Introduction to Wireless Sensor Networks Directed Diffusion 28 March 2005

C. Intanogonwiwat R. Govindan D. Estrin Directed Diffusion : A Scalable and Robust Paradigm for Sensor Networks C. Intanogonwiwat R. Govindan D. Estrin

Introduction and terminology ….. Availing cheap nodes for sensing, communication and computation Deploying them in a region of interest to form a network and sensing environment phenomena ( events ) Events are transmitted ( directed ) from the sensing nodes( source ) to a destination ( sink ) for processing.

Simplified view….

Example of events …… Detecting variations in temperature Seismic vibrations Detecting any object like a four-legged animal in an area under inspection

Objective…… Making the routing algorithm 1)energy efficient : Optimizing radio communications, efficient routing and performing local computations 2)Scalable : Scale with an increase in the number of source and sinks 3)Robust : Handling node failures

Two ways of packet forwarding during routing…. Address Centric: The nodes route data independently without looking at the data content. Data Centric: The nodes while routing data use aggregation functions to eliminate redundancy. Our focus is data centric.

Assumptions …… Data centric routing Achieving a desired global behavior through local interactions Application aware – the task types are known at the time the sensor network is deployed

Directed Diffusion basics….. A sink node expresses interest in a particular data and inserts it as a query in the network Sensor nodes reply to this interest An interest may look like “At every I ms for the next T seconds send me a location estimate of any four legged animal in sub region R of the sensor field “

Possible naming (structure) of an Interest… type = four-legged animal Interval = 10ms Rect = [-100,200,300,400] Timestamp = 01:22:35 expiresAt = 01:30:40 Consists of attribute value pairs – its like querying the network for a particular data

Interest propagation…….. Flooding Geographic routing ( filtering out the interests on basis of the coordinate specification ) Using cached data to find out which neighbor had previously responded to similar interest Any other intelligent way, depending on the application

Establishing Gradients….. Done between every pair of nodes Consists of a <rate, direction > pair E.g. the gradient from A to neighbor B rate : the inverse of the value of the Interval in the interest sent by B direction : The link to B ( A might have many neighbors – a local naming is required ) They are used for sending back data to the sink – the path with the highest gradient is generally preferred

Simplified view….

The Algorithm……. Initially the sink sends an exploratory interest ( with a low data rate i.e. high interval ) The sensors store it in an Interest cache and forwards it. Subsequent interests having same type,interval,rect values are suppressed – thus selective forwarding Gradients set up between neighbors

Algorithm(cont……) A sensor whose sensed value matches with the type in an interest samples the readings based on the stored interval and sends it to all the neighbors with which it has a gradient The intermediate sensors route the data based on the gradient in that direction Eventually the sink receives the sampled information through some neighboring node

Directed Diffusion…. Directional Flooding Interest Gradient Sink Source

Directed Diffusion…. Interest Gradient Sink Source

Directed Diffusion…. Interest Gradient Sink Source

Directed Diffusion…. Gradient Sink Source

Data Caching……. Helps in suppressing similar interests from different sinks Helps in suppressing similar event information from different sources and helps in data aggregation

Data propagation….. The sources send back the data along the paths which were set up Interest Reply

Reinforcement ….. The sink chooses a high quality( optimal path ) by choosing the appropriate neighbor (using greedy strategy) and reinforces it by 1) sending an interest packet with a lower interval to that link 2) negatively reinforce non-optimal links

Reinforcement….. The reinforced interest is forwarded by each sensor node till it reaches the source The exploratory gradients exist which helps the network to be robust in case of node failures.

Example of reinforcement…. original interest reinforced interest

Directed Diffusion…. Directional Flooding Interest Gradient Sink Source

Directed Diffusion…. Interest Gradient Sink Source

Directed Diffusion…. Interest Gradient Sink Source

Directed Diffusion…. Gradient Sink Source

Directed Diffusion…. Reinforcement Gradient Sink Source

Directed Diffusion…. Reinforcement Gradient Sink Source

Directed Diffusion…. Reinforcement Gradient Sink Source

Directed Diffusion…. Data Gradient Sink Source

Negative reinforcement….. Send interest packets with higher interval to faulty links or links with higher delay. A measure to reduce redundant communication after finding out the optimal path

Directed Diffusion…. Data Gradient Sink Source

Directed Diffusion robustness…. Data Gradient Sink Source

Directed Diffusion…. Data Gradient Reinforcement Sink Source

Directed Diffusion…. Data Gradient Reinforcement Sink Source

Directed Diffusion…. Data Gradient Reinforcement Sink Source

Design considerations……

Multiple sources…… source sink Data aggregation…

Multiple sinks…… source sink sink

Evaluation metrics………. Average delay : average one way latency between transmitting an event and receiving it at the sink Average dissipated energy : ratio of the total dissipated energy per node to the number of distinct events seen by the sink Event delivery ratio : number of distinct events received to the number originally sent ns-2 simulator used for evaluation

Compared with …… Flooding : unrestricted broadcast of events to the sink nodes Omniscient multicast : Each source transmitting along the shortest path multicast tree to the sink nodes

DD performance graphs….

Impact of node failures….

Observations…….. Energy efficient – outperforms omniscient multicast Robust and fault tolerant Works only for query driven networks

Rumor Routing….. Two types of data delivery models …. Push(Event driven): Sources push data to the sink Pull (Query driven) : The sink pulling data from the sources A hybrid approach – rumor routing Rumor routing results in lesser number of transmissions than either of the above in certain situations.

Algorithm…. Sources on observing events create agents Agents carry routing information and go on a random walk across the network The have a fixed Time-To-Live Routing information is carried to nodes in the form of rumors and recorded. Agents also synchronize themselves with information from nodes.

Power of agents…

Rumor routing ….

Features… Once a query finds a recorded rumor it gets a definite direction/route to the event source. Saves on transmissions by avoiding groping around for the sources Also can get quick information about the event instead of having to go all the way to the source. Needs more storage, agent complexity involved

Thank you …