An Architecture to Enable

fearlessquickMobile - Wireless

Dec 12, 2013 (3 years and 9 months ago)

75 views

An Architecture to Enable

Multi
-
view Integration for Sensor
Deployments in Buildings

Jorge Ortiz

Tapia Conference Doctoral Consortium

University of California, Berkeley

April 3, 2011

/32


Integration of disparate data
source


E
nable
integrated
data
collection


Simple integration with
external systems (input and
output)


Building
Management System
captures Heat/cooling and
ventilation


Lighting systems


Miscellaneous electrical
loads


Weather data, price, etc.

Building energy consumption
highly fragmented

B
u
i
l
d
i
n
g
s

E
n
e
r
g
y

D
a
t
a

B
o
o
k
:


1
.
1

B
u
i
l
d
i
n
g
s

S
e
c
t
o
r

E
n
e
r
g
y

C
o
n
s
u
m
p
t
i
o
n
O
c
t
o
b
e
r

2
0
0
9
1
.
1
.
5
2
0
1
0

U
.
S
.

B
u
i
l
d
i
n
g
s

E
n
e
r
g
y

E
n
d
-
U
s
e

S
p
l
i
t
s
,

b
y

F
u
e
l

T
y
p
e

(
Q
u
a
d
r
i
l
l
i
o
n

B
t
u
)
N
a
t
u
r
a
l
F
u
e
l
O
t
h
e
r
R
e
n
w
.
S
i
t
e
S
i
t
e
P
r
i
m
a
r
y
P
r
i
m
a
r
y
G
a
s
O
i
l

(
1
)
L
P
G
F
u
e
l
(
2
)
E
n
.
(
3
)
E
l
e
c
t
r
i
c
T
o
t
a
l
P
e
r
c
e
n
t
E
l
e
c
t
r
i
c

