1. Routing in Sensor Networks: Directed Diffusion and other proposals Presented By Romit Roy Choudhury & Pradeep Kyasanur Class Presentation - CS 598ig

2. 2 Sensor Networking – Why ??  Monitoring activities – A basic need  How many people cross Green St. every day?  How much poisenous gas in the atmosphere?  How many enemy tanks crossed through the jungle?  Human monitoring possible/feasible ?  Not always  Automated smart montoring required  Network small computing elements to achieve this

3. 3 San Fransisco’s Moscone Center equipped with sensor network

4. 4 AdHoc and Sensors …  Ad Hoc network lacking killer applications  Difficult to force co-operation among HUMAN users  Mobility/connectivity unreliable for a business model  Difficult to bootstrap – critical mass required  Sensor networks more realizable  More defined applications  Single owner/administration – easier to implement  Sensing already an established process – just add networking to it.

5. 5 However …  Ad Hoc and Sensor Networks are both multi- hop wireless architectures  Thereby shares several technical issues and challenges  Solutions in one domain often applicable to others.  However, key differences exist  Energy constraint in sensor networks  Traffic models and characteristics  Other issues like coverage, fault-tolerance, etc.

6. 6 This Talk …  Directed Diffusion  Focusing on the shift from the ad hoc paradigm  The attention to energy conservation  Other routing proposals  SPIN, LEACH, Rumor Routing, etc.  Energy Efficient disaster recovery  Focusing on an application of adhoc/sensor network  Quick note on other issues in sensor networking  Coverage, Fault-toerance, synch, aggregation, disseminations

7. 7 Directed Diffusion

8. 8 The Problem  A region requires event- monitoring (harmful gas, vehicle motion, seismic vibration, temperature, etc.)  Deploy sensors forming a distributed network  On event, sensed and/or processed information delivered to the inquiring destination Event Sensor sources Sensor sink Directed Diffusion A sensor field

9. 9 The Proposal  Proposes an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination  Challenges:  Scalability  Energy efficiency  Robustness / Fault tolerance in outdoor areas  Efficient routing (multiple source destination pairs)

10. 10 Directed Diffusion  Typical IP based networks  Requires unique host ID addressing  Application is end-to-end, routers unaware  Directed diffusion – uses publish/subscribe  Inquirer expresses an interest, I, using attribute values  Sensor sources that can service I, reply with data

11. 11 Data Naming  Expressing an Interest  Using attribute-value pairs  E.g.,  Other interest-expressing schemes possible  E.g., hierarchical (different problem) Type = Wheeled vehicle// detect vehicle location Interval = 20 ms// send events every 20ms Duration = 10 s// Send for next 10 s Field = [x1, y1, x2, y2]// from sensors in this area

12. 12 Gradient Set Up  Inquirer (sink) broadcasts exploratory interest, i1  Intended to discover routes between source and sink  Neighbors update interest-cache and forwards i1  Gradient for i1 set up to upstream neighbor  No source routes  Gradient – a weighted reverse link  Low gradient  Few packets per unit time needed

13. 13 Low Exploratory Gradient Event Low Exploratory Request Gradient Bidirectional gradients established on all links through flooding

14. 14 Event-data propagation  Event e1 occurs, matches i1 in sensor cache  e1 identified based on waveform pattern matching  Interest reply diffused down gradient (unicast)  Diffusion initially exploratory (low packet-rate)  Cache filters suppress previously seen data  Problem of bidirectional gradient avoided

15. 15 Reinforcement  From exploratory gradients, reinforce optimal path for high-rate data download  Unicast  By requesting higher-rate-i1 on the optimal path  Exploratory gradients still exist – useful for faults Event Sink A sensor field Reinforced gradient

16. 16 Path Failure / Recovery  Link failure detected by reduced rate, data loss  Choose next best link (i.e., compare links based on infrequent exploratory downloads)  Negatively reinforce lossy link  Either send i1 with base (exploratory) data rate  Or, allow neighbor’s cache to expire over time Event Sink Src A C B M D Link A-M lossy A reinforces B B reinforces C … D need not A (–) reinforces M M (–) reinforces D

17. 17  M gets same data from both D and P, but P always delivers late due to looping  M negatively-reinforces (nr) P, P nr Q, Q nr M  Loop {M  Q  P} eliminated  Conservative nr useful for fault resilience Loop Elimination A QP DM

18. 18 Simulation Setup & Metrics  ns2, 50 nodes in 160x160 sqm., range 40m  Node density maintained, 802.11 MAC  Random 5 sources in 70x70, random 5 sinks  Average Dissipated Energy  Per node energy dissipation / # events seen by sinks  Average Delay  Latency of event transmission to reception at sink  Distinct event delivery ratio  Ratio of # events sent to # events received by sink

19. 19 Average Dissipated Energy In-network aggregation reduces DD redundancy  Flooding poor because of multiple paths from source to sink flooding Diffusion Multicast

20. 20 Delay DD finds least delay paths, as OM – encouraging  Flooding incurs latency due to high MAC contention, collision flooding Diffusion Multicast

