Directed Diffusion for Wireless Sensor Networking By Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, and Fabio Silva Presented by: Jin Sun 1. Sensor Network – 100s or 1000s of sensor nodes deployed in a geographical region to sense an event. 2. Sensor Networks provide a high-level description of the event being sensed. 3. Used in harsh inhospitable environments, environmental control in offices, robot control and guidance in automatic manufacturing environments etc 02/08/2005 CS240 Presentation

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

The Problem Where should the data be stored? Event A sensor field 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 Directed Diffusion How many people cross Linden St. every day? (eg.) How much poisonous gas in the atmosphere? We can expect that with the development in processors, memory and radio technology, small and cheap sensor node will be capable of wireless communication and significant computation Several problem here: 1. Where should the data be stored? 2. How should queries be routed to the stored data? 3. How should queries for sensor networks be expressed? 4.Where and how should aggregation be performed? Sensor sink On event, sensed and/or processed information delivered to the inquiring destination 02/08/2005 CS240 Presentation

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

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 Additional source 1. Data Centric Routing 2. Query–Response Model A Query (Interest) is broadcasted by a node (sink). This reaches the sensor nodes (sources) which can satisfy the query. This sets up exploratory gradients. 3. Once data is available, the sources sends data back to the sink via a sequence of local interaction. 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 02/08/2005 CS240 Presentation

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

Data Naming Data reply Expressing an Interest Using attribute-value pairs E.g., Data reply 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 Reply need to modified 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 02/08/2005 CS240 Presentation

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

Interest Propagation 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 Periodically purged Host must reissue interest No information about sink Gradient table Rate per neighbor Timestamp Expiration 02/08/2005 CS240 Presentation

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 Periodically purged Host must reissue interest No information about sink Gradient table Rate per neighbor Timestamp Expiration 02/08/2005 CS240 Presentation

Directed Diffusion Data Naming Interests and Gradient Data Propagation Reinforcement Path establishment Path failure / recovery Loop elimination 02/08/2005 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 1. A node that matches an interest generates reply at desired rate 2. If node receives a reply, it searches interest cache 3. Forwards along given route(s) if found Drops otherwise 4. Loop prevention and otherwise 02/08/2005 CS240 Presentation

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

Reinforcement Reinforced gradient Event Reinforced gradient D B 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 A sensor field Sink A C 2 .Reinforcement period: multiple paths are reduced and now still certain redundancy is permitted 3. Message cache can be used to avoid loop Source can reissue the same request with a higher rate "Draw down" higher quality data from a particular neighbor Other node reacts when receiving "Outflow" increased, must reinforce another node to increase "inflow" Selects and empirically low delay path Such as the first neighbor that sent it the reply Poor metric, switches a lot – wasteful Need a metric that in consistent and doesn’t switch paths unless sure beneficial Perhaps neighbor that usually returns the fastest Neighbor with most power Best metric unknown, probably application dependent 02/08/2005 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 02/08/2005 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 02/08/2005 CS240 Presentation

Simulation Results Compare directed diffusion to Key metrics: 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

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

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

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

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 1. Works with multiple sources and sinks 2. Can perform local repair 3. Reinforce another path if a node dies 02/08/2005 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 1. Periodic broadcasts of interest reduces network lifetime 2. Nodes within range of human operator may die quickly. 02/08/2005 CS240 Presentation

Thank You! 02/08/2005 CS240 Presentation