(
4
)
T
o
t
a
l
P
e
r
c
e
n
t
S
p
a
c
e

H
e
a
t
i
n
g

(
5
)
4
.
8
6


0
.
8
9


0
.
2
4


0
.
1
9


0
.
4
4


0
.
5
0


7
.
1
3


3
5
.
1
%
|
1
.
5
9


8
.
2
1


2
0
.
0
%
L
i
g
h
t
i
n
g
1
.
2
9


1
.
2
9


6
.
3
%
|
5
.
7
8


5
.
7
8


1
4
.
1
%
S
p
a
c
e

C
o
o
l
i
n
g
0
.
0
2


0
.
1
9


0
.
2
1


1
.
0
%
|
4
.
0
4


4
.
0
6


9
.
9
%
W
a
t
e
r

H
e
a
t
i
n
g
1
.
6
2


0
.
1
4


0
.
0
5


0
.
0
4


0
.
5
4


2
.
3
9


1
1
.
7
%
|
1
.
6
9


3
.
5
4


8
.
6
%
E
l
e
c
t
r
o
n
i
c
s

(
6
)
1
.
8
4


1
.
8
4


9
.
0
%
|
2
.
9
6


2
.
9
6


7
.
2
%
R
e
f
r
i
g
e
r
a
t
i
o
n

(
7
)
0
.
6
8


0
.
6
8


3
.
4
%
|
2
.
1
4


2
.
1
4


5
.
2
%
W
e
t

C
l
e
a
n

(
8
)
0
.
0
7


0
.
9
4


1
.
0
2


5
.
0
%
|
1
.
1
9


1
.
2
7


3
.
1
%
C
o
m
p
u
t
e
r
s
0
.
3
5


0
.
3
5


1
.
7
%
|
1
.
1
0


1
.
1
0


2
.
7
%
C
o
o
k
i
n
g
0
.
4
7


0
.
0
3


0
.
3
8


0
.
8
8


4
.
3
%
|
0
.
4
6


0
.
9
6


2
.
3
%
V
e
n
t
i
l
a
t
i
o
n

(
9
)
0
.
1
5


0
.
1
5


0
.
7
%
|
0
.
6
0


0
.
6
0


1
.
5
%
O
t
h
e
r

(
1
0
)
0
.
2
9


0
.
0
2


0
.
2
5


0
.
0
5


0
.
1
3


2
.
0
2


2
.
7
6


1
3
.
6
%
|
6
.
3
5


7
.
0
9


1
7
.
3
%
A
d
j
u
s
t

t
o

S
E
D
S

(
1
1
)
0
.
6
6


0
.
1
9


0
.
8
0


1
.
6
4


8
.
1
%
|
2
.
5
0


3
.
3
4


8
.
1
%
T
o
t
a
l
7
.
9
9


1
.
2
3


0
.
5
7


0
.
2
4


0
.
6
2
9
.
6
7
2
0
.
3
3
1
0
0
%
|
3
0
.
3
8
4
1
.
0
4
1
0
0
%
N
o
t
e
(
s
)
:
1
)

I
n
c
l
u
d
e
s

d
i
s
t
i
l
l
a
t
e

f
u
e
l

o
i
l

(
1
.
1
3

q
u
a
d
)

a
n
d

r
e
s
i
d
u
a
l

f
u
e
l

o
i
l

(
0
.
1
0

q
u
a
d
)
.


2
)

K
e
r
o
s
e
n
e

(
0
.
0
8

q
u
a
d
)

a
n
d

c
o
a
l

(
0
.
0
9

q
u
a
d
)

a
r
e

a
s
s
u
m
e
d
a
t
t
r
i
b
u
t
a
b
l
e

t
o

s
p
a
c
e

h
e
a
t
i
n
g
.


M
o
t
o
r

g
a
s
o
l
i
n
e

(
0
.
0
5

q
u
a
d
)

a
s
s
u
m
e
d

a
t
t
r
i
b
u
t
a
b
l
e

t
o

o
t
h
e
r

e
n
d
-
u
s
e
s
.


3
)

C
o
m
p
r
i
s
e
d

o
f

w
o
o
d

s
p
a
c
e

h
e
a
t
i
n
g
(
0
.
4
4

q
u
a
d
)
,

b
i
o
m
a
s
s

(
0
.
1
3

q
u
a
d
)
,

s
o
l
a
r

w
a
t
e
r

h
e
a
t
i
n
g

(
0
.
0
5

q
u
a
d
)
,

g
e
o
t
h
e
r
m
a
l

s
p
a
c
e

h
e
a
t
i
n
g

(
l
e
s
s

t
h
a
n

0
.
0
1

q
u
a
d
)
,

a
n
d

s
o
l
a
r
p
h
o
t
o
v
o
l
t
a
i
c
s

(
P
V
)

l
e
s
s

t
h
a
n

0
.
0
1

q
u
a
d
)
.


4
)

S
i
t
e

-
t
o
-
s
o
u
r
c
e


e
l
e
c
t
r
i
c
i
t
y

c
o
n
v
e
r
s
i
o
n

(
d
u
e

t
o

g
e
n
e
r
a
t
i
o
n

a
n
d

t
r
a
n
s
m
i
s
s
i
o
n

l
o
s
s
e
s
)

=

3
.
1
4
.


5
)

I
n
c
l
u
d
e
s

f
u
r
n
a
c
e

f
a
n
s

(
0
.
2
0

q
u
a
d
)
.


6
)

I
n
c
l
u
d
e
s

c
o
l
o
r

t
e
l
e
v
i
s
i
o
n

(
1
.
2
3

q
u
a
d
)
.

7
)

I
n
c
l
u
d
e
s

r
e
f
r
i
g
e
r
a
t
o
r
s

(
1
.
8
9

q
u
a
d
)

a
n
d

f
r
e
e
z
e
r
s

(
0
.
2
5

q
u
a
d
)
.


