1. Deborah Estrin, Ramesh Govindan, John Heidemann USC/ISI and UCLA SCADDS Staff and Students: Jeremy Elson, Deepak Ganesan, Chalermek Intanagonwiwat, Fabio Silva, Jerry Zhao For more information: http:/www.isi.edu/scadds SCADDS: Research Update October 2000

2. Research Update –Directed diffusion studies Update Aggregation Multipath –Systems contributions API and implementation for Diffusion and SenseIT routing Address free fragmentation –Experimental platform and experience PC-104s Instrumentation/debug support! –Plans and related projects Aggregation and multipath simulations and implementations Adaptive fidelity evaluations Related projects: Localization, Time synchronization, Tags, Tiered architecture

3. SENSIT PI-MTG October 003 PART I: Algorithm/Protocol/Diffusion Studies Diffusion recap Aggregation Multipath

4. SENSIT PI-MTG October 004 Diffusion-Recap Directed diffusion –Can provide significantly longer network lifetimes than existing schemes –Keys to achieving this: In-network aggregation Empirical adaptation to path Average Dissipated Energy (Joules/Node/Received Event) Network Size (nodes) 0 0.005 0.01 0.015 0.02 0.025 0.03 050100150200250 300 Diffusion without suppression flooding Diffusion with suppression Omniscient multicast

5. 5 Latency in Data Diffusion Compare latency with: flooding: large amount of traffic causes delay omniscient multicast: theoretical centralized optimum (unrealizable in practice) data diffusion without suppression data diffusion with suppression Diffusion’s empirical adaptation and in-network processing (suppression) achieves latency as low as optimum (o. multicast). Delay (Seconds) Network Size (nodes) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 050100150200250300 Diffusion without suppression flooding Diffusion w/suppressiono. multicast

6. SENSIT PI-MTG October 006 Diffusion Status Preliminary simulation results were presented in Mobicom 2000 (and April00 PI meeting) Diffusion version 1 integrated into current ns snapshot and released to research community A simple TDMA MAC is implemented in ns for better simulations of sensor radio –Tracking other researchers group TDMA work for future incorporation (e.g., Srivastava et. al.)

7. SENSIT PI-MTG October 007 Diffusion Work in Progress Aggregation mechanisms for energy savings Multipath

8. 8 Aggregation Opportunistic and greedy aggregation Distributed aggregation points automatically and locally selected such that they are close to sources Opportunistic: aggregation on existing tree Greedy: use reinforcement to increase aggregation closer to sources..favoring energy reduction over latency

9. 9 Simplified Problem Statement Where should network aggregate ? –B, C, D, E, or F? If aggregation reduces size only slightly –F is acceptable, “shortest path tree” –“opportunistic aggregation” minimizes latency to sink If aggregation reduces size significantly –D is preferred (closer to A), “greedy(ier) tree” –Conserved energy compared to F –May increase A to F latency Data Source 1 Sink New Data Source 2 A B C D E F

10. SENSIT PI-MTG October 0010 Simplified Problem (Continued) Naïve local-rules may not work –If local rule always favors aggregated data paths, B may be selected as aggregation point— inefficient and higher latency Data Source 1 Sink New Data Source 2 A B C D E F

11. 11 Desired Aggregation Behavior [x1,y1,SNR1] [x2,y2,SNR2] Sink Gradient Low rate data Reinforcement A sample local reinforcement rule to provide “greedy(ier)” tree –A, already getting source [x1,y1] data at high rate from neighbor B –A receives [x2,y2] aggregatable data from neighbor C –A decides whether to aggregate at A or let B (upstream neighbor) aggregate –if (DelayViaB-DelayViaC < d), A reinforces B, else reinforces C - d is an adjustable parameter B C A

12. SENSIT PI-MTG October 0012 Desired Aggregation Behavior [x1,y1,SNR1] [x2,y2,SNR2] Sink Gradient Low rate data Reinforcement A sample local reinforcement rule for new data [x2, y2, SNR2] –if A sees ( delay(B)-delay(C) < d) then A reinforces B, else reinforces C –B is an upstream neighbor that has a high-rate gradient toward A for data that is aggregatable with new data [x2, y2, SNR2] - d is an adjustable parameter B C A

13. SENSIT PI-MTG October 0013 Challenges Some aggregation/processing problems are more challenging than others Future work: –“Bounding box” applications as initial target –More general applications will require additional mechanism identify classes of problems for which opportunistic aggregation does not produce imprecise or incorrect results establish error bounds for class of problems for which opportunistic aggregation produces imprecise results

14. SENSIT PI-MTG October 0014 Multipath for Low-Latency Robustness in Lossy Networks In the same design space as FEC and spread spectrum approaches to minimize losses and latency due to disturbances in the network Use local rules for redundancy in lossy regions to achieve higher likelihood of delivery. Local metrics for Path selection –Latency –Loss –Energy Shaded regions correspond to regions of high losses. Darker shades correspond to greater losses

15. 15 Braided Multipath Disjoint Paths –Stringent restriction –Allow end-to-end decisions only –Unsuitable for broadcast model Braided paths –enable distributed decision making –Offers greater flexibility to route around losses –May offer greater robustness for same energy constraints –May be better suited for changing losses in the network. Alternate path (higher latency) Braided multi-path

