Presenter: Malik Tubaishat Department of Computer Science University of Missouri - Rolla

1. Next Century Challenges: Scalable Coordination in Sensor Networks Deborah Estrin, Ramesh Govindan, John Heidemann and Satish Kumar In Proceedings of the Fifth Annual International Conference on Mobile Computing and Networks (MobiCOM '99), August 1999, Seattle, Washington. Presenter: Malik Tubaishat Department of Computer Science University of Missouri - Rolla Directed Diffusion: A Scalable and Robust CommunicationParadigm for Sensor Networks Chalermek Intanagonwiwat,  Ramesh Govindan and Deborah Estrin In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networking (MobiCOM '00), August 2000, Boston, Massachusetts.

2. Topics Information gathering and processing, coordinate amongst themselves to achieve a larger sensing task Necessary for sensor network coordination Communication model for describing localized algorithms

3. Sensor Networks Networks that are formed when a set of small un-tethered sensor devices are deployed in an ad-hoc fashion cooperate on sensing a physical phenomenon.

4. Sensor Network Scenario: • Sensors are used to analyze the motion of a tornado • Sensors are deployed in a forest for fire detection • Sensors are attached to taxi cabs in a large metropolitan area to study the traffic conditions and plan routes effectively

5. Sensor Network Characteristics • Sensor • Observer • Phenomenon

6. Sensor The device that implements the physical sensing of environmental phenomena and reporting of measurements (through wireless communication). Typically, it consists of five components: 1. sensing hardware, 2. memory, 3. battery, 4. embedded processor, 5. trans-receiver.

7. Observer • The end user interested in obtaining information disseminated by the sensor network about the phenomenon. • The observer may indicate interests (or queries) to the network and receive responses to these queries.

8. Phenomenon The entity of interest to the observer that is being sensed and optionally analyzed/filtered by the sensor network.

9. Network Dynamics Models • A sensor network forms a path between the phenomenon and the observer • The goal of the sensor network protocol is to create and maintain this path (or multiple paths) under dynamic conditions while meeting the application requirements of low energy, low latency, high accuracy, and fault tolerance.

10. Approaches to Construct and Maintain the path between the observer and the phenomenon: • Static Sensor Networks • Mobile Sensor Networks

11. Static Sensor Networks • In static sensor networks, there is no motion among communicating sensors, the observer and the phenomenon • An elected node relays a summary of the local observations to the observer Example: A group of sensors spread for temperature sensing • Such algorithms extend the lifetime of the sensor network because they trade-off local computation for communication.

12. Mobile Sensor Networks • In dynamic sensor networks, either the sensors themselves, the observer, or the phenomenon are mobile • Whenever any of the sensors associated with the current path from the observer to the phenomenon moves, the path may fail • Either the observer or the concerned sensor must take the initiative to rebuild a new path.

13. Building Paths • During initial setup, the observer can build multiple paths between itself and the phenomenon and cache them, choosing the one that is the most beneficial at that time as the current path • If the path fails, another of the cached paths can be used • If all the cached paths are invalid then the observer must rebuild new paths.

14. Motion of the Components • Mobile Observer • Mobile Sensors • Mobile Phenomena

15. Mobile Observer For example, a plane might fly over a field periodically to collect information from a sensor network. Thus the observer, in the plane, is moving relative to the sensors and phenomena on the ground.

16. Mobile Sensors • For example, consider traffic monitoring implemented by attaching sensors to taxis • As the taxis move, the attached sensors continuously communicate with each other about their own observations of the traffic conditions

17. Mobile Sensors (Cont.) • The overhead of maintaining a globally unique sensor ID in a hierarchical fashion like an IP address is expensive and not needed • Instead, these sensors should communicate only with their neighbors • In such networks, repairing a path can be used so that the information about the phenomenon is always available to the observer regardless of the mobility of the individual sensors.

18. Mobile Phenomena • A typical example of this paradigm is sensors deployed for animal detection • In this case the infrastructure level communication should be event-driven • Depending on the density of the phenomena, it will be inefficient if all the sensor nodes are active all the time • Only the sensors in the vicinity of the mobile phenomenon need to be active • The number of active sensors in the vicinity of the phenomenon can be determined by application specific goals such as accuracy, latency, and energy efficiency.

19. Concern • The primary concern here is the ability of the sensor network to report the desired level of accuracy under latency constraints within an acceptable deployment cost • The accuracy is a function of the sensing technology of the sensors and their distance from the phenomenon.

20. Distributed Sensor Networks Next Century Challenges: Scalable Coordination in Sensor Networks

21. Topics Information gathering and processing, coordinate amongst themselves to achieve a larger sensing task Necessary for sensor network coordination Communication model for describing localized algorithms

22. Scenario Several thousand sensors are rapidly deployed (e.g., thrown from an aircraft) in remote terrain • The sensors communicate to establish a communication network • Divide the task of mapping and monitoring the terrain amongst themselves in an energy efficient manner • Adapt their overall sensing accuracy to the remaining total resources • Re-organize upon sensor failure.