I
n
c
l
u
d
e
s

c
o
m
m
e
r
c
i
a
l

r
e
f
r
i
g
e
r
a
t
i
o
n
.

8
)

I
n
c
l
u
d
e
s

c
l
o
t
h
e
s

w
a
s
h
e
r
s

(
0
.
0
9

q
u
a
d
)
,

n
a
t
u
r
a
l

g
a
s

c
l
o
t
h
e
s

d
r
y
e
r
s

(
0
.
0
7

q
u
a
d
)
,

e
l
e
c
t
r
i
c

c
l
o
t
h
e
s

d
r
y
e
r
s

(
0
.
8
0

q
u
a
d
)


a
n
d

d
i
s
h
w
a
s
h
e
r
s

(
0
.
2
9

q
u
a
d
)
.


D
o
e
s

n
o
t

i
n
c
l
u
d
e

w
a
t
e
r

h
e
a
t
i
n
g

e
n
e
r
g
y
.

9
)

C
o
m
m
e
r
c
i
a
l

o
n
l
y
;

r
e
s
i
d
e
n
t
i
a
l

f
a
n

a
n
d

p
u
m
p

e
n
e
r
g
y

u
s
e

i
n
c
l
u
d
e
d

p
r
o
p
o
r
t
i
o
n
a
t
e
l
y

i
n

s
p
a
c
e

h
e
a
t
i
n
g

a
n
d

c
o
o
l
i
n
g
.

1
0
)

I
n
c
l
u
d
e
s

r
e
s
i
d
e
n
t
i
a
l

s
m
a
l
l
e
l
e
c
t
r
i
c

d
e
v
i
c
e
s
,

h
e
a
t
i
n
g
e
l
e
m
e
n
t
s
,

m
o
t
o
r
s
,

s
w
i
m
m
i
n
g

p
o
o
l

h
e
a
t
e
r
s
,

h
o
t

t
u
b

h
e
a
t
e
r
s
,

o
u
t
d
o
o
r

g
r
i
l
l
s
,

a
n
d

n
a
t
u
r
a
l

g
a
s

o
u
t
d
o
o
r

l
i
g
h
t
i
n
g
.

I
n
c
l
u
d
e
s

c
o
m
m
e
r
c
i
a
l

s
e
r
v
i
c
e

s
t
a
t
i
o
n

e
q
u
i
p
m
e
n
t
,

A
T
M
s
,

t
e
l
e
c
o
m
m
u
n
i
c
a
t
i
o
n
s

e
q
u
i
p
m
e
n
t
,

m
e
d
i
c
a
l

e
q
u
i
p
m
e
n
t
,

p
u
m
p
s
,

e
m
e
r
g
e
n
c
y

e
l
e
c
t
r
i
c

g
e
n
e
r
a
t
o
r
s
,

c
o
m
b
i
n
e
d

h
e
a
t

a
n
d

p
o
w
e
r

i
n

c
o
m
m
e
r
c
i
a
l

b
u
i
l
d
i
n
g
s
,

a
n
d

m
a
n
u
f
a
c
t
u
r
i
n
g

p
e
r
f
o
r
m
e
d

i
n

c
o
m
m
e
r
c
i
a
l

b
u
i
l
d
i
n
g
s
.

1
1
)

E
n
e
r
g
y

a
d
j
u
s
t
m
e
n
t


E
I
A

u
s
e
s

t
o

r
e
l
i
e
v
e

d
i
s
c
r
e
p
a
n
c
i
e
s

b
e
t
w
e
e
n

d
a
t
a

s
o
u
r
c
e
s
.


E
n
e
r
g
y

a
t
t
r
i
b
u
t
a
b
l
e

t
o

t
h
e

r
e
s
i
d
e
n
t
i
a
l

a
n
d

c
o
m
m
e
r
c
i
a
l

b
u
i
l
d
i
n
g
s

s
e
c
t
o
r
,

b
u
t

n
o
t

d
i
r
e
c
t
l
y

t
o

s
p
e
c
i
f
i
c

e
n
d
-
u
s
e
s
.
S
o
u
r
c
e
(
s
)
:
E
I
A
,

A
n
n
u
a
l

E
n
e
r
g
y

O
u
t
l
o
o
k

2
0
0
8
,

M
a
r
.

