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Ad Hoc Now2008 Probabilistic Query Dissemination

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Talk presented at Ad Hoc Now 2008 in Nice.

Talk presented at Ad Hoc Now 2008 in Nice.

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Ad Hoc Now2008 Probabilistic Query Dissemination Ad Hoc Now2008 Probabilistic Query Dissemination Presentation Transcript

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