16. SENSIT PI-MTG October 0016 Exploring Multipath Exploring tradeoff between choosing higher latency path that avoids regions of high losses vs sending redundant packets through lossy regions Exploring Localized mechanisms for low-energy notifications –Piggybacking on data packets –Nodes use notifications to trigger multipath explorations Tradeoff-increased latency

17. SENSIT PI-MTG October 0017 Adaptive Fidelity extend system lifetime while maintaining accuracy approach: –estimate node density needed for desired quality –automatically adapt to variations in current density due to uneven deployment or node failure –assumes dense initial deployment or additional node deployment zzz

18. SENSIT PI-MTG October 0018 Adaptive Fidelity Status applications: –maintain consistent latency or bandwidth in multihop communication –maintain consistent sensor vigilance status: –probablistic neighborhood estimation for ad hoc routing 30-55% longer lifetime with 2-6sec higher initial delay –currently underway: location-aware neighborhood estimation

19. SENSIT PI-MTG October 0019 Part II: System Developments API for Diffusion/Network Routing Using Random Identifiers

20. SENSIT PI-MTG October 0020 Integration Participation Coordinated integration effort –BAE (Signal Processing) – ISI-W (Diffusion Routing) – Penn State (CSP) Included 4 SensIT nodes along the road –Local detection of vehicles –Messages exchanged via Diffusion

21. SENSIT PI-MTG October 0021 Diffusion Routing Implementation Two implementations: –WinCE (WINS NG 1.0 Nodes) – PC104s + Radiometrix Radios or Wired Main development platform Easily portable to QNX Develop various in-house applications Evaluate implementation Gain experience with API

22. SENSIT PI-MTG October 0022 Diffusion Routing API Objective: Improve current Network Routing API to better match distributed applications needs Solution: Allow more control over routing decisions and packet forwarding –Support in-network processing and aggregation with flexible application interface Diffusion App 1App 2

23. SENSIT PI-MTG October 0023 Future Directions TDMA Release updated network routing API after gaining experience with in- house experiments

24. Random Transaction Identifiers Maximize usefulness of every bit –each bit transmitted reduces net lifetime –can’t amortize large headers or claim-collide overhead for low data rates + high dynamics Still need to identify transmitter –Reinforcements, Fragmentation Use small, random transaction identifiers (locally selected…like multicast addresses) –Treat identifier collisions as any other loss Address-free method wins in networks with locality –simultaneous transactions at any one point is much less than in network as a whole

25. SENSIT PI-MTG October 0025 AFF Allows us to optimize # bits used for identifiers Fewer bits = fewer wasted bits per data bit, but high collision rate; vs. More bits = less waste due to ID collisions but many bits wasted on headers Example: A model of address-free fragmentation (16 bit data)

26. SENSIT PI-MTG October 0026 Testbed Validation of AFF Collision Model: 5 Transmitters and 1 Receiver

27. SENSIT PI-MTG October 0027 Part III: Experimental Infrastructure

28. SENSIT PI-MTG October 0028 Platform for experimentation with SCADDS algorithms Complementary platform to Sensoria nodes: –Not for desert-field testing ! COTS, rather than custom low- power, real-time, integrated sensor platform Can provide larger scale networking studies and flexibility via COTS Model: explore on this testbed and feedback lessons to integrated, Sensoria platform Will be much easier to move back and forth with any Unix variant (e.g., QNX) Specifications: –COTS PC104 CPU module AMD ELANSC400, 16MB RAM+16MB FlashDisk, 4 serial/1 parallel ports –Radio: 418Mhz RPC from Radiometrix Moving to RFM –OS: Slimmed Redhat 6.1. (2.2.x/Libc6)

29. SENSIT PI-MTG October 0029 Using Testbed for SCADDS Experimentation Expanded the testbed size to explore SCADDS related algorithms –Currently 30, Target 50-100 Debugging/Management Utilities –Special debug-stations with Ethernet and 8-serial- port adapters, acting as a bridge for interactive debugging from host PCs. –CVS-like Scripts to automatically update binaries when newer version is available. Iteratively improving SCADDS algorithms based on experimental feedback –E.g., per-hop filters underway since v.1 –Validating and feeding back into simulation results

30. SENSIT PI-MTG October 0030 Leveraging Tiered architecture *Photo From http://www.cs.berkeley.edu/~jhill/ Leveraging other funding to enrich SCADDS experiments Designing “Tags” under a complementary NSF grant (NSF SCOWR and ONR DURIP) –Modular architecture, reusable components Module Bus: 80pin connector: I2C, INTQ/A and GPIOs Modules: PIC based master module, sensor module, RFM based radio module. –Experiments with low power architecture Software selectable clocking –Also collaborate with UC Berkeley folks to incorporate their silver-dollar –sized “motes”. Developing a beaconing application to complement SCADDS testbed as well as an objecting tracking application.

31. SENSIT PI-MTG October 0031 Planned Work Diffusion –Aggregation simulation and implementation –Multipath simulation and implementation –Exploring power-aware and geographic routing assist –Adaptive fidelity Testbed experimentation Beyond SCADDS –Timing and coordinate synchronization –Localization (ranging and self-configuring beacon placement) –Sensor network health monitoring and debugging Other collaborators: Nirupama Bulusu, Alberto Cerpa, Lewis Girod, Satish Kumar, Yan Yu