2
0
0
8
,

T
a
b
l
e
s

A
2
,

p
.

1
1
7
-
1
1
9
,

T
a
b
l
e

A
4
,

p
.

1
2
2
-
1
2
3
,

T
a
b
l
e

A
5
,

p
.

1
2
4
-
1
2
5
,

a
n
d

T
a
b
l
e

A
1
7
,

p
.

1
4
3
-
1
4
4
;

E
I
A
,
N
a
t
i
o
n
a
l

E
n
e
r
g
y

M
o
d
e
l
i
n
g

S
y
s
t
e
m

(
N
E
M
S
)

f
o
r

A
E
O

2
0
0
8
,

M
a
r
.

2
0
0
8
;

a
n
d

E
I
A
,

S
u
p
p
l
e
m
e
n
t

t
o

t
h
e

A
E
O

2
0
0
8
,

A
p
r
i
l

2
0
0
8
,

T
a
b
l
e

2
2
.
S
p
a
c
e

H
e
a
t
i
n
g

,

2
0
.
0
%
L
i
g
h
t
i
n
g
,

1
4
.
1
%
S
p
a
c
e

C
o
o
l
i
n
g
,

9
.
9
%
W
a
t
e
r

H
e
a
t
i
n
g
,

8
.
6
%
E
l
e
c
t
r
o
n
i
c
s
,

7
.
2
%
R
e
f
r
i
g
e
r
a
t
i
o
n

,

5
.
2
%
W
e
t

C
l
e
a
n

,

3
.
1
%
C
o
m
p
u
t
e
r
s
,

2
.
7
%
C
o
o
k
i
n
g
,

2
.
3
%
V
e
n
t
i
l
a
t
i
o
n
,

1
.
5
%
O
t
h
e
r

,

1
7
.
3
%
A
d
j
u
s
t

t
o

S
E
D
S

,

8
.
1
%
2
0
1
0

U
.
S
.

B
u
i
l
d
i
n
g
s

E
n
e
r
g
y

E
n
d
-
U
s
e

S
p
l
i
t
s

1
-
5
HVAC: 31.4%

+

2

/32

Main research question


What is a good system architecture to


1)
Integrate

multiple physical data streams


2) Combine mathematical and physical
modeling

with
real
-
time
data processing


3) support a
diverse

set of monitoring and
control applications?


Energy efficient buildings

3

/32

What are the metrics?


Generality


Supports the integration of many input/output sources


Ease of use


Add/remove input sources, add/remove output targets


Use the metadata to make more informed
queries


Querying/Cleaning/Modeling/Sharing the data


Organizing principle: Everything looks like a
distributed file system

4


/32

High
-
level system
Architecture

weather

price

BMS

Zigbee

WirelessHART

Message Dispatcher

Storage

Security

Incoming Streams Manager

Data representation layer

weather

price

BMS

Zigbee

WirelessHART

Metadata Management

Stream processing

Model Manager

Integration

Modeling

Application

Interface

5

/32


Fault detection study[Schein2005]








Fault: Simultaneous heating and cooling


PID controllers on separate schedules

Why integrate?

Heating coil valve

Position varies

Outside
-
air mixer

Position varies

Cooling coil remains


off

6

/32

Other opportunities

for integration


Human
-
activity classification


Electrical
activity [Patel2007]


HVAC air
pressure [Patel2008
]


Water
usage[
Froehlich2008]


IP traffic and circuit
-
level activity [Kim2010]



7

/32

Savings with

intelligent control


SmartThermostat
[Lu2010]


Combines motion sensors and contact switches to
reduce HVAC energy consumption by 28
%



Distributed Wireless Control for Building
Energy Management [Marchiori2010]


Devices that share contextual information can
control a space more intelligently and save
energy
(up to 40% on certain appliances).


