1. Scalable Coordination Algorithms for Deeply Distributed Systems
PIs:
Deborah Estrin (UCLA and USC-ISI)
John Heidemann (USC-ISI)
Ramesh Govindan (USC-ISI)
Technical staff:
Fabio Silva (USC-ISI)
Students (SCADDS USC-ISI, and UCLA):
Alberto Cerpa, Jeremy Elson, Deepak Ganesan, Lewis Girod, Chalermak Intanagowat, Ya Xu, Yan Yu, Jerry Zhao
11/18/2018

2. Outline Diffusion Testbed measurements (Silva, Intanago)
In network processing:
Nested Queries (Silva, Intanago)
Aggregation (Intanago)
Tracking (Ganesan, Work in progress)
Scaling mechanisms
GEAR (Yu) and GAF (Xu) routing
TinyDiffusion (Ganesan)
Tiered testbed update
PC-104+, UCB Motes with TinyOS, Tags
MAC (Ye)
Plans for Q2-3 ’01
11/18/2018

3. Experiments on our PC104 testbed
Initial experimental measurements of diffusion (e.g., for comparison with simulation)
Compare bytes sent by diffusion with and without aggregation (simple in network processing)
Measurement Setup
A 5-hop network of 14 nodes on 2 ISI floors (testbed is actually 30 nodes and growing)
Radio: 13kbps radiometrix
1 sink and 1-4 sources (each source sends 112 bytes every 6 seconds)
11/18/2018

4. Experimental Results
Bytes sent by diffusion per event vs. Number of sources
Diffusion without suppression
Diffusion with suppression
11/18/2018

5. Comparison to Simulation
Bytes sent by diffusion per event vs. Number of sources
Diffusion without suppression
Diffusion with suppression
11/18/2018

6. Differences between Simulations and Experiments
MAC differences
Modified for simulations to represent hybrid TDMA-Contention
Radiometrix MAC for experiments
Channel differences
No obstacles used in ns-2 simulations
Note: we have added ability to include simple “terrain” but didn’t try to replicate indoor exp terrain in sims
More packet losses and collisions in experiments
Collisions in experiments act as unintentional suppression (make no suppression look better than it will with better mac)
11/18/2018

7. In network processing: Nested Queries
Edge processing overwhelms power and bandwidth consumption
Nested queries where low-energy sensors trigger high-energy sensors
Edge Processing
Nested Queries with In-network Processing
11/18/2018

8. Experimental Validation: Testbed Measurements
Higher delivery ratio for nested query indicates that localizing data traffic benefits performance.
% Audio Events Successfully Delivered vs. Number of light sensors
1-level query
Nested query
11/18/2018

9. Reinforced Aggregation
Promote In-network Data Aggregation near the Sources for Better Energy Savings
Two Approaches for Reinforced Aggregation
Greedy Tree Approach
Incremental approach -- Adds minimum number of links on the existing tree
Iterative Approach
Selects aggregation points such that energy dissipation for delivering aggregated data is approximately minimized
11/18/2018

10. Greedy Tree Approach (incremental)
Each node enumerates additional cost for supplying additional data samples of the same data type for previously reinforced path (on-tree)
On-tree nodes don’t increment cost for additional data samples
Sink node selects path for particular data samples based on cost advertised on the existing tree, and that advertised on other (possibly shorter) paths
Advertised cost along existing tree reflects sharing
Each node maintains message cache [message:energy:last hop]
Source 2
Source 1 B
d2*:1:B
d2:1:A
d1:0:B A D
d2*:1:B
d2:1:A
d1:1:B C
d2*:1:D
d2:2:C
d1:2:D E
Sink
11/18/2018

11. Iterative Approach Each node advertises cost for each data sample
Each node also advertises cost for each aggregate (multiple data samples belonging to same data type)
Sink reinforces aggregate with minimum advertised energy cost
Each node maintains message cache [message:energy:last hop]
Source 2
Source 1
d2:1:A B A
d1:0:B
d1&2:1:B D
d2:1:A C
d1:1:B
d1&2:2:D
d2:2:C E
d1:2:D
Sink
d1&2:3:D
11/18/2018

12. Planned: Tracking-based in network processing
Work in progress on other “primitives” such as tracking (example motivated by Xerox and U Wisc)
Edge processing:
Node A with detection subscribes to other nodes that it (A) believes might “see” tracked object and contribute most to location/tracking
In network processing:
Node A with detection sends out interest containing attributes and function that characterizes locations/nodes that might “see” tracked object and contribute most to location/tracking
11/18/2018

13. Scaling Mechanisms Flooding of interests: Exploiting redundancy:
Geographic and Energy informed routing of interest messages
Exploiting redundancy:
Geographic Adaptive fidelity applied to topology used for flooding interests
Optimizations for large numbers of listeners:
Pushed data (e.g., needed by Univ Wisc API) discussed in Integration meeting (see John H. slides)
Optimizations for much smaller/constrained nodes
11/18/2018