23. Applications: Environmental Analysis

24. Applications: Contaminant Flow Monitoring

25. Applications: Traffic Control • Sensor attached to every vehicle. • Capable of detecting their location, vehicle sizes, speeds and densities; road conditions… • Alternate routes, estimate trip times…

26. Current Networks • Internet • Each PC on the Internet has a user who can resolve or at least report all manner of minor errors and problems • Automated Factories • May contain hundreds of largely unsupervised computers • Deployed with very careful planning and react to very few external events

27. Differences with Current Networks Dynamic Changes • Sensor Network • Sensors may be inaccessible • extremely difficult to pay special attention to any individual node • Ratio of communicating nodes to users is much greater • They operate and must respond to very dynamic environments • Deployed in a very ad hoc manner (possibly thrown down at random) • Must automatically adapt to changes in environment and requirements

28. Localized Algorithms Localized algorithms – where sensors only interact with other sensors in a restricted vicinity, but nevertheless collectively achieve a desired global objective. Objective: Electing sensors that form the longest-baseline for triangulation for locating external objects.

29. Data Centric unlike traditional networks, a sensor node may not need an identity (e.g., an address) • What is the temperature at sensor #27? () • Where are nodes whose temperatures recently exceeded 30 degrees? () • Applications focus on the data generated by sensors • This approach decouples data from the sensor that produced it • This allows for more robust application design: even if sensor #27 dies, the data it generates can be cached in other (possibly neighboring) sensors for later retrieval.

30. Localized Clustering Algorithm Clustering: efficient coordination cluster Cluster-head

31. Overview: • A link level procedure is run on each sensor that adjusts the transmission power and thus the communication range to a minimum value that maintains full network connectivity • The clustering algorithm then elects cluster-head sensors such that each sensor in the multi-hop network is associated with a cluster-head sensor as its parent • Cluster-heads could summarize the object located in their clusters to provide a less detailed view to distant nodes.

32. Assumption: • Associate sensors at a particular level with a radius • The radius specifies the number of physical hops that a sensor’s advertisement will travel • Sensors at a higher level are associated with larger radii that those at lower levels • All sensors start off at the lowest level of 0 • Each sensor then sends out periodic advertisements to sensors within radius hops • Advertisement = { hierarchical level, parent ID, remaining energy }

33. Localized Clustering Algorithm Advertisement L0 L0 L0 L0 L0 Wait time In order to allow advertisements from various sensors to reach each other hierarchical level, parent ID, remaining energy

34. Localized Clustering Algorithm Promotion Timer • Start promotion timer if no parent. • Promotion timer: inversely proportional (remaining energy, number of other sensors from whom level 0 advertisements were received) • If promotion timer expires  GO

35. Localized Clustering Algorithm Promotion Timer • Start promotion timer if no parent. • Promotion timer: inversely proportional (remaining energy, number of other sensors from whom level 0 advertisements were received) • If promotion timer expires  GO L0 New advertisement L0 L0 L0 L1 Cluster-head

36. Next: • Once a level 0 sensor picks a closest potential parent, it cancels its promotion time if running and drops out of the election process • After promotion, the level 1 sensors start a wait time proportional to their new larger radius • At the end of the wait period, the level 1 sensor may demote it self if it does not have any child sensor or if its energy level is less than a certain threshold function of its children’s energy  Any change in network conditions, or in sensor energy level results in re-clustering with bounded delay

37. Application of Clustering Algorithm • Aim: To pinpoint in an energy-efficient manner, the exact location of objects. • Accuracy: widest possible measurement baseline. • Energy efficiency: fewest number of sensors participating in the triangulation.

38. Triangulation • Determine position in space. • Can specify approx direction of object relative to its own location.

39. Algorithm of Locating Objects There exists a simple rule whereby each cluster-head sensor locally determine (based on information from neighbor cluster-heads alone) whether it should participate in the triangulation computation: 1- If all the neighboring cluster-heads of a cluster-head sensor lie on the same side of a line drawn between the sensor and the object, then that cluster-head sensor elects itself as a participant in the computation 2- Once elected, these sensors report their readings to an external observer

40. Base-line Estimation

41. Advantages of Cluster-based Approach • Sensor algorithms only use local information. • generally lower energy consumption in comparison to global communication. • Robust to link or node failures and network partitions • mechanisms for self-configuration can be simpler.

42. Advantages of Cluster-based Approach • Local communication and per-hop data filtering • avoid transmitting large amounts of data over long distances. • preserving node energy resources. • Node energy resources are better utilized • cluster-heads adapt to changing energy levels.

43. Disadvantage of Cluster-based Approach • Non-optimal under certain terrain conditions.

44. Several Sensors Electing Themselves Obstacle Allow a cluster-head to switch on some number of child sensors in its cluster to do object location.

45. Adaptive Fidelity Algorithms Z Y A quality of the answer can be traded against battery lifetime, network bandwidth, or number of active sensors. Some cluster-head sensors turn themselves off to conserve power.

46. Directed Diffusion: A Scalable and Robust CommunicationParadigm for Sensor Networks Source: http://www.isi.edu/scadds

47. Topics Information gathering and processing, coordinate amongst themselves to achieve a larger sensing task Necessary for sensor network coordination Communication model for describing localized algorithms

48. Directed Diffusion • A communication paradigm • A new data dissemination paradigm

49. Properties • Directed diffusion is data-centric in that all communication is for named data (not nodes id). • All nodes are application-aware • This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network • Interactions are localized • Data can be aggregated or processed within the network • Network empirically adopts to best distribution path.

50. Sensor Network Examples: • How many pedestrians do you observe in the geographical region X? • Tell me in what direction that vehicle in region Y is moving.