1. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks presented by: Stoyan Paunov Authors: Intanagonwiwat, C., Govindan, G. & Estrin, D.

2. Motivation for Directed Diffusion Future advances in processor, memory and radio technology Addition of sensing capabilities to enable distributed micro-sensing Revolutionize information gathering and processing Large-scale, dynamically changing, robust sensor networks –Inhospitable physical environments Remote geographical regions Toxic urban locations –More benign, but less accessible environments Large industrial plants Aircraft interiors

3. Usage Types Human operators posing questions: –How many pedestrians do you observe in the geographical region X? –Tell me in what direction that vehicle in region Y is moving Queries result in tasking sensors in region to begin gathering info Sensor collaboration to disambiguate info Some sensor reports the result

4. Directed Diffusion Overview 1/3 Motivation –Robustness –Scalability –Energy efficiency New data dissemination paradigm, which is data-centric Named data in attribute-value pair format Data requested by sending interests Requested data is “drawn” down towards the requesting node Intermediate nodes can cache data, transform it or direct it based on the cache

5. Directed Diffusion Overview 2/3 Operator queries transformed into interests Interests are diffused toward the nodes in a specific region Upon interest reception, nodes collect data via their sensors and return it along the reverse path Intermediate nodes might perform data aggregation Important feature: –Interest and data propagation and aggregation are determined by localized interactions

6. Directed Diffusion Overview 3/3 Directed diffusion is based on hop-to-hop routing –differs from IP-style communication and ad-hoc networking Benefits –Robust multi-path delivery –Empirical adaptation to a small set of paths –Significant energy savings via data aggregation

7. Distributed Sensor Networks Expectations Node capabilities –Matchbox-sized –Battery power source –CPU clocked at several hundred Mhz –Memory amounting to sever tens of Mbytes –Radio modem employing some type of diversity coding –Energy efficient MAC layer, e.g. TDMA –Stripped down modern OS, e.g. Windows CE & µCLinux –1-many sensors, e.g. seismic geophones, infrared dipoles, electret microphones for acoustic sensing

8. Distributed Sensor Networks Expectations Node capabilities continued … –Common signal-processing capabilities offloaded to a low-power ASIC allowing main processor much needed rest –GPS capability –Low cost Possible use of supercharged sensor nodes –Deployments in the vicinity of the phenomena –Dense deployments, in possibly unplanned fashion –e.g. at busy intersections, or in the interior of large machinery –Spatial density of deployments can minimize multi-target resolution on a per- node basis

9. Today’s Grave Reality [today being Y2K] Current model –Large, complex sensor systems –Complex signal processing to separate noise from target very far away from the phenomena –Time series transmitted to base station(s) performing data filtering and reduction –Long-range transmissions Drawback of current method –Low energy efficiency –Battery powered nodes – energy efficiency is crucial Preferred method –Short-range hop-by-hop communication –Orders of magnitude of energy savings via localized data reduction –Added advantage: obstacle work-around

10. Running Example 1/2 Task-specific SN for regional remote surveillance Contact sensor field via a long-range radio link Every I ms for the next T seconds, send me a location estimate of any four-legged animal in sub- region R of the sensor field Task is conveyed to sensors on region R Gathered data matched against locally-stored library

11. Running Example 2/2 Upon a match data is propagated back –Own location –a codebook value corresponding to the animal –intensity of the signal –degree of confidence for estimation Sensor nodes may collaborate to pick best estimate Directed Diffusion design goals –Design of dissemination mechanisms for tasks and events –Multiple concurrent task initiations –Scalable for several thousand sensor nodes –Robustness to failures –Minimization of energy usage

12. Directed Diffusion Elements Data is named using attribute- value pairs Sensing tasks are disseminated in the form of interests The dissemination mechanism sets up gradients to draw events Events are flowing toward originators of interest along multiple paths One or a small number of paths are reinforced The following discussion is in the context of location tracking

13. Naming Task description - interest –species an interest for data matching attributes Responses take a similar form selecting a naming scheme is the rst step in designing directed difusion choice of naming scheme –can affect the expressivity of tasks –may impact performance of a diffusion algorithm.

14. Interests & Gradients 1/6 Interest injected somewhere in SN Subsequently it is diffused through the SN Task –specified type –Rectangle –Duration, e.g. 10 min –Interval, e.g. 10ms Receiver records task –Interval specifies the data rate, e.g. 10ms = 100 events/sec –Task is purged upon duration expiration

15. Interests & Gradients 2/6 Node periodically broadcasts interest message to its neighbors Initial request is exploratory with much larger interval, e.g. 1 event/sec –Idea: determine if a positive match exists, e.g. four-legged animal Interests are periodically refreshed –Idea: compensate for transmission unreliability –Tradeoff: robustness for overhead

16. Interests & Gradients 3/6 Nodes maintain interest cache (IC) consisting of distinct interests –i.e. different type, interval, (partially) disjoint region Note: no info is maintained about the sink Interest definition enables aggregation Entries in IC contain –A timestamp –Up to one-per-node gradient Gradients contain –Data rate field (derived from interest interval) –Duration field (derived from interest timestamp and expiresAt fields)

17. Interests & Gradients 4/6 Upon interest reception the node checks the cache for the existence of the interest: –No match add interest single gradient towards sink –Interest exist, but no gradient towards the sink Add a gradient towards the sink Update timestamp and duration fields accordingly –Interest exist, as well as gradient towards the sink Simply update timestamp and duration fields

18. Interests & Gradients 5/6 When a gradient expires, it is removed Note: Not all gradients will expire at the same time Interest entry removed from IC upon expiration of all gradients Upon reception of an interest a node might decide to re-send it to some subset of its neighbors This is an example of diffusion based on localized interactions Note: interest receiving node does not know where the interest originated

