1. Distribution & Aggregation Technologies for SensIT
4/19/2017
Distribution & Aggregation Technologies for SensIT
SensorWare and DSN Projects at UCLA

2. Two Projects: Distinct but Complementary
SensorWare (RSC subcontract, start January 2000)
Sensor Control Scripts
lightweight, mobile, platform independent, secure
target tracking application concept demonstration
Distributed Middleware Services
spatial addressing & communication (multicast, gathercast etc.)
timing synchronization, fault management services
DSN (ISI subcontract, start Oct 1999 with 1 student funding)
Network protocol stack
low-power and power-aware routing, MAC, and link protocols
protocols for GPS-synchronized communication subsystem
capability and attribute based addressing and connectivity partially leverage SensorWare spatial algorithms
Network boot-up: discovery & distribution of location, capabilities
Sensor network simulation and emulation
leveraged by SensorWare

3. Introduction to the Project Teams
SensorWare
Mani Srivastava
Miodrag Potkonjak
CS faculty, co-PI
Graduate students:
Athanassios Boulis
scripting environment
Sascha Slijepcevic
tracking with mobile scripts
Scott Zimbeck
node software
Vlasios Tsiatsis (*)
spatial addressing
DSN
Mani Srivastava
Graduate students:
Sung Park
simulation platform
Andreas Savvides
MAC & routing
Curt Schurgers
power-aware routing
Vlasios Tsiatsis (*)
attribute based routing

4. Selected Work-In-Progress, Accomplishments, and Plans

5. Sensor Control Scripts [SensorWare]
Desired capabilities
rapid development and deployment of mission-specific sensor network applications
allow transient users to access collective capabilities of sensor network in a mission-specific fashion
go beyond the simple (static) query and response
query as scripts, low latency customized tracking
Approach
scriptable, lightweight runtime system at each node
compact and platform independent sensor node control scripts
Node Object Model (sensor, SP, and communication)
scripts can migrate (download, replicate), i.e. mobile
modeled on systems with embedded runtime scripting environments to provides users access to system resources
e.g. javascript (web servers and browsers)
e.g. Tcl (routers, CAD tools, VxWorks RTOS, NS)

6. SensorWare Software Architecture
Transient External User
download
Download
migrate
Sensor
Scripts
Sensor Node Hardware
Hardware Abstraction Layer
Node Kernel & APIs
Sensor
Scripts
Middleware
Applications
APP
SCRIPT
SCRIPT
Applications
APP
Node Kernel & APIs
Sensor
Middleware
Hardware Abstraction Layer
Sensor Node Hardware
Sensor 1
Sensor 2

7. Implementation Approach
Scripts based on subset of Tcl
embeddable scripting language with portable interpreter
RTOS/node resources & services visible as Tcl objects
network protocol stack, signal processing stack, battery
Model
scripts express interest in RTOS events and messages
events and messages put in a queue
processed by handlers specified by script
script can send, activate, and kill scripts at remote nodes
Alternative organizations being evaluated
single script per interpreter, all interpreters in a single process
single script per interpreter, single interpreter per process

8. Scripting Environment Single Process Approach
Variables
HandlerFunc1 {}
HandlerFunc2 {} . {
{Event1 HandlerFunc1}
{Event2 HandlerFunc2} }
Interpreter #1
Interpreter #n
script
script
App1
App1
Scheduler & Event Distributor
Apps
Script
Manager
Event Queue
Script as FSM
Sensor Object
N/W Object
Tcl Process
Sensor
Stack
Network
Stack
Other
Drivers/Services
Base RTOS
Hardware Abstraction Layer

9. Scripting Environment Multiple Process Approach{ .
wait e1 e2
if (e1=v1)… }
Tcl Process #1
Interpreter
script
Event
Queue
Sensor Object
N/W Object
App1
Tcl Process #n
Interpreter
script
Event
Queue
Sensor Object
N/W Object
App1
Apps
Script
Manager
Free-form
Script
Sensor
Stack
Network
Stack
Other
Drivers/Services
Base RTOS
Hardware Abstraction Layer

10. Plan and Status Implementation plan Status FY00 FY01 FY02
uC/OS on ARM processor in RSC nodes
script-based target tracking application for demo in August
no support for safety, security and authentication
FY01
implementation on Win CE with support for Sensor.com nodes
FY02
support for security, safety, and authentication
Status
port of basic Tcl interpreter to RSC node being debugged
initial tracking algorithm using script model
initial work on Tcl-ns link for rapid prototyping and testing

11. Tracking and Taxonomy of Queries
Synchronous
immediate reply
Is there movement anywhere in the 2nd floor?
Asynchronous
establish a trigger
Inform me whenever there is movement on the 2nd floor?
Tracking
continuous sampling
Inform me of the location of the object every 10s

12. Mission-specific Tracking Using Sensor Scripts
Resident application (or initial script flooded to all nodes) sends message to user informing a potential target
User downloads a tracking script to the appropriate node
script encodes a custom tracking mechanism, e.g. calculate new position every 10s and send it to user
Script spreads to form an initial sensing cluster
Script does data fusion or simple beamforming (using signal processing modules resident at the node)
Script arranges for the active sensor cluster to “migrate” as the target moves
motion prediction using history (e.g. movement direction)
sentry scripts spawned around the cluster
avoids polling and cluster management by the distant user

