Data Dissemination

1. Data Dissemination Peyman Teymoori

2. Introduction • Data dissemination: the process by which queries or data routed in the network • Source: the node generating data • Event: the information to be reported • Sink: the node interested in an event • Two general steps: • Interest propagation: temperature, intrusion • Data propagation: routing, aggregation

3. Routing Models • Address Centric • Each source independently send data to sink • Data Centric • Routing nodes en-route look at data sent Source 2 Source 2 Source 1 Source 1 A B A B DC Routing AC Routing Sink Sink

4. Differences with Current Networks • Difficult to pay special attention to any individual node: • Collecting information within the specified region • Collaboration between neighbors • Sensors may be inaccessible: • embedded in physical structures. • thrown into inhospitable terrain. • Sensor networks deployed in very large ad hoc manner • No static infrastructure

5. Differences with Current Networks • They will suffer substantial changes as nodes fail: • battery exhaustion • accidents • new nodes are added. • User and environmental demands also contribute to dynamics: • Nodes move • Objects move • Data-centric and application-centric • Location aware • Time aware

6. Overall Design of Sensor Networks • One possible solution? • Internet technology coupled with ad-hoc routing mechanism • Each node has one IP address • Each node can run applications and services • Nodes establish an ad-hoc network amongst themselves when deployed • Application instances running on each node can communicate with each other

7. Why Different and Difficult? • A sensor node is not an identity (address) • Content based and data centric • Where are nodes whose temperatures will exceed more than 10 degrees for next 10 minutes? • Tell me the location of the object ( with interest specification) every 100ms for 2 minutes. • Multiple sensors collaborate to achieve one goal. • Intermediate nodes can perform data aggregation and caching in addition to routing. • where, when, how?

8. Challenges • Energy-limited nodes • Computation • Aggregate data • Suppress redundant routing information • Communication • Bandwidth-limited • Energy-intensive • Scalability: ad-hoc deployment in large scale • Robustness: unexpected sensor node failures • Dynamic changes: no a-priori knowledge, mobility Goal: Minimize energy dissipation

9. Aggregation • Many studies addressing not only the routing problem but also representing and combining data more efficiently • Process of data while being forwarded toward the sink • Reducing the number of transmissions • Definition: • Gathering & routing information in a multihop network • Processing data at intermediate nodes to • Reduce resource consumption • Increase network lifetime

10. Aggregation • Two approaches: • In-network with size reduction • More ability to reduce traffic • Less accuracy • Difficulty in reconstructing the original data • In-network without size reduction • Merging some smaller packets into one • Requires: • Networking protocol: routing • Effective aggregation functions • Data representation

11. Aggregation • A different routing paradigm is required • Data-centric routing: nodes route packets based on packet content • Taking into account: • The most suitable aggregation points • Data type • Priority of information • Timing strategies: • Periodic simple aggregation • Periodic per-hop aggregation • Periodic per-hop adjusted aggregation • depends on the node’s position in the gathering tree

12. Aggregation • Aggregation functions: • Lossy & lossless • Duplicate sensitive vs. duplicate insensitive • AVG vs. MAX • Data representation: • limited storage capabilities • vary according to the application requirements • distributed source coding: A method to deal with data representation and compression

13. Network Protocols for In-Network Aggregation • How to forward packets in order to facilitate in-network aggregation • Several approaches: • Tree-based (Hierarchical): SPTs rooted at sink, or nodes grouped into clusters • Multi-path routing: DAG, more robust • Hybrid approaches

14. (a) (a) A • Implosion • Data overlap B A B C (a) (a) D C r q s (r,s) (q,r) Flooding • Just broadcast what you receive and is not yours (consider the max hop count) • Disadvantages: • Resource blindness

15. Gossiping • A modified version of flooding • Random selection of neighbors for broadcast • Avoids implosion • Disadvantages: • Taking a long time to propagate • No delivering guarantee

16. Rumor Routing • An agent-based path creation algorithm • Agents or “ants”: • long-lived entities created at random by nodes • packets circulated to establish shortest paths to events they encounter • perform path optimization • Motivation • Sometimes a non-optimal route is satisfactory

17. Rumor Routing • Creating Paths: • Nodes having observed an event send out agents which leave routing info to the event as state in nodes • Agents attempt to travel in a straight line • If an agent crosses a path to another event, it begins to build the path to both • Agent also optimizes paths if they find shorter ones.

