1. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Charlmek Intanagonwiwat Ramesh Govindan Deborah Estrin Presentation By : Hardik Shah

2. .. Key Issue : Embedding the sensors in the physical world requires network of such nodes to co-ordinate to perform distributed sensing of environmental phenomena. Goal : Embed computing (computing device/sensors) close enough to the environment to sense (detect) the phenomena, monitor and take actions accordingly.

3. Motivation Motivation E nergy Efficiency E nergy Efficiency Infeasible to transmit time-series data even hop-by-hop. Infeasible to transmit time-series data even hop-by-hop. Perform local computation and reduce data before transmission. Perform local computation and reduce data before transmission. Scalability Scalability Requires thousands of sensors to coordinate to reach the decision. Requires thousands of sensors to coordinate to reach the decision. Decisions should be done as much local as possible. Decisions should be done as much local as possible. Robustness Robustness Handle changing environment situations Handle changing environment situations

4. Architectural Requirements Application aware communication premetives (expressed in terms of named data not in terms of node who request data) Application aware communication premetives (expressed in terms of named data not in terms of node who request data) Achieve locality for decision making. Achieve locality for decision making. (and reduce the communication) (and reduce the communication) Application centric, data-driven networks. Application centric, data-driven networks. Achieve desired global behavior through localized interactions, without global state. Achieve desired global behavior through localized interactions, without global state.

5. Directed Diffusion Data dissemination paradigm for distributed network of sensors. Data dissemination paradigm for distributed network of sensors.

6. Assumptions Sensor network's lower level communication is topology independent.( not like IP networks mean logical connectivity distinct from physical geography). Sensor network's lower level communication is topology independent.( not like IP networks mean logical connectivity distinct from physical geography). Data aggregation is task dependant.( set of tasks defined by application (or set of applications) which defines interests for network) Data aggregation is task dependant.( set of tasks defined by application (or set of applications) which defines interests for network) Naming scheme decides the expressiveness and effectiveness of communication. Naming scheme decides the expressiveness and effectiveness of communication.

7. Basic Directed Diffusion concepts Communication for named data not for those who produces (its not our concern!) Communication for named data not for those who produces (its not our concern!) Query generates (virtually from any node in the network) interest (collection of attribute value pair) Query generates (virtually from any node in the network) interest (collection of attribute value pair) For specific data (which tries to map with events supported by network ). For specific data (which tries to map with events supported by network ). Interest diffused locally based on the naming scheme (its most imp since communication done for named data (hierarchical /flat)( mit ins uses hierarchical approach).) Interest diffused locally based on the naming scheme (its most imp since communication done for named data (hierarchical /flat)( mit ins uses hierarchical approach).)

8. This sets the gradients (within network) to draw events matching the interest. This sets the gradients (within network) to draw events matching the interest. Gradient represents both direction towards data matching and status of demand with desired update rate (active/inactive). Gradient represents both direction towards data matching and status of demand with desired update rate (active/inactive).

9. Architectural elements Naming Scheme Naming Scheme Interest propagation Interest propagation Data propagation Data propagation Data caching and aggregation Data caching and aggregation Reinforcement Reinforcement

10. Naming Given Set of Tasks supported by sensor network selecting a naming scheme is first step in designing sensor networks. Given Set of Tasks supported by sensor network selecting a naming scheme is first step in designing sensor networks. Basically list of attribute value pairs. Basically list of attribute value pairs. E.g. For tracking animal its attributes should describe tasks like, type of animal, E.g. For tracking animal its attributes should describe tasks like, type of animal, geographic location to track, interval for sending updates, duration for which it was recorded (event occurrence time) geographic location to track, interval for sending updates, duration for which it was recorded (event occurrence time)

11. Interest propagation Flooding. Flooding. Location aware routing (or geo casting). Location aware routing (or geo casting). Directional propagation on previously cached data. Directional propagation on previously cached data. In paper they have used flooding approach. In paper they have used flooding approach.

12. Have u seen any four leg animal??? Sink Event Source Interest Propagation (Flooding) interests QUERY DIFFUSED IN TO INTEREST WHICH IS LIST OF ATTRIBUTE VALUE PAIRS

13. INTIAL GRADIENTS SETUP(VALUE+DIRECTION) YES I HAVE SEEN ONE….

14. Data Propagation Reinforcement to single path delivery. Reinforcement to single path delivery. Multi path delivery with selective quality. Multi path delivery with selective quality. Probabilistic forwarding with multi path delivery. Probabilistic forwarding with multi path delivery. For selecting neighbor who gave first or either who has highest energy or lowest delay can be chosen. (Its application dependant.) For selecting neighbor who gave first or either who has highest energy or lowest delay can be chosen. (Its application dependant.)

15. DATA DELIVERY THROUGH REINFORCED PATH SINGLE PATH DELIVERY (CAN BE MULTIPATH ALSO)

16. Data caching and aggregation Data caching and aggregation Robust data delivery in case of node failure. Robust data delivery in case of node failure. Validate with timestamps. Validate with timestamps. May use hierarchical scheme with one or more entry for distinct interest. May use hierarchical scheme with one or more entry for distinct interest.

