Directed Diffusion in Sensor Networks: Energy-Efficient Event Monitoring and Routing Proposals

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

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. San Fransisco’s Moscone Center equipped with sensor network

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. 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. 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. Directed Diffusion

8. 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 Directed Diffusion The Problem A sensor field Event Sensor sources Sensor sink

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. 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. 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. 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 i1set up to upstream neighbor • No source routes • Gradient – a weighted reverse link • Low gradient  Few packets per unit time needed

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

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

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 Link A-M lossy A reinforces B B reinforces C … D need not A (–) reinforces M M (–) reinforces D Event D M Src A C B Sink

17. Loop Elimination • 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 P Q D M A

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

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

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

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

26. Rumor Routing

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

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

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

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

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. 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. 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. Summary • Complexity of O(n3logT) • n3 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. 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. Thank You

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. 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, Tj Total msg. cost = 2 |{distinct links l: l  Uj = 1 to n (Tj)}| Expressing all trees in terms of a common tree, T1, 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