Introduction to Wireless Sensor Networks Directed Diffusion 28

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

Introduction and terminology …. . • Availing cheap nodes for sensing, communication and computation

Simplified view…. The University of Iowa. Copyright© 2005 A. Kruger

Example of events …… • Detecting variations in temperature • Seismic vibrations • Detecting

Objective…… • Making the routing algorithm 1)energy efficient : Optimizing radio communications, efficient routing

Two ways of packet forwarding during routing…. • Address Centric: The nodes route data

Assumptions …… • Data centric routing • Achieving a desired global behavior through local

Directed Diffusion basics…. . • A sink node expresses interest in a particular data

Possible naming (structure) of an Interest… type = four-legged animal Interval = 10 ms

Interest propagation……. . • Flooding • Geographic routing ( filtering out the interests on

Establishing Gradients…. . • Done between every pair of nodes • Consists of a

The Algorithm……. • Initially the sink sends an exploratory interest ( with a low

Algorithm(cont……) • A sensor whose sensed value matches with the type in an interest

Directed Diffusion…. Directional Flooding Interest Gradient Source The University of Iowa. Copyright© 2005 Sink

Directed Diffusion…. Interest Gradient Source The University of Iowa. Copyright© 2005 Sink A. Kruger

Directed Diffusion…. Gradient Source The University of Iowa. Copyright© 2005 Sink A. Kruger

Data Caching……. • Helps in suppressing similar interests from different sinks • Helps in

Data propagation…. . • The sources send back the data along the paths which

Reinforcement …. . • The sink chooses a high quality( optimal path ) by

Reinforcement…. . • The reinforced interest is forwarded by each sensor node till it

Example of reinforcement…. original interest The University of Iowa. Copyright© 2005 reinforced interest A.

Directed Diffusion…. Reinforcement Gradient Source The University of Iowa. Copyright© 2005 Sink A. Kruger

Directed Diffusion…. Data Gradient Source The University of Iowa. Copyright© 2005 Sink A. Kruger

Negative reinforcement…. . • Send interest packets with higher interval to faulty links or

Directed Diffusion robustness…. Data Gradient Source The University of Iowa. Copyright© 2005 Sink A.

Directed Diffusion…. Data Gradient Reinforcement Source The University of Iowa. Copyright© 2005 Sink A.

Design considerations…… The University of Iowa. Copyright© 2005 A. Kruger

Multiple sources…… source sink Data aggregation… The University of Iowa. Copyright© 2005 A. Kruger

Multiple sinks…… source sink The University of Iowa. Copyright© 2005 A. Kruger

Evaluation metrics………. • Average delay : average one way latency between transmitting an event

Compared with …… • Flooding : unrestricted broadcast of events to the sink nodes

DD performance graphs…. The University of Iowa. Copyright© 2005 A. Kruger

Impact of node failures…. The University of Iowa. Copyright© 2005 A. Kruger

Observations……. . • Energy efficient – outperforms omniscient multicast • Robust and fault tolerant

Rumor Routing…. . • Two types of data delivery models …. Push(Event driven): Sources

The University of Iowa. Copyright© 2005 A. Kruger

Algorithm…. • Sources on observing events create agents • Agents carry routing information and

Power of agents… The University of Iowa. Copyright© 2005 A. Kruger

Rumor routing …. The University of Iowa. Copyright© 2005 A. Kruger

Features… • Once a query finds a recorded rumor it gets a definite direction/route

• Thank you … The University of Iowa. Copyright© 2005 A. Kruger

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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. The University of Iowa. Copyright© 2005 A. Kruger

Example of events …… • Detecting variations in temperature • Seismic vibrations • Detecting any object like a four-legged animal in an area under inspection The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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. The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 “ The University of Iowa. Copyright© 2005 A. Kruger

Possible naming (structure) of an Interest… type = four-legged animal Interval = 10 ms Rect = [-100, 200, 300, 400] Timestamp = 01: 22: 35 expires. At = 01: 30: 40 Consists of attribute value pairs – its like querying the network for a particular data The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

Data Caching……. • Helps in suppressing similar interests from different sinks • Helps in suppressing similar event information from different sources and helps in data aggregation The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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. The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger

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. The University of Iowa. Copyright© 2005 A. Kruger

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. The University of Iowa. Copyright© 2005 A. Kruger

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 The University of Iowa. Copyright© 2005 A. Kruger