Presenter: Malik Tubaishat Department of Computer Science University of Missouri - Rolla 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. Directed Diffusion: A Scalable and Robust Communication Paradigm 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.

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 used to analyze the motion of a tornado Sensors are deployed in a forest for fire detection 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 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 Sensor Observer Observer Phenomenon 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, 1. sensing hardware, 2. memory, 3. battery, 4. embedded processor, 5. trans-receiver. 5. trans-receiver.

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

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 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. 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 Static Sensor Networks Mobile 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 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 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. 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 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 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. If all the cached paths are invalid then the observer must rebuild new paths.

14 Motion of the Components Mobile Observer Mobile Observer Mobile Sensors Mobile Sensors Mobile Phenomena 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 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 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 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 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. 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 A typical example of this paradigm is sensors deployed for animal detection In this case the infrastructure level communication should be event-driven 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 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 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. 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 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. The accuracy is a function of the sensing technology of the sensors and their distance from the phenomenon.

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

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 1. The sensors communicate to establish a communication network 2. Divide the task of mapping and monitoring the terrain amongst themselves in an energy efficient manner 3. Adapt their overall sensing accuracy to the remaining total resources 4. Re-organize upon sensor failure.

23 Applications: Environmental Analysis

24 Applications: Contaminant Flow Monitoring

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

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? (  ) What is the temperature at sensor #27? (  ) Where are nodes whose temperatures recently exceeded 30 degrees? ( ) Where are nodes whose temperatures recently exceeded 30 degrees? ( ) Applications focus on the data generated by sensors Applications focus on the data generated by sensors This approach decouples data from the sensor that produced it 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. 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.

Localized Clustering Algorithm cluster Cluster-head Clustering: efficient coordination

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 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 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. 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, parent ID, remaining energy}

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

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  GOGO

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 L1 L0 Cluster-head New advertisement

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

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

Base-line Estimation

41 Advantages of Cluster-based Approach Sensor algorithms only use local information. Sensor algorithms only use local information. –generally lower energy consumption in comparison to global communication. Robust to link or node failures and network partitions 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 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 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. Non-optimal under certain terrain conditions.

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

Adaptive Fidelity Algorithms A Y Z 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.

Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks Source:

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 communication paradigm A new data dissemination paradigm A new data dissemination paradigm

49 Properties Directed diffusion is data-centric in that all communication is for named data (not nodes id). Directed diffusion is data-centric in that all communication is for named data (not nodes id). All nodes are application-aware 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? How many pedestrians do you observe in the geographical region X? Tell me in what direction that vehicle in region Y is moving. Tell me in what direction that vehicle in region Y is moving.

51 Examples Cont’d Sensors within the specified region start collecting information Sensors within the specified region start collecting information Once individual nodes detect object movements, they might collaborate with neighboring nodes to disambiguate object location or movement direction Once individual nodes detect object movements, they might collaborate with neighboring nodes to disambiguate object location or movement direction One of these nodes might then report the result back to the human operator. One of these nodes might then report the result back to the human operator.

52 Basic Scenario  A data consumer express interest in data with certain attributes  The interest will be propagated towards producers using only local interactions  The interest propagation will leave traces or gradients in the network along the way  The producers will send data back to the consumers using the established gradients  Reinforcement will be used to reduce the number of data delivery path for efficient distribution

Implementation Human operator’s query Transformed into interest Diffused into region X and Y Receives interest Activate sensors to collect information Query: “Every I ms for the next T seconds, send me a location estimate of any four-legged animal in subregion R” Descriptions: sensor’s location, intensity of the signal, degree of confidence in its estimation

54 Directed diffusion Data is named using attribute-value pairs Data is named using attribute-value pairs A sensing task is disseminated throughout the sensor network as an interest for named data A sensing task is disseminated throughout the sensor network as an interest for named data The dissemination sets up gradients within the network designed to “draw” events (i.e., data matching the interest) The dissemination sets up gradients within the network designed to “draw” events (i.e., data matching the interest) Events start flowing towards the originators of interests along multiple paths Events start flowing towards the originators of interests along multiple paths The sensor network reinforce one, or small number of these paths The sensor network reinforce one, or small number of these paths

55 A simplified schematic for directed diffusion interestgradient reinforcement

56 Issues Naming Naming Interests and Gradients Interests and Gradients Data Propagation Data Propagation Reinforcement Reinforcement

57 Naming In directed diffusion, task descriptions are named by, for example, a list of attribute-value that describe a task.

58 The Animal Tracking Task type = four-legged animal //detect animal location interval = 20 ms // send back events every 20 ms duration = 10 seconds // … for the next 10 seconds Rect = [-100, 100, 200, 400] // from sensors within rectangle // from sensors within rectangle  The task description specifies an interest for data matching the attribute.

59 A Sensor that detects an animal might generate the following data: type = four-legged animal// type of animal seen instant = elephant// instant of this type location = [125, 220]// node location Intensity = 0.6// signal amplitude measure Confidence = 0.85// confidence in the match Timestamp = 01:20:40// event generation time

60 Interests and Gradients An interest is usually injected into the network at some (possibly arbitrary) node in the network An interest is usually injected into the network at some (possibly arbitrary) node in the network We use the term sink to denote this node We use the term sink to denote this node The task state is purged from the node after the time indicated by the duration attribute The task state is purged from the node after the time indicated by the duration attribute Interests

61 How Interests are Diffused Through the Sensor Network? For each active task, the sink periodically broadcasts an interest message to each of its neighbors For each active task, the sink periodically broadcasts an interest message to each of its neighbors Interest is periodically refreshed by the sink Interest is periodically refreshed by the sink –The refresh rate is a protocol design parameter that trades off overhead for increased robustness to lost interests.