13. Tracking Scenario Tank @ x,y,z,t Region A Monitor Track Region A
Tank 10s
Monitor
Region A

14. Tracking Scenario
Region A
x,y,z,t

15. Tracking Scenario
Region A
x,y,z,t

16. Tracking Scenario
Region A
x,y,z,t

17. Relationship with Other Mobile Code Technologies
Download new firmware (OS, apps) over the air
RSC’s node allow this, useful for long timescale
Penn State’s Reactive Sensor Network
downloads executables and DLLs, identified by URLs, from repositories and their caches
read/write data from URLs
essentially, lookup service + Java’s applet model generalized to arbitrary executables and data
general-purpose, heavyweight (no scripting) but may complement SensorWare environment
can be used as the basis for search-and-fetch of modules needed by a SensorWare script, and for node initialization
Mobile Streams in AGNI (Ranganathan)
Mstreams not directly applicable, but underlying Tcl technology may be leveraged

18. Sensor Network Simulation & Emulation [DSN]

19. Sensor Network Simulation & Emulation [DSN]
Accurate modeling of power consumption by the protocol stack and applications
Modeling of sensors and targets
creation of standard application scenarios for comparison of protocols
Hybrid simulation/emulation
network of “virtual” and “real” sensor nodes
scalability studies with live sensor data
easier debugging and development of protocol code
“real” application on “virtual” nodes
easier debugging and development of application code
Approach: ns based

20. Power Modeling: ns-based
Mobile Node in ns
Mobile Sensor Node with Power Model
Agent
Agent
Routing
Sensor
Routing
Link Layer
Link Layer
Battery
CPU
Buffer
Buffer
MAC
MAC with
Power Management
Baseband
(Rate) RF
(analog)
Power
Amplifier
(PRF)
PHY
Model-aware PHY
Node Model
Channel
Channel

21. Hybrid Simulation & Emulation
monitor and control
hybrid network
(local or remote)
real sensor apps on
virtual sensor nodes
app
GUI
app
socket
comm
serial
comm ns
event gw
Ethernet
RS232
gateway
Dll (RSC) V V R V V
Gateway
(Windows PC) R
modified event scheduler
Proxies for real
sensor nodes
Simulation Machine (Linux PC)

22. Status Accomplishments so far
detailed radio model
MAC layer with radio shutdown management
low-power ad hoc routing protocol
first generation hybrid capability (demonstration)
routing layer and higher layers can be hybrid
network with one real and n virtual nodes
GUI to monitor the hybrid network
Collaboration with Deborah Estrin’s group
interest in UCLA’s more sophisticated energy consumption models for diffusion algorithm simulation
they will incorporate UCLA’s models into standard ns release
available to SensIT community

23. Power-aware Routing [DSN]

24. Power-aware Routing [DSN]
Traditional power unaware multihop ad hoc routing protocols focus on fast topology changes
metrics used: shortest hop, shortest delay, link quality, location stability, message & time overhead
reduced node and network lifetime, and coverage
power hot-spots
power lost in signaling messages in quiescent state
UCLA effort: routing protocols that focus on power
power-based routing metrics
leveraging location information during routing
exploiting path diversity
Intelligent combining of replies at intermediate nodes
interaction between MAC and routing (TDMA MAC)

25. Power Metrics Considered
Minimize energy consumed / packet
large dissipation at selected bottleneck nodes
just as with shortest path metric
Maximize time to network partition
important for sensor networks etc.
load balance across the nodes in the cut-set
difficult to implement
Minimize variance in node power levels
no single node is penalized, but difficult to implement
Minimize cost / packet
cost for a node reflects remaining battery life
nodes “reluctance” to forward packets
Minimize maximum node cost
delay node failure and reduce variance in remaining battery lives

26. Accomplishments Evaluation of several power metrics with DSR
significant extension of network lifetime (time to partition)
Scheme to exploit path diversity for power
idea is distribute traffic over alternative paths to increase network lifetime and coverage
packet disperser and combiner entities
Works with DSR as well as gradient based routing
evaluation metrics
time to breakdown, # of depleted nodes, RMS energy distribution
problem: do not know which nodes are important as it depends on future target traffic pattern and user movement pattern

27. Path Diversity ScenarioB C A
user
A and B generate 1 packet every 100 ms until 5s
C generates 1 packet every 100 ms from 5s till 15s

28. # of Nodes with > 10% Battery
Packets received by t=150:
Normal: 127
Stochastic: 133
Energy Disperse: 160
Stochastic ED: 161
Divert streams: 175

29. RMS Battery Energy Consumption
Lower Bound 2
Lower Bound 1

30. Intelligent Combining of Replies
Target
Target
User
User
Naive Approach
Better Approach

31. Other Accomplishment: Sensor Subsystem for ISI Platform [DSN]
XC4013XLA
FPGA
3.3v
64Kx16 Flash
128Kx8 SRAM
A1-A16
D0-D15
ADC, 8ch
Humidity 5v
Angular
Velocity 3D
Accel
Magnet-
ometer 6v
Microphone
Speaker
Light to
Freq 2
2D Accel
ADXL202
Freq 1
Temp2
Temp 1
I2C Bus
8:2 mux
IR ENC/
DEC
Serial Port
TFDU IR
RPC, 5V
GPS
D0-D7
A0 - A15
D8-D15
RS232
PC Serial Port 2
General purpose
I/O lines
3.3V to 5V
Transceiver
Processor
AT91M AC
JTAG
DAC
8:1 mux
UART 3.3v
Amp
Communications
Analog
Counter/Timer Ports
Processing
Digital
XChecker
PC Serial Port 1
GPS IR
Light
Sound in/out
2D accelerometer
Temperature
Magnetometer
Angular velocity
3D accelerometer