18. Event Source Rumor Routing • Basis for algorithm: • Observation: Two lines in a bounded rectangle have a 69% chance of intersecting • Create a set of straight line gradients from event, then send query along a random straight line from source.

19. Rumor Routing (b) (a)

20. Rumor Routing • Advantages: • Tunable best effort delivery • Tunable for a range of query/event ratios • Disadvantages: • Optimal parameters depend heavily on topology (but can be adaptively tuned) • Does not guarantee delivery

21. Sensor Protocols for Information via Negotiation (SPIN) • A data-centric routing approach • Uses negotiation & resource adaptation to address deficiencies of flooding • Two basic ideas: • Exchanging sensor data may be expensive, but exchanging data about sensor data may not be. • Nodes need to monitor and adapt to changes in their own energy resources

22. Sensor Protocols for Information via Negotiation (SPIN) • Data negotiation • Meta-data (data naming) • Application-level control • Model “ideal” data paths • SPIN messages • ADV- advertise data • REQ- request specific data • DATA- requested data • Resource management ADV A B REQ A B DATA A B

23. REQ DATA DATA DATA DATA REQ ADV REQ ADV ADV ADV DATA REQ ADV DATA ADV ADV REQ REQ Sensor Protocols for Information via Negotiation (SPIN) A B

24. Cost-Field Approach • Sets up minimum cost paths to a sink • A two-phase process: • Set up the cost field (metrics such as delay) • Data dissemination using the cost • At each node, cost = min cost to the sink • No explicit path information

25. Cost-Field Approach • Setting up the cost field: • Sink broadcasts: ADV + its cost as 0 • A node N hears an ADV from M: • Its path • Ln : total cost from node N to sink • Lm : the cost of node M to sink • Cnm : the cost from node N to M • If Ln was updated, the new cost is broadcasted (new ADV) • Flooding-based implementation of Dijkstra’s algorithm • A back-off-based approach, Time to defer = γ * Cmn • γ is a parameter of the algorithm

26. Cost-Field Approach • An example of setting up the cost field • γ = 10

27. Cost-Field Approach • Data dissemination: • A source sends a message • cost = Cs • cost-so-far = 0 • In an intermediate node M (with cost = Cm): • If cost-so-far + Cm = Cs then • Forward the packet

28. Directed Diffusion • A reactive data-centric protocol • Suitable for monitoring • Expressed in terms of named data • Organized in three phases: • Interest dissemination • Gradient setup • Path reinforcement & forwarding

29. Reply Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35 Directed Diffusion • Naming • A list of attribute – value pairs • Animal tracking: Request Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400]

30. Directed Diffusion • Interest • The sink periodically broadcasts interest messages • Every node maintains an interest cache • Each item corresponds to a distinct interest • No information about the sink • Interest aggregation : identical type, completely overlap rectangle attributes • Each entry in the cache has several fields • Timestamp: last received matching interest • Several gradients: data rate, duration, direction

31. Directed Diffusion • Setting Up Gradient Source Sink Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests Interest = Interrogation Gradient = Who is interested (data rate , duration, direction)

32. Directed Diffusion • Data propagation • Sensor node computes the highest requested event rate among all its outgoing gradients • When a node receives a data: • Find a matching interest entry in its cache • Examine the gradient list, send out data by rate • Cache keeps track of recent seen data items (loop prevention) • Data message is sent individually to the relevant neighbors (unicast)

33. Directed Diffusion • Reinforcing the best path Source The neighbor reinforces a path: 1. At least one neighbor 2. Choose the one from whom it first received the latest event (low delay) 3. Choose all neighbors from which new events were recently received Sink Low rate event Reinforcement = Increased interest

34. Directed Diffusion • The reinforced path must be periodically refreshed • A trade off based on network dynamics: • Frequency of gradient setup • Achieved performance • MAC layer issues: • Keeping local (control) traffic at a low level • Avoid collision, delay • Enhanced Directed Diffusion • Joint of Directed Diffusion & cluster-based arch.

