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Directed Diffusion for Wireless Sensor Networking By Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, and Fabio Silva Presented by: Jin Sun 02/08/2005 CS240 Presentation 1 Outline Introduction The problem Directed Diffusion Concepts Simulation Results Summary 02/08/2005 CS240 Presentation 2 Introduction A region requires eventmonitoring Deploy sensors forming a distributed network Wireless networking Energy-limited nodes 02/08/2005 CS240 Presentation On event, sensed and/or processed information delivered to the inquiring destination 3 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? 02/08/2005 A sensor field Sensor sources CS240 Presentation Event Directed Diffusion Sensor sink On event, sensed and/or processed information delivered to the inquiring destination 4 Directed Diffusion Initial Goals: Propose an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination 02/08/2005 CS240 Presentation 5 Directed Diffusion-how it works Low data rate Sink “How many vehicles do you observe in the southeast quadrant?” High data rate Source aggregation point Robust, efficient data distribution in sensor networks 02/08/2005 Additional source 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 CS240 Presentation 6 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement Path establishment Path failure / recovery Loop elimination 02/08/2005 CS240 Presentation 7 Data Naming Expressing an Interest Using E.g., Data attribute-value pairs Type = Wheeled vehicle Interval = 20 ms Duration = 10 s Field = [x1, y1, x2, y2] // detect vehicle location // send events every 20ms // Send for next 10 s // from sensors in this area reply Using attribute-value pairs E.g., Type = Wheeled vehicle // type of vehicle seen 02/08/2005 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, CS240 x2, y2] // from sensors in this area Presentation 8 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement Path establishment Path failure / recovery Loop elimination 02/08/2005 CS240 Presentation 9 Interest Propagation Sink Sink Sources Interest 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 02/08/2005 CS240 Presentation 10 Gradient Establishment Sink Gradient Sink Routed Data Gradient for i1 set up to upstream neighbor No source routes Gradient – a weighted reverse link Low gradient Few packets per unit time needed 02/08/2005 CS240 Presentation 11 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement Path establishment Path failure / recovery Loop elimination 02/08/2005 CS240 Presentation 12 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 02/08/2005 of bidirectional gradient avoided CS240 Presentation 13 Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement Path establishment Path failure / recovery Loop elimination 02/08/2005 CS240 Presentation 14 Reinforcement Reinforced gradient D Event Reinforced gradient B A sensor field Sink A C From exploratory gradients, reinforce optimal path for high-rate data download Unicast By requesting higher-rate-i1 on the optimal path Exploratory 02/08/2005 gradients still exist – useful for faults CS240 Presentation 15 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 D Event M Src A C 02/08/2005 B CS240 Presentation Sink Link A-M lossy A reinforces B B reinforces C … D need not A negative reinforces M M negative reinforces D 16 Loop Elimination P D Q M A 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 02/08/2005 CS240 Presentation 17 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 02/08/2005 CS240 Presentation 18 Average Dissipated Energy flooding Multicast Diffusion In-network aggragation reduces DD redundancy - Flooding is poor because of multiple paths from source to sink 02/08/2005 CS240 Presentation 19 Delay flooding Diffusion Multicast DD finds least delay paths - Floof]ding incurs latency due to high MAC contention, colission 02/08/2005 CS240 Presentation 20 Event Delivery Ratio under node failures 0% 10% 20% Delivery ration degrades with more nodes failures - Graceful degradation indicate efficient negative reinforcement 02/08/2005 CS240 Presentation 21 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 02/08/2005 CS240 Presentation 22 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 02/08/2005 CS240 Presentation 23 Thank You! 02/08/2005 CS240 Presentation 24