19. Interests & Gradients 6/6 Possible interest propagation schemes – Broadcasting = flooding due to lack of info – Geographic routing to save energy – Cache-based diffusion if SN is immobile Local interactions consequences – unknown interest originator – Pair-wise gradient establishment Fast recovery from failed paths Reinforcement of empirically better paths Persistent loops are avoided

20. Data Propagation 1/3 Upon reception of interest a node –Propagates the interest –Tasks its own sensors to collect samples Target recognition –Based on pre-sampled and classified data E.g. a four-legged animal has a different acoustic or seismic footprint than e.g. a human –Degree of confidence derived from “exactness” of the match –Intensity based on distance of signal origin

21. Data Propagation 2/3 Upon a successful match a node –Generates event samples at the highest requested event rate –Sends an event description every second to every neighbor which has a gradient in the IC Note: The exact sending mechanism depends on the radio’s MAC layer and can have significant impact on performance

22. Data Propagation 3/3 Upon reception of a data message a node –Checks its IC for an interest entry of that type If no match, message is dropped If matched, node checks its data cache (DC) –DC is used for loop prevention among other things –Received message is dropped if it matches a DC entry Added to the DC and re-sent to neighbors otherwise Re-sending rate may need to be down-converted to match IC rate Down-conversion = %-based dropping or application –specific interpolation of successive events Loop-prevention and down-conversion are a consequence of the application-oriented nature of SN

23. Reinforcement 1/5 Sink initially diffuses interest for low-rate notification Once sources detect a match, they send low-rate events towards the sink The sink then reinforces a particular neighbor based on data-driven local rules, e.g. –Higher data quality –Higher data rate –A previously unseen event To reinforce neighbor re-send interest message with smaller interval, i.e. higher data rate

24. Reinforcement 2/5 Upon reception of reinforcement neighbor –Finds the already existing gradient in its IC –Updates the data rate –Reinforces at least one neighbor To reinforce a neighbor a node –Examines its DC and follows some local rules, e.g.: –neighbor from which it first received the latest event –All neighbors from with new recently received events High data rate event path established via a sequence of local interactions

25. Reinforcement 3/5 By means of local rules an empirically low delay path is chosen Scheme is very reactive to changes in path quality –If a path delivers an event faster than others, the sink attempts to use it to draw down high quality data This approach allows for the reinforcement of multiple paths –Node may need to negatively reinforce some paths

26. Reinforcement 4/5 Negative Reinforcement Mechanisms –Based on timeout –Explicit by resending interest with low data rate Upon reception of negative reinforcement –Node degrades its data rate towards the sender –If all gradients are now low data rate, it negatively reinforces its high data rate sending neighbors Explicit negative reinforcement ensures rapid degradation at the cost of increased resource utilization Neighbor Negative Reinforcement Rules –No new events, e.g. other neighbors consistently send events first N events or time T (e.g. window of 2 seconds) –Fewer events received from that neighbor

27. Reinforcement 5/5 Multi-source scenario Multi-sink scenario –Two sinks with identical interests –Two distinct empirically sound paths Local repair –Intermediate nodes applying reinforcement rules –Link failure, e.g. battery depletion or environmental factors

28. Design Space for Diffusion

29. Diffusion Discussion Data-centric model Communication –Neighbor-to-neighbor (hop-by-hop) –Unlike end-to-end connection-oriented approaches –Each node can interpret the messages –No need for globally unique identifiers, as long as neighbors are uniquely identifiable –Loop avoidance vs looplessness constraint as in ad hoc networks –Improved energy efficiency by path minimization based on observation Coordinated sensing opportunity due to node capability to cache, aggregate and process data Aiming to achieve energy efficiency, robustness and scalability

30. Evaluation Metrics, Goals, and Methodology 1/2 Animal tracking scenario implemented in NS-2 Goals: –Performance comparison against idealized model Flooding Omniscient Multicast –Understand the impact of dynamics, i.e. node failures –Explore influence of radio MAC layer on performance –Sensitivity to the choice of parameters Metrics as a function of SN size –Average dissipated energy Ratio of per-node dissipated energy to distinct events seen at sinks –Average delay One way latency

31. Evaluation Metrics, Goals, and Methodology 2/2 Experiments in regime far from overload –No congestion –Event losses still possible, e.g. during dynamics –Another metric used is event delivery ratio Experiment set-up –Network size between 50 and 250 nodes –50 node field size of 160x160 meters –Average density of sensor nodes is kept constant –Node radio rage of 40 meters –1.6 Mbps 802.11 MAC layer (TDMA better!, so …) –NS-2 modification - Idle time power dissipation 10% of receive power & 5% of transmit power –5 sources & 5 sinks –Interests every 5 sec, duration 15 sec, negative reinforc. window 2 sec

32. Comparative Evaluation 1/2 Average Dissipated Energy

33. Comparative Evaluation 2/2 Average Delay

34. Impact of Dynamics 1/3 Average Dissipated Energy

35. Impact of Dynamics 2/3 Average Delay

36. Impact of Dynamics 3/3 Event Delivery Ratio

37. Impact of Various Factors 1/3 Negative Reinforcement

38. Impact of Various Factors 2/3 Duplicate suppression

39. Impact of Various Factors 3/3 High Idle Radio Power

40. Conclusions Directed Diffusion has the potential for significant energy efficiency –Outperforms omniscient multicast even with relatively unoptimized path selection Diffusion mechanisms are stable under the dynamics ranges considered in the paper For directed diffusion to achieve its full potential, careful attention has to be paid to the design of sensor radio MAC layers. This work is an initial investigation and a lot more work is required in the area