CENS Seminar, Dec 2007 (1) Alberto Cerpa © UCM Human in the loop: From Search and Rescue to Smart Buildings Alberto Cerpa Assistant Professor Computer

CENS Seminar, Dec 2007 (1) Alberto Cerpa © UCM

Human in the loop: From Search and Rescue to Smart Buildings

Alberto Cerpa

Assistant Professor Computer Science

School of Engineering, UC Merced

CENS Seminar, UCLA

December, 7 2007


Outline

• SenSearch– GPS and witness assisted tracking for DTNs

– How does it work?

– Performance evaluation

• SCOPES


Design Goals for Family of Applications

• Self-*, self-operate, self-configure

• Long lifetime

• Small and light weight

• Non intrusive; no infrastructure needed

• Power and memory efficient

• Low cost

• Meets security and privacy requirements


Search and Rescue

Goal: To build a search and rescue system that can pinpoint missing person’s last seen point and expected location in wilderness areasLost hikers, stranded climbers,

injured skiers, …Difficult because of lack of timely

information about the current location

“Last seen point” is critical for search and rescue actions


A real case of a last seen point


Current Search and Rescue Technologies

• The Old School Way – Ask

• Personal GPS receiver and Satellite transmitter – Power greedy; Must operate manually to send your location

• Localization system and GSM transmitter – Need GSM network coverage

• Avalanche beacon/RFID reflector – Limited usage

• Need a better, cheaper, reliable system


SenSearch

• Comprised of:

– RF sensors

– GPS receivers

– Access points

– Control center

• GPS and Witness Assisted Tracking for Delay Tolerant Sensor Networks

• Collaboration with Jyh Huang, John Ledbetter, Shivakant Mishra and Rick Han from University of Colorado at Boulder, together with Lun Jiang, Ankur Kamthe and Ian Freeman from UC Merced


SenSearch – How it Works (I)

Node ID Coordinate Time

10

3


8

6

8

6

2

10

3

1

1 2


SenSearch – How it Works (II)


10

3

8

6 x3, y3, z3 16:58

2

1


…

6 x1, y1, z1 12:31

…

…

6 x2, y2, z2 14:09

…

6 x3, y3, z3 16:58


SenSearch – How it Works (III)

Hiker 6 is reported missing at 23:59 Node ID Coordinate Time

…

6 x1, y1, z1 12:31

…

…

6 x2, y2, z2 14:09

…

6 x3, y3, z3 16:58

x1, y1, z1

x2, y2, z2

x3, y3, z3

Inferred location at 23:59

Hot Search Zone


Witness

Witness

Search & Rescue Team Control Center

SenSearch – Architecture Overview


Main Design• Duty cycle: independent duty cycle periods for

both the GPS unit and the radio

– Paramount for increased system lifetime

– GPS cold start takes 8-12 sec, need coordinates before the possible encounter

• Memory reuse: limited number of entries per source

• Group communication and storage:

– When nodes move in groups, leader election algorithm based on energy reserves

– Data replication among members of the group


SenSearch Implementation

• Full implementation and testing ongoing at UCM (June-July ‘06, Nov-Dec ‘07) and Boulder (Oct-Nov ’06, Sep-Nov ‘07)

• Some preliminary results:

– Bounded localization error (70 meters average)

– Increase system lifetime with the use of duty cycling techniques for both the GPS and radio units

– Bounded memory usage without degradation in localization error


Localization Error


Power and Memory Usage


Some lessons learned• GPS duty cycle is critical for this type of systems

– No significant increase in localization and tracking error

– Large number of local GPS readings decrease overall performance

▪ Increase in memory usage and total data transferred

• Group of people moving together dramatically affect performance

– Nodes within the group constantly updating tables increase network traffic and memory usage

– The effect gets propagated to other nodes when they come in contact with members of the group

• Memory bounds– Storing only last n recent entries per node helps bounds the

size of each node’s database


Future Work• Data richness is not being properly exploited:

– Simple linear regression for inferred localization

– Machine learning methods that exploit past history and topological (hike) path structure seem quite promising

• Need larger scale experiments (20+ nodes)

– Problem: Very costly to get localization ground truth for validation as the number of nodes increases

• Other applications:

– Cattle tracking for health and epidemic control

– Kids tracking in theme parks


Outline

• SenSearch

• SCOPES: Smart Cameras Object Position Estimation System– System design overview

– Local processing algorithms

– Performance evaluation

• Other projects

• Summary


SCOPES: Distributed Smart Cameras• Goal: build a distributed vision based tracking

system for human density estimation inside buildings

– Automatic distributed control of HVAC systems

– Efficient lighting use and control

– Building usage and design

• Part of the “Living Laboratory” concept

• Synergy with other disciplines:

– designing and constructing green buildings that produce net-zero energy use and carbon emissions

– being the first comprehensive research university that is energy independent and carbon neutral!


Economic ImpactA larger fraction of electricity goes to buildings in rich countries!

Data provided by Paul Waide, graphics by Shoibal Chakravarty, from Robert Scollow


• 35 nodes deployed in the second floor of SE1 Building and 65 more nodes (100 total) to be deployed by Jan 15 (hopefully!)

• It can measure temperature, humidity, light and movement

• It will interface with the building’s EM&CS

• Close the loop: temperature and air flow control commanded from the network

• Integration with demand-response systems


Testbed Components

• Hacked version of USB/IP driver to support unlimited number of devices.

• Provides multiple local usb devices as if the nodes were directly connected to the main server

• Running EmStar/EmTOS as well as stand-alone TOS

USB (data + power)

POE (data + power)

USB/IPserver

Ethernet (data)

USB/IPclient


Software Architecture


Image Capture• Cyclops board has 512 KB of SRAM, in 8

banks of 64 KB each (max addressable by the MCU)

• Most efficient image capture:

– We capture as many consecutive nFrames (images) as possible to fill a single memory bank

– We switch banks and continue capturing images

– Interleaving image capturing and processing proves to be less efficient


Object Detection + Background Update• Perform background subtraction of each image with

the reference background image

• Keep EWMA of both the mean and the deviation:

• Depending on the values of Mean and Deviation we apply the following algorithm:


Grouping and Direction Inference• Raster scanning for connected components

• Check if pixel x is connected to a,b,c,d

• Group labeling is done according to:

a b c

d x

• Object counting and center of mass for each object

• Tracking above for multiple images provides direction of travel and speed of each object


Local Processing Performance

• The local processing time ultimately depends on the number and frequency of objects detected over time.

• This introduces a variable duty cycling, since the camera does not capture images while processing the data in the banks


Occupancy and Flow Estimation Maps

• 16 nodes, covered 150 sq. meters of SE1

• Main source of error: missed events due to duty cycling


Position Estimation and Detection Error

• The position estimation error remains roughly constant at different times of the day

• The detection failure probability decreases as we increase the density of the nodes


Detection Probability and Latency

• The detection latency increases as we increase the memory used.

• The detection probability however, significantly increase as we increase the memory used.


Integration: lighting and HVAC control

Concentrated and filterednatural daylight

is conveyed to the interior

through a liquid light pipe with minimal roof

penetration

Collaboration with Prof. Roland Winston


Thank You!


Energy Management


Memory Management

Documents

CENS Seminar, Dec 2007 (1) Alberto Cerpa © UCM Human in the loop: From Search and Rescue to Smart Buildings Alberto Cerpa Assistant Professor Computer