62 Interests - Cont’d The initial interest takes the following form: The initial interest takes the following form: type = four-legged animal interval = 1s rect = [-100, 100, 200, 400] Timestamp = 01:20:40// hh:mm:ss expiresAt = 01:30:40

63 How interests are processed?  Every node maintains an interest cache  When a node receives an interest, it checks to see if the interest exists in the cache  If no matching entry exists, the node creates an interest entry  This entry has a single gradient towards the neighbor from which the interest was received, with the specified event data rate

64 How interests are processed?  When a gradient expires, it is removed from its interest entry  When all gradients for an interest entry have expired, the interest entry itself is removed from a cache  All received interests are re-sent. A node may suppress a received interest if it recently re-sent a matching interest.

65 Interests and Gradients Gradients Every pair of neighboring nodes establishes a gradient towards each other

66 Gradients – Cont’d When a node receives an interest from its neighbor, it has no way of knowing whether that interest was in response to one it sent out earlier, or is an identical interest from another sink on the “other side” of that neighbor When a node receives an interest from its neighbor, it has no way of knowing whether that interest was in response to one it sent out earlier, or is an identical interest from another sink on the “other side” of that neighbor Such two-way gradients can cause a node to receive one copy of low data rate events from each of its neighbors Such two-way gradients can cause a node to receive one copy of low data rate events from each of its neighbors This technique can enable fast recovery from field paths or reinforcement of empirically better paths This technique can enable fast recovery from field paths or reinforcement of empirically better paths A gradient specifies both a data rate and a direction in which to send events. A gradient specifies both a data rate and a direction in which to send events.

67 Data Propagation A sensor node that detects a target searches its interest cache for a matching interest entry A sensor node that detects a target searches its interest cache for a matching interest entry A matching entry is one whose rect encompasses the sensor location and the type of the entry matches the detected target type A matching entry is one whose rect encompasses the sensor location and the type of the entry matches the detected target type

68 Event Description The source sends to each neighbor for whom it has a gradient, an event description every second of the form: type = four-legged animal// type of animal seen instance = elephant// instance of this type location = [125, 220] // node location intensity = 0.6 // single amplitude measure confidence = 0.85// confidence in the match timestamp = 01:20:40 // local time when event was generated

69 Data Propagation – Cont’d A node that receives a data message from its neighbor attempts to find a matching interest entry in its cache A node that receives a data message from its neighbor attempts to find a matching interest entry in its cache If no matching exist, the data message is silently dropped If no matching exist, the data message is silently dropped Otherwise, the received message is added to the data cache and the message is re-sent to the node’s neighbors Otherwise, the received message is added to the data cache and the message is re-sent to the node’s neighbors

70 Data Propagation – Cont’d By examining its data cache, a node can determine the data rate of received events By examining its data cache, a node can determine the data rate of received events To re-send a received data message, a node needs to examine the matching interest entry’s gradient list To re-send a received data message, a node needs to examine the matching interest entry’s gradient list If all gradient have a data rate that is greater than or equal to the rate of incoming events, the node may simply send the received data message to the appropriate neighbors If all gradient have a data rate that is greater than or equal to the rate of incoming events, the node may simply send the received data message to the appropriate neighbors

71 Reinforcement The sink initially diffuses an interest for a low event- rate notification (1 event per second) The sink initially diffuses an interest for a low event- rate notification (1 event per second) Once sources detect a matching target, they send low- rate events, possibly long multiple paths, towards the sink Once sources detect a matching target, they send low- rate events, possibly long multiple paths, towards the sink After the sink starts receiving these low data rate events, it reinforces one particular neighbor in order to “draw down” higher quality (higher data rate) events. After the sink starts receiving these low data rate events, it reinforces one particular neighbor in order to “draw down” higher quality (higher data rate) events.

72 Reinforcement – Example Reinforce any neighbor from which a node receives previously unseen event Reinforce any neighbor from which a node receives previously unseen event To reinforce this neighbor, the sink re-sends the original interest message but with a smaller interval (higher data rate) To reinforce this neighbor, the sink re-sends the original interest message but with a smaller interval (higher data rate)

73 Example – cont’d Type = four-legged animal Interval = 10ms Rect = [-100, 200, 200, 400] Timestamp = 01:22:35 expireAt = 01:30:40  When the neighboring node receives this interest, it will reinforce at least one neighbor.

74 Reinforcement – cont’d  The local rule we described, then, selects an empirically low delay path  Whenever one path delivers an event faster than others, the sink attempt to use this path to draw down high quality data

Basic Directed Diffusion Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested Source:

Basic Directed Diffusion Source Sink Sending data and Reinforcing the best path Low rate eventReinforcement = Increased interest Source:

Directed Diffusion and Dynamics Recovering from node failure Source Sink Low rate event High rate event Reinforcement Source:

Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event Source:

79 Conclusion We have described directed diffusion paradigm for designing distributed sensing algorithm We have described directed diffusion paradigm for designing distributed sensing algorithm Directed diffusion has the potential for significant energy efficiency Directed diffusion has the potential for significant energy efficiency Directed diffusion are stable under the ranges of network dynamics Directed diffusion are stable under the ranges of network dynamics Directed diffusion has some novel features Directed diffusion has some novel features –Data-centric dissemination –Reinforcement-based adaptation to the empirically best path –In-network data aggregation and caching

80 Thanks