21. 21 Delivery ratio degrades with higher % node failures  Graceful degradation indicates efficient negative reinforcement Event Delivery Ratio under node failures 0 % 10% 20%

22. 22 Conclusion  Directed diffusion, a paradigm proposed for event monitoring sensor networks  Energy efficiency achievable  Diffusion mechanism resilient to fault tolerance  Conservative negative reinforcements proves useful  A careful MAC protocol, designed for such specifics, can yield further performance gains

23. 23 Contribution  Application-awareness – a beneficial tradeoff  Data aggregation can improve energy efficiency  Better bandwidth utilization  Network addressing is data centric  Probably correct approach for sensor type applications  Notion of gradient (exploratory and reinforced)  Flexible architecture – enables configuration based on application requirements, tradeoffs  Implementation on Berkley motes  Network API, Filter API

24. 24 Critique  Choice of path does not maximize aggregation  Least delay path does not  max aggregation  Exploratory paths improve fault tolerance  But at the cost of additional msg./energy overhead  Overhead analysis omits the exploratory paths  Data overlap can be suppressed  2 sources, reporting overlapping data can be combined  Idle energy = 10% of receive, 5% of transmit  Explains the poor energy performance of flooding  Not realistic numbers – optimistic assumption

25. 25 Rumor Routing LEACH SPIN Some other proposals for sensor routing

26. 26 Rumor Routing

27. 27 LEACH  Proposes clustering of sensors + cluster leaders  Can aggregate data in single (local) cluster  Rotating cluster head balances energy consumption  Cluster formation distributed and energy efficient Cluster-head always awake Member nodes can sleep when not Txing

28. 28 LEACH – The Protocol  Time is divided into rounds  A node self-elects itself as the cluster head  Higher residual energy, higher probability to be head  Close-by sensors join this cluster-head  Cluster head does TDMA scheduling and gathers data  Gathered data compressed based on spatial correlation  Transmits data to Base Station (@ higher power)  In the next round, another cluster head elected  Probabilistic load balancing  Network lifetime can increase manifolds

29. 29 SPIN: Information Via Negotiation  Flooding  many sensors transmit same data  Redundant  Make sensors disseminate spatially/temporally disjoint data sets  Name data with meta-data to define space/time property  Sensors compare overheard data with self-sensed data  Combine data to minimize overlap  Make sensors resource-adaptive  When low battery  perform minimum activities

30. 30 The SPIN 3-Step Protocol B A ADV REQ DATA ADV REQ DATA

31. 31 The SPIN 3-Step Protocol B A DATA Notice the color of the data packets sent by node B

32. 32 The SPIN 3-Step Protocol B A DATA SPIN effective when DATA sizes are large : REQ, ADV overhead gets amortized

33. 33 Energy Efficient Routing in Ad Hoc Disaster Recovery Networks: An Application Perspective

34. 34 Motivation  Disaster recovery – emerging application for adhoc/sensor networks  During Sep 11 attacks – survivors were detected through mobile phone signals  People often buried below earthquake disaster  New RFID or smart badge technologies  Each person wears a badge that is a transceiver  Sends out very low rate signals about human location  Information collected at peripheral central stations

35. 35 Problem  Given some pkt generation rate at each badge  Design routing strategy that maximizes network lifetime  Problem formulated as a LPP  Maximize minimum lifetime subject to the flow constraints on each node Subject to the capacity constraints of the links

36. 36 Approach  Existing simplex techniques can be used to solve the problem  Computation intensive due to several iterations for convergence  Paper proposes binary search on network lifetime  In plain words, a network lifetime (T) is chosen and applied to see if there exists a feasible flow assignment  If not, (T/2) is tried, else (2T) … until convergence

37. 37 Summary  Complexity of O(n 3 logT)  n 3 for finding a feasible assignment of flows  Log T for the binary search  However, distributed version of this protocol  Only available for a single origin node  For multiple badges  future work

38. 38 Other Research Challenges in Sensors  Coverage  Union of all sensing ranges need to cover entire region  Time synchronization  Data Aggregation  Calculating functions over a spatial distribution of sensors  Data Dissemination  Rumour routing, Ant colonies, swarm intelligence  Motion tracking, object guiding  Sensors + Actuators  mobile robots !!!

39. 39 Thank You

40. 40 Message Complexity Grid topology N = 25 n = 5 Sources m = 3 sinks Nodes talk with Adj. or diagonal nodes Flooding: Unrestricted broadcast Each interest broadcast by each node  nN messages A msg received twice over a link  total # receptions = 2n (# of links) Total msg. cost = nN + 4n(  N – 1)(2  N – 1) = O( nN )

41. 41 Message Complexity II Omniscient Multicast: Multicast trees rooted at each source (Cost of tree establishment not counted.) Overhead of 2 receptions on each link of tree, T j Total msg. cost = 2 |{distinct links l: l  U j = 1 to n (T j )}| Expressing all trees in terms of a common tree, T 1, we get Message Complexity = O(n  N), asymptotically, and m «  N Directed Diffusion: Similar approach using rooted trees Message Complexity = O(n  N), asymptotically, and m «  N But, cost lower than OM, cause DD can perform duplicate suppression on common link. More gain when more sources