14. Geographical and Energy Aware Routing (GEAR)
Motivation:
Reduce overhead of interest and low rate data flooding in directed diffusion
Basic ideas:
Leverage geographical information to restrict flooding, and recursively disseminate data inside the target region.
Extend overall network lifetime using local techniques to balance energy usage
Reuse routing information across multiple user queries.
Extension of GPSR, LAR, other geographic routing
11/18/2018

15. GEAR Forward the packets towards the target region:
Greedy mode
minimizing cost function (f=mix function of distance and energy)
Route around “communication holes” with energy aware neighbor estimation
Disseminate the packet within the target region:
Geographic Recursive Forwarding
recursively re-send packets to sub-regions of the original geographic region
Restricted Flooding
apply in low density case.
11/18/2018

16. Simulation results: multiple traffic pairs # packets delivered before network partition vs. # nodes
GEAR
GEAR Geo-only
11/18/2018

17. Simulation results: multiple traffic pairs # connected pairs broken down per received data packet vs. # nodes
GEAR
GEAR Geo-only
11/18/2018

18. GEAR Plans Prototype Implementation on our testbed in progress (Yu)
Planned experimentation w/CSIP support
Desired data is characterized by geographic attributes
Xerox and U. Wisc as users/collaborators
Planned addition of data-dissemination-cost attribute
Support CSIP “informed” decision re. data contribution (to task) vs. dissemination cost
11/18/2018

19. TinyDiffusion
Implementation of Diffusion on resource constrained UCB motes
8bit CPU, 8K program memory, 512 bytes data memory
Subset of full system
retains only gradients, and condenses attributes to a single tag.
Entire System runs for less than 5.5 KB memory
TinyOS adds ~3.5K and 144 bytes of data. (incl. support for Radio and Photo Sensor)
Diffusion adds ~2K code and 110 bytes of data to TinyOS.
11/18/2018

20. TinyDiffusion Functionality
Resource Constraints
Limited cache size: currently 10 entries of 2bytes each
Limited ability to support multiple traffic streams. Currently supports 5 concurrently active gradients.
Tiered Deployment
PC104s running diffusion interface with mote clusters using TinyDiffusion.
Motes enable dense sensor deployment but can support limited in-network processing
Logical Header format of TinyDiffusion is compatible with the Diffusion header.
11/18/2018

21. Gateway Architecture PC104 TINYOS Mote-NIC LINUX MOTE
TinyDiffusion
Photo
Data Source
Data Sink
MOTE
ATMEL MHz MCU
8K program memory
512 Bytes Data Memory
RFM Radio 900 MHz
PC104
Mote-NIC
Device
Driver
LINUX
DIFFUSION
Query
Data Sink
Acoustic
Data Source
MOTE
TINYOS
Transceiver
RFM
PC104
AMD Elan™SC400
66MHz CPU
16MB RAM
Form Factor: 3.6"  x  3.8"  x  0.6"
Serial
11/18/2018

22. Tiered Testbed PC-104+(linux) with MoteNIC Tags, Sensor Card
UCB Motes w/TinyOS
Yet to come: SmartDust (highly specialized nodes)
PC/104
Tag
11/18/2018
UCB Mote

23. “Shoebox Testbed v2” Featuring: PC-104+ w/ Pentium 266 Mote-NIC
Ethernet for debugging and measurement
Linux w/glibc 2.1.3
Plastic
shoeboxes
from local drugstore
11/18/2018

24. Sensor Card
The sensor card is a small (2”x4”) microcontroller board with several on-board sensors and emitters
Microphone
Light sensor
Accelerometer
Designed to perform simple sensing tasks at low power.
Currently it is connected to the PC-104 platform by serial.
Data is preprocessed on the sensor board and fed back to the PC-104 for analysis and communication.
The next version of the PC-104 platform will have the capability to be awakened by a peripheral such as the sensor card.
11/18/2018

25. Plans Q2-4 ‘01 Diffusion Experimentation: In Network Processing
larger scale experiments and tuning
port to WINSng 2.0 platform
TinyDiffusion experiments and interoperate with Diffusion
In Network Processing
Develop primitives for tracking
Implement in network aggregation
Scaling enhancements
Geographic/Energy Adaptive Routing in Diffusion 3
Adaptive fidelity experiments (applied to interest flooding)
Data Push (Univ. Wisc. API)
Bulk transfer capability (e.g. for mobile code, larger sensor data)
SenseIT experimentation support
November Demo participation
Emulation of diffusion over wired networks for debugging
11/18/2018

26. Related (other funding) Projects
Active cooperative localization
for sensor network self configuration when no/subset GPS
ASCENT
Self-configuring topology for densely deployed networks
Adaptive beacon placement/activation
For proximity based localization
Computation primitives and constructs
Beyond nested queries (w/ Culler)
Application projects:
Habitat monitoring (Biocomplexity mapping)
Ecophysiology (w/ Culler, Pister, Rundel)
11/18/2018

27. Publications/Submissions http://www.isi.edu/scadds
Mobicom submissions (adaptive fidelity, multipath, adaptive beacon placement)
SOSP submission (naming-based architecture)
IROS (Robotics) submission (localization)
ICDCS (address-free, beacon placement)
IPDPS (time synch)
Sigcomm submission (self-config topology experiments)
11/18/2018