17. IN CASE OF NODE FAILURE USE ALTERNATIVE PATHS

18. Reinforcement When to reinforce ?(quality/delay matrices can be chosen) When to reinforce ?(quality/delay matrices can be chosen) Whom to reinforce ? Whom to reinforce ? How many to reinforce? How many to reinforce? When to send negative reinforcement ? When to send negative reinforcement ?

19. TinyOS Implementation

21. Summary of results Diffusion has achieved same delay of omniscient multicast. Diffusion has achieved same delay of omniscient multicast. Application level data dissemination has potential for energy saving. Application level data dissemination has potential for energy saving. This work did not develop the software architecture necessary for realizing attributes for in networking processing in an operational system. This work did not develop the software architecture necessary for realizing attributes for in networking processing in an operational system.

22. Comparison of Directed Diffusion to flooding and omniscient multicast

23. Work is influenced by Multicast routing join techniques for interest propagation spt tree construction (or shared tree) for deciding reinforcement policies. Multicast routing join techniques for interest propagation spt tree construction (or shared tree) for deciding reinforcement policies. Declarative routing is similar in approach except no filters used. Declarative routing is similar in approach except no filters used. Intentional naming system of mit has similar concept for naming as directed diffusion(but hierarchical not flat attribute value pair.) Intentional naming system of mit has similar concept for naming as directed diffusion(but hierarchical not flat attribute value pair.) In network processing for local repair is similar to router assist for localized error recovery. In network processing for local repair is similar to router assist for localized error recovery.

24. What it Proposes? What it Proposes? A simple architecture that uses a topological- independent naming for low-level communication to achieve flexible, yet highly energy efficient application designs. A simple architecture that uses a topological- independent naming for low-level communication to achieve flexible, yet highly energy efficient application designs.

25. Discusses Design space of protocols underlying directed diffusion. (Where every sensor is task aware and possibly knows where it is.)

26. Evaluates Design questions concerning naming and in- network processing encountered in deploying a sensor network and presents experimental results. Design questions concerning naming and in- network processing encountered in deploying a sensor network and presents experimental results.

27. Issues of Concern Ad hoc, self organizing, adaptive systems with predictable behavior Ad hoc, self organizing, adaptive systems with predictable behavior Collaborative processing, data fusion, multiple sensory modalities Collaborative processing, data fusion, multiple sensory modalities Data analysis/mining Data analysis/mining

28. Issues yet to be resolved How to handle congested network? How to handle congested network? Semantics for gradients. Semantics for gradients. (Variant of D.D. Is gradient directed diffusion.) (Variant of D.D. Is gradient directed diffusion.) Handling of more than one sources. Handling of more than one sources. Negative reinforcement increases delay and contention (D.D. Uses mac layer unicast) Negative reinforcement increases delay and contention (D.D. Uses mac layer unicast)

29. Optimization Create processing points in the network. Create processing points in the network. High level interests/queries for activity triggers lower level local queries for particular data modalities and signatures (e.g. acoustic and vibration patterns that are mapped to the activity of interest)As opposed to generating detailed queries at sink points and relying on High level interests/queries for activity triggers lower level local queries for particular data modalities and signatures (e.g. acoustic and vibration patterns that are mapped to the activity of interest)As opposed to generating detailed queries at sink points and relying on opportunistic aggregation alone. opportunistic aggregation alone.

30. Work In Progress Multi path: reinforcing multiple upstream neighbors for load balancing and robustness. Multi path: reinforcing multiple upstream neighbors for load balancing and robustness. Disjoint paths selection. Disjoint paths selection. Opportunistic aggregation of source data Opportunistic aggregation of source data Managing gradients/resources. Managing gradients/resources. Tiny diffusion for Motes. Tiny diffusion for Motes. Diffusion under mobility: objects, nodes Diffusion under mobility: objects, nodes

31. Possible Areas of Future Work Adaptation to local node densities. Adaptation to local node densities. How to map diffusion’s parameters to Diffusion needs? How to map diffusion’s parameters to Diffusion needs? Diffusion to work on Asymmetric links. Diffusion to work on Asymmetric links. Intelligence in filters for decision making. Intelligence in filters for decision making.

32. Reference Design and implementation of INS. Design and implementation of INS. Location aware routing. Location aware routing. Geocasting in mobile ad hoc networks Geocasting in mobile ad hoc networks Location based multicast algorithms Location based multicast algorithms Query localization techniques for on- demand routing protocols in ad-hoc net. Query localization techniques for on- demand routing protocols in ad-hoc net. Declarative routing. Declarative routing.

33. More Information SCADDS project SCADDS project http://www.isi.edu/scadds http://www.isi.edu/scadds ns-2: network simulator ns-2: network simulator http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz testbed and software testbed and software http://www.isi.edu/scadds/testbeds.html http://www.isi.edu/scadds/testbeds.html