Desc. Id y Energy consumption for query dissemination Energy spent to acquire topology information I t d d l ti l f k Idea Reachability Direct I di t Introduced analytical framework Determines p0<1 for probabilistic broadcasting to reach all nodes Indirect Topology Information Allows predictions regarding sent / received messages Energy consumption Evaluation Setup Simulation Break Even gy p Energy spent for topology information pays off after a few (5) query disseminations Even if echo algorithm is used! Break-Even Deployment Conclusion Even if echo algorithm is used! Evaluation using Simulation & real Sensor network Q&A Slide 18Markus Bestehorn
  19. 19. Outlook Integrate „backwards“ reachability into framework Introduction Problem Desc. Id More topology information required? Payoff? Relation between query dissemination and query result Idea Reachability Direct I di t Relation between query dissemination and query result accuracy Indirect Topology Information p0 ~100% reachability 100% accuracy ~100% reachability 100% accuracy <100% reachability ? accuracy <100% reachability ? accuracy Evaluation Setup Simulation Break Even Dynamic usage of different broadcast algorithms Probabilistic approach good for dense networks Break-Even Deployment Conclusion Probabilistic approach good for dense networks Switch to other broadcast algorithms in less populated areas of the network? Q&A Slide 19Markus Bestehorn
  20. 20. Thank you for your attention! Introduction Problem Desc. Id Questions? Idea Reachability Direct I di tIndirect Topology Information Evaluation Setup Simulation Break EvenBreak-Even Deployment Conclusion Q&A Slide 20Markus Bestehorn

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