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02/08/2005CS240 Presentation 1 Directed Diffusion for Wireless Sensor Networking By Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann,

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Presentation on theme: "02/08/2005CS240 Presentation 1 Directed Diffusion for Wireless Sensor Networking By Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann,"— Presentation transcript:

1 02/08/2005CS240 Presentation 1 Directed Diffusion for Wireless Sensor Networking By Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, and Fabio Silva Presented by: Jin Sun

2 CS240 Presentation2 02/08/2005 Outline Introduction The problem Directed Diffusion Concepts Simulation Results Summary

3 CS240 Presentation3 02/08/2005 Introduction A region requires event- monitoring Deploy sensors forming a distributed network  Wireless networking  Energy-limited nodes On event, sensed and/or processed information delivered to the inquiring destination

4 CS240 Presentation4 02/08/2005 The Problem Where should the data be stored? How should queries be routed to the stored data? How should queries for sensor networks be expressed? Where and how should aggregation be performed? Event Sensor sources Sensor sink Directed Diffusion A sensor field On event, sensed and/or processed information delivered to the inquiring destination

5 CS240 Presentation5 02/08/2005 Directed Diffusion Initial Goals:  Propose an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination

6 CS240 Presentation6 02/08/2005 Directed Diffusion-how it works Robust, efficient data distribution in sensor networks  name data (not nodes), use physicality  diffuse requests and responses across network  optimize path with gradient-based feedback  additional data can be processed and aggregated within the network “How many vehicles do you observe in the southeast quadrant?” Source Sink aggregation pointAdditional source Low data rate High data rate

7 CS240 Presentation7 02/08/2005 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement  Path establishment  Path failure / recovery  Loop elimination

8 CS240 Presentation8 02/08/2005 Data Naming Expressing an Interest  Using attribute-value pairs  E.g., Data reply  Using attribute-value pairs  E.g., Type = Wheeled vehicle// detect vehicle location Interval = 20 ms// send events every 20ms Duration = 10 s// Send for next 10 s Field = [x1, y1, x2, y2]// from sensors in this area Type = Wheeled vehicle// type of vehicle seen Instance = truck// instance of this type Intensity = 0.6// signal amplitude measure Confidence = 0.85// confidence in the match Timestamp = 01:20:34// event generation time Field = [x1, y1, x2, y2]// from sensors in this area

9 CS240 Presentation9 02/08/2005 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement  Path establishment  Path failure / recovery  Loop elimination

10 CS240 Presentation10 02/08/2005 Interest Propagation Inquirer (sink) broadcasts exploratory interest, i1  Intended to discover routes between source and sink Neighbors update interest-cache and forwards i1 No way of knowing differentiating new interests from repeated Sink Sources Interest

11 CS240 Presentation11 02/08/2005 Gradient Establishment Routed Data Sink Gradient Gradient for i1 set up to upstream neighbor  No source routes  Gradient – a weighted reverse link  Low gradient  Few packets per unit time needed

12 CS240 Presentation12 02/08/2005 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement  Path establishment  Path failure / recovery  Loop elimination

13 CS240 Presentation13 02/08/2005 Event-data propagation Event e1 occurs, matches i1 in sensor cache  e1 identified based on waveform pattern matching Interest reply diffused down gradient (unicast)  Diffusion initially exploratory (low packet-rate) Cache filters suppress previously seen data  Problem of bidirectional gradient avoided

14 CS240 Presentation14 02/08/2005 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement  Path establishment  Path failure / recovery  Loop elimination

15 CS240 Presentation15 02/08/2005 Reinforcement From exploratory gradients, reinforce optimal path for high-rate data download  Unicast  By requesting higher-rate-i1 on the optimal path  Exploratory gradients still exist – useful for faults Event Sink A A sensor field Reinforced gradient B C D

16 CS240 Presentation16 02/08/2005 Path Failure / Recovery Link failure detected by reduced rate, data loss  Choose next best link (i.e., compare links based on infrequent exploratory downloads) Negatively reinforce lossy link  Either send i1 with base (exploratory) data rate  Or, allow neighbor’s cache to expire over time Event Sink Src A C B M D Link A-M lossy A reinforces B B reinforces C … D need not A negative reinforces M M negative reinforces D

17 CS240 Presentation17 02/08/2005 M gets same data from both D and P, but P always delivers late due to looping  M negatively-reinforces (nr) P, P nr Q, Q nr M  Loop {M  Q  P} eliminated Conservative nr useful for fault resilience Loop Elimination A QP DM

18 CS240 Presentation18 02/08/2005 Simulation Results Compare directed diffusion to  flooding  Omniscient multicast Key metrics:  Average dissipated energy per node energy dissipation / # events seen by sinks  Average packet delay latency of event transmission to reception at sink  Distinct event delivery # of distinct events received / # of events originally sent

19 CS240 Presentation19 02/08/2005 Average Dissipated Energy flooding Diffusion Multicast In-network aggragation reduces DD redundancy - Flooding is poor because of multiple paths from source to sink

20 CS240 Presentation20 02/08/2005 Delay flooding Diffusion Multicast DD finds least delay paths - Floof]ding incurs latency due to high MAC contention, colission

21 CS240 Presentation21 02/08/2005 Event Delivery Ratio under node failures 0 % 10% 20% Delivery ration degrades with more nodes failures - Graceful degradation indicate efficient negative reinforcement

22 CS240 Presentation22 02/08/2005 Summary Main Contributions  Description of new networking paradigm Interests, gradients, reinforcement Benefits of in-network processing Aggregation and nested-queries  Works with multiple sources and sinks  Can perform local repair  Reinforce another path if a node dies

23 CS240 Presentation23 02/08/2005 Summary (cont’d) Disadvantages  Design doesn’t deal with congestion or loss  Periodic broadcasts of interest reduces network lifetime  Nodes within range of human operator may die quickly

24 02/08/2005CS240 Presentation 24 Thank You!


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