35. Tiny AGgregation (TAG) • A tree-based data-centric approach • Timing: periodic per hop adjusted • Two main phases: • Distribution: disseminating queries • Collection: aggregating & routing readings • Declarative interface for data collection and aggregation – SQL style

36. Tiny AGgregation (TAG) • A sample query: • SELECT: an expression over one or more aggregation values • expr: the name of a single attribute • agg: aggregation function • attrs: the attributes by which the sensor readings are partitioned • WHERE, HAVING: filters out irrelevant readings • GROUP BY: specifies an attribute based partitioning of readings • EPOCH DURATION: time interval of aggr record computation SELECT {agg(expr), attrs} from SENSOR WHERE {selPreds} GROUP BY {attrs} HAVING {havingPreds} EPOCH DURATION i

37. Tiny AGgregation (TAG) • Distribution: • The sink broadcasts an organizing message • Message contains level & distance from the root • Node receiving the message: • If it doesn’t belongs to any level: • Its level = message.level + 1 • Its parent = the sender node • Rebroadcasts the message adding its own ID & level • Broadcasting the query along the structure

38. Tiny AGgregation (TAG) • Collection: • Each parent waits for data • Then sends its aggregation up the tree • Epochs are divided slots equals to the max depth of the tree – Sleep & Wake up • Every epoch, new aggregate produced • Most of the times, motes are idle and in low power state

39. 1 2 3 4 5 Tiny AGgregation (TAG) SELECT COUNT(*) FROM sensors Sensor # <- Time 1

40. 1 2 3 4 5 Tiny AGgregation (TAG) SELECT COUNT(*) FROM sensors Sensor # 2 <- Time

41. 1 2 3 4 5 Tiny AGgregation (TAG) SELECT COUNT(*) FROM sensors Sensor # 1 3 <- Time

42. 1 2 3 4 5 Tiny AGgregation (TAG) SELECT COUNT(*) FROM sensors 5 Sensor # <- Time

43. 1 2 3 4 5 Tiny AGgregation (TAG) SELECT COUNT(*) FROM sensors Sensor # <- Time 1

44. Tiny AGgregation (TAG) • A grouping example

45. Synopsis Diffusion • Synopsis Diffusion : In-network aggregation • Synopsis Functions • Synopsis Generation: s=SG(r) • Synopsis Fusion: s=SF(s1,s2) • Synopsis Evaluation: r*=SE(s) • Efficient Topology for Synopsis Diffusion • Rings (R0 R1  … Ri-1  Ri ) • Duplicate Sensitive Aggregates Mapping • DS Aggregates  Order- and duplicate-insensitive synopsis

46. Class of Aggregates Sink Not TAG Median Count Distinct Histogram Average MIN Count Synopsis Diffusion • Aggregate : A metric of aggregation

47. Synopsis Diffusion [ ]

48. Synopsis Diffusion • Phases of SD: • Distribution Phase • Aggregate query is flooded through the network • Network node form a set of rings • Aggregation Phase • Each node uses SG to convert local data to local synopsis and then uses SF to merge two synopsis to create a new one. The query initiator uses the SE to generate the final result. • Adapting the Topology • Ring Topology • Adaptive Ring Topology • Nodes moves up or down in the rings dependent upon the messages it overhears.

49. Delay Bounded Medium Access Control (DB-MAC) • A tree-based aggregation scheme • A joint design of routing & MAC protocols • Minimizes the latency for delay bounded apps. • Takes advantage of data aggregation • Adopts CSMA/CA scheme based on RTS/CTS/DATA/ACK handshake • Suitable for cases where: • Different sources sense an event • There is delay constraint

50. Delay Bounded Medium Access Control (DB-MAC) • A message exchange example in DB-MAC