Directed Diffusion for Wireless Sensor Networking

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

Outline • Introduction • The problem • Directed Diffusion Concepts • Simulation Results • Summary CS240 Presentation

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 CS240 Presentation

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? Directed Diffusion The Problem A sensor field Event Sensor sources Sensor sink On event, sensed and/or processed information delivered to the inquiring destination CS240 Presentation

Directed Diffusion • Initial Goals: • Propose an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination CS240 Presentation

aggregation point Additional source Directed Diffusion-how it works Low data rate • 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 Sink “How many vehicles do you observe in the southeast quadrant?” High data rate Source CS240 Presentation

Directed Diffusion • Data Naming • Interests and Gradient • Data Propagation • Reinforcement • Path establishment • Path failure / recovery • Loop elimination CS240 Presentation

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 CS240 Presentation

Sink Sink Interest Sources 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 CS240 Presentation

Gradient Sink Sink Gradient Establishment Routed Data • Gradient for i1set up to upstream neighbor • No source routes • Gradient – a weighted reverse link • Low gradient  Few packets per unit time needed CS240 Presentation

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 CS240 Presentation

Reinforced gradient Reinforced gradient Reinforcement Event D B • From exploratory gradients, reinforce optimal path for high-rate data download  Unicast • Byrequesting higher-rate-i1 on the optimal path • Exploratory gradients still exist – useful for faults A sensor field Sink A C CS240 Presentation

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 Link A-M lossy A reinforces B B reinforces C … D need not A negative reinforces M M negative reinforces D Event D M Src A C B Sink CS240 Presentation

Loop Elimination P Q • 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 D M A CS240 Presentation

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 Simulation Results CS240 Presentation

Average Dissipated Energy flooding Multicast Diffusion In-network aggragation reduces DD redundancy - Flooding is poor because of multiple paths from source to sink CS240 Presentation

Delay flooding Diffusion Multicast DD finds least delay paths - Floof]ding incurs latency due to high MAC contention, colission CS240 Presentation

Event Delivery Ratio under node failures 0 % 10% 20% Delivery ration degrades with more nodes failures - Graceful degradation indicate efficient negative reinforcement CS240 Presentation

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 CS240 Presentation

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 CS240 Presentation

Thank You! CS240 Presentation

Directed Diffusion for Wireless Sensor Networking