8

/32

Integration With Current

Systems is Hard

9

/32

Commercial BMS
Architecture

Field Level

Routing/Co
ntrollers

Applications

10

/32

Problems with BMS’s


Not designed to collect all the data


Memory limit at control layer, application layer


Most information is lost through sense
-
point
“bundling” (averaging)


Burden on
o
perator to manage


Building manager is not a data analyst!


Must decide which signals to “trend
/
unbundle”,
monitor (set trigger)



Multi
-
signal fault detection done by human
operator


11

/32

The world is a nasty place


State
-
of
-
art not designed for data collection


30% of sensors are broken[BEMS2000]


Mixed air reading errors +2.8
C
elsius


increases
cooling energy consumption by
60%
[Kao1983]


Mixed are reading errors
-
2.8
C
elsius


increases
heating energy consumption by
30%
[Kao1983]


Imperfect data, imperfect view of physical
state


Large
accouting

errors, false diagnoses, poor
forecasting

12


/32

Integration fills in the gaps


BLAST, DOE
-
2,
EnergyPlus


Integrates building data with model
processing


BMS +
EnergyPlus

[ESL2002]

Fault
Detection

13

/32

Lessons learned so far…


Current work optimizes a particular system


More input sources used by control mechanism


Cross
-
system information flow should be
standard and not special case


Developing “smarter” ad
-
hoc system is time
consuming


Many mechanisms are duplicated

14


/32

System Architecture

11/30/10

15

/32

Building
multiview

integration


Electrical Load Tree

Environment and Activity

Climate plant

/
SodaHall

/
hvac

/CT

/Chiller

/
loadtree

/panel

/
xform

/spaces

/floor3

/floor4

16

/32

Organizing the metadata

/
SodaHall

/inventory

{


desc
”:”inventory
inside SDH”

“timestamp”: …

}

{“
desc
”:”Lamp”

“timestamp”: …}

{“
desc
”:”Phone”

“timestamp”: …}

{“
desc
”:”Outlet”

“timestamp”: …}

{“
desc
”:”Acme”

“timestamp”: …}

r
-
node

s
-
node

/
hvac

/CT

/Chiller

/vent

/
loadtree

/panel

/
xform

/outlet

/spaces

/floor3

/floor4

17

/power

/mote123

/32

Data collection and querying

/inventory/mote123

DB

PID
1

PID
1

PID
2

PID
2

PID
3

PID
3

PID
4

PID
4

Time

GET

?query=
true&ts_timestamp
=gt:now
-
100,ls=now

GET

/SDH/spaces/*?query=
true&props_metertype
=
powermeter

{


metertype
”:”
powermeter
”,


desc
”:”Electric power meter”,

“timestamp”: 1290500046

}

18

/power

/temp

/hum

/par

/32

Data representation layer


Narrow
-
waist for data representation


Simple Metering and Actuation Profile (
sMAP
)

sMAP

Electrical

Weather

Geographical

Water

Environmental

Structural

Actuator

Occupancy

Physical
Information

/ # list resource under URI root [GET]


/data # list sense points under resource data [GET]


/ [sense_point] # select a sense points [GET]


/meter # meters provide this service [GET]


/ [channel] # a particular channel [GET]


/reading # meter reading [GET]


/format # calibration and units [GET/POST]


/parameter # sampling parameter [GET/POST]


/profile # history of readings [GET]


/report # create and query periodic reports [GET/POST]

19

/32

RESTful

+ JSON

Interface

20

{


"operation":"
create_publisher
",


"
resourceName
”:”power"

