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

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