}

PUT http://is4server.com/is4/devices/mote123/

{



pubid
"
:
"
550e8400
"
,

}

REPLY: 201 Created

PUT http://is4server.com/is4/devices/mote123
/power

{


"
Reading":
120,

}

POST
http://is4server.com/is4/devices
/


mote123/
power?pubid
=
550e8400

{


desc
”:”Temperature mote”,

"
Reading":
120,

“timestamp”: 1290500046

}

GET http
://is4server.com/is4/devices
/


mote123/power

/32

Sharing real
-
time feeds

21

POST http://
is4server.com/
sub

{


"
streams
":
[
550e8400
]
,


"
url
":"http://128.32.37.21:8011/
sub.php
"

}

{



s
ubid"
:
"
41d4
"
,

}

http
:/
/is4server.com/sub/
41d4

REPLY: 201 Created

m
ote123/power

price

BMS

Zigbee

StreamFS

http://128.32.37.21:8011/
sub.php

POST

550e8400

41d4

/32

Data cleaning and

distillation
s
taging

22

Interpolate

F
1
(
x)

Extrapolate

F
2
(x)

/distillers

/inventory/mote123

/power

/current

/
interp

/filter

/inventory
/mote123/power
|

/distillers/
interp

|

/
distillers
/filter

|

http://128.32.37.21:8011/
sub.php

/
procstage
/983hfq

Java/
Javascript
/Python

/32

Putting it all together

(1)

23


/floor4

/room410

/
therm

/mote01

GET

/…/floor4/room410/*/*?query=
true&prop_type
=temp

/temp

/temp

Interpolate

F
1
(
x)

Interpolate

F
1
(
x)

/…/floor4/room410/room410
/
therm
/temp

/…/floor4/room410/room410/
mote01/temp

/…/floor4/room410/room410/mote01/
temp?query
=
true&ts_timestamp
=lte:t1,gte:t7

/…/floor4/room410/room410/
therm
/
temp?query
=
true&ts_timestamp
=lte:t1,lte:t7

/32

Putting it all together (2)

24


/…/floor4/room410/*/*?query=
true&type
=
device

|

/distillers/
ts_getall?ts_timestamp
=lte:t1,
g
te:t7

|

/
distiller
/
interp_all
?attr
=
timestamp&unit
=1

|
/
distiller
/
join?attr
=
timestamp

|

http://
viewer.com
/
viewer.php

t
1

t
2

t
3

t
5

t
4

t
6

t
7

m
ote01/temp

therm
/temp

In
-
time pipe
-
chain

Continuous pipe
-
chain

/32

Current Status


Public
StreamFS

implementation


http://is4server.com


Sutardja

Dai Hall BMS
HVAC system


400+ live streams


Cory Hall Electrical data


2500+ live streams


Streaming data rate


~600
-
700 Kbps


Almost 300 GB stored





/32

Open questions and

u
pcoming work

26

/32

Standard distillation

elements


Provide regression,
interpolation,
extrapolation functions
over space and time
values


Provide join and filter
functions

x

y

User

x

y

Consistent

uniform

view

Apply regression;

Compute

temp


at grid


points

27

/32

Streams, models, and
resampling


Real
-
time

query optimization


Raw stream data


Post
-
processed distillation data


Model

output data


Physical, mathematical, probabilistic models


Prior work


MauveDB
,
FunctionDB


Focus on model expression and model
-
query optimization


TelegraphCQ
, Eddies,
Psoup


Focus on raw streaming query optimization

28


/32

Message
-
passing

Scalability

Processing
Element

Processing
Element

Streams

Subscribers

Processing
Element

Processing
Element


Scaling with processing time (p), the number of
streams (s), and the number of subscribers (t)

29

/32

Efficient graph and
timeseries

queries

Inter
-
relationship analysis

Through graph queries

Time
-
series correlation

analysis

30

/32

Architecture is an enabler


P
roactive detection of inefficient energy use


Enables exploration for energy
-
saving
opportunities by focusing on the data

31


/32

Feedback/Questions?

32

Jorge Ortiz <
jortiz@cs.berkeley.edu
>