A migration-based approach towards resource-efficient wireless

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25 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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1
/
46

A migration
-
based approach towards resource
-
efficient wireless
structural health monitoring


Kay Smarsly
*
, Kincho H. Law

Department of Civil and Environmental Engineering

Stanford University

Stanford, CA
,
USA


Abstract


W
ireless sensor networks
have emerged as a
complementary technology
to

conventional,
cable
-
based systems

for

structural health monitoring
.
However,
the
wireless
transmission of
sensor data
and the
on
-
board
execution of engineering analyses
directly o
n the sensor nodes
can consume a significant

amount of the
inherently restricted
node resources
.
This paper
presents a
n agent

migration

approach towards resource
-
eff
icient wireless sensor networks
.
A
utonomous software agents
, referred to as “on
-
board agent
s”
,

are embedded into the
wireless sensor nodes

employed for structural health monitoring

perform
ing

simple
resource
-
efficient routines

to

continuously
analyz
e
, aggregat
e
, and communicat
e

the sensor
data

to a central server
.
Once
potential

anomalies
are detected
in the observed structural
system,
the on
-
board agents
autonomously
request
for
specialized software
programs


*

Corresponding author; Department of Civil and Environmental Engineering; Yang &


Yamazaki Environment & Energy Building (Y2E2); 473 Via Ortega, Room 279;


Stanford University; Stanford, CA 94305
-
4020; USA; phone: +1
-
650
-
283
-
5586;


email: smarsly@stanfor
d.edu


2
/
46

(“migrating agents”)

that

physically migrat
e

to the sensor nodes to
analyze

the
suspected
anomaly

on demand
.
In addition to the local
ized data analyses
,
a central information pool

available on
the

central server is
accessible by the software agents

(
and by human users
),
fac
ilitat
ing

a distributed
-
cooperative
assessment of
the global
condition

of the monitored
structur
e
.

As a result

of this study
, a 95% reduction of memory utilization and a 96%
reduction of
power

consumption
of the wireless sensor nodes
have been
achieved as
compared with traditional approaches
.


Keywords


Structural
Health

Monitoring,
Wireless Sensor Networks,
Smart

Structures, Distributed
Artificial Intelligence,
Mobile
Multi
-
Agent Systems
,
Dynamic Wireless Code Migration


1
Introduction


According to the American Society of Civil Engineers (ASCE), deficient and deteriorating
surface transportation infrastructure in
the United States is expected to cost $912.0 billion
by 2020 and more than $2.9 trillion by 2040 [
1
].
As the Urban Land Institute (ULI)
reveals
in its “Infrastructure 2012” report [
2
]
, t
he situation

in other regions is similar
, for example
in China and India


countries that are rapidly urbanizing


or in Europe, where
investments for infrastructure improvements of more t
han
$
2.6 trillion (

2.0 trillion) are
needed
.

O
ther
infrastructure systems
, such as dams, buildings or wind turbines,
face similar
problems as they are
subjected to ageing and
other
environmental
factors
. Therefore, future

3
/
46

generations of civil engineering
structures,
termed

“smart
structures
”, are expected to be
instrumented with structural health monitoring (SHM) systems
so that the structures are
capable of continuously monitoring and assessing
the
ir

own
structural condition
s

[
3
-
5
]
.


Structural health monitoring

systems,
consisting of
sensors, data acquisition units,
computer systems and connecting cables,
are designed to
detec
t

structural changes before
they reach cri
tical states
.
By analyzing
the sensor data recorded from the structures
,
SHM
systems

provide
the
opportunities

to enhance the safety and reliability of engineering
structures

and to reduce
the
cost
s

for management, maintenance and repair throughout the
str
uctures’ life cycles

[
6
]
. However, in
conventional
SHM system
s

the installation of cables
can be expensive, time
-
consuming and labor
-
intensive, entailing high maintenance costs for
the SHM systems. Eliminating the need for connecting cables, wireless sensor networks
have emerged as a cost
-
effective and reliable alter
native to conventional, cable
-
based SHM
systems
[
7
-
11
]
.
Composed of
numerous

wirelessly connected sensor nodes, wireless sensor
networks are
installed

in the structure
to automatically collect, analyze
,

aggregat
e a
nd
communicat
e

vast amounts of
sensor data.
The sensor nodes, i
ntegrating advance
d
embedded systems technologies
,
are c
apable of self
-
interrogating collected
sensor

data for
signs of structural
changes

[
12
,
13
]
.

In essence, the sensor data is first
analyze
d

and
aggregate
d on the nodes,
from high
-
bandwidth raw sensor data to low
-
bandwidth streams
of processed results. T
he
analysis results are then

t
ransferred to centralized computer
systems
, or to adjacent
sensor
nodes,

for further processing.



4
/
46

By first

analyzing the data sets locally and then communicating the results to
the
connected

computer systems,
transmissions

of large records of raw sensor da
ta can be avoided.

As a
result,
the
energy consumption for wireless data transmission is substantially

reduced.
However, considerable computational power is needed for the local

execution o
f complex
engineering analyses.

Therefore
, t
here have been active r
esearch efforts

in the past several
years
towards
redu
cing

the
power

consumption
of

wireless
sensor nodes

by

optimizing
the
sensor node
hardware
as well as

the software embedded into the nodes
.
For example, n
ew
software

approaches, such as energy
-
efficient source coding
and
resource
-
efficient network
protocols, and new concepts on hardware

circuitry and transmitter modules for improving
energy
-
consuming
node
components

have been proposed [
14
-
16
].



Besides the resource consumption, a second major issue
when deploying wireless sensor
networks for st
ructural health monitoring is the
isolated, limited view
of a wireless sensor
node
on a small

area of the total structure.

It is well known that c
hanges in the global
structural response and behavior

(such as altered stiffnesses and structural stabi
lity)
should
also be considered i
n addition to the detection of local damages and deteriorations

(e.g.
corrosion,
cracks, etc.).
Since

the
sensor
data is usually collected at critical

locations,
individual sensor information does not provide a global picture of
the

structural condition.
In summary, besides

making the hardware
and software
more resource
-
efficient, holistic
(local/global) strateg
ies

are

needed to assess local and global

structural changes.


T
he
goal
s

of
the
research presented
herein are

twofold. Fi
rs
t
,
the resource consumption of
the sensor nodes is to be reduced with respect to memory utilization

and power

5
/
46

consumpt
ion. S
econd, a
SHM
system

prototype
,

capable of
holistically
monitoring

local as
well as global structural phenomena
, is to be implement
ed
.

To achieve these
goals
,
this
study integrates
mobile multi
-
agent systems
and
dynamic wireless code migration
into
a
wireless sensor network
.
The

paper
begins

with a
background on
mobile multi
-
agent
systems
.

Then,

the migration
-
based monitoring concept
is described in detail
, and the
architecture

and

prototype
implementation of
the agent
-
based
SHM system
are
shown
.
Next,

laboratory tests are presented validating the
feasibility of the newly proposed concept
a
s well as

the
performance
of the prototype sys
tem
.
Th
e

paper

concludes with
a
discussion
of the test

results

and a comparison of the
proposed
migration
-
based concept with
conventional approaches
currently

used in
structural health

monitoring.


2
Background o
n

mobile multi
-
ag
ent systems


Multi
-
agent
technology
, originating from distributed artificial intelligence research,

is
a
rapidly developing research area
that is
of practical relevance

since many years
[
17
]
.

The
broad range of a
pplication domains of multi
-
agent systems include
s
,
e.g.
,
process control,
air traffic control, business process management,

health care,
water resource management
,
traffic and transportation engineering,
building control
,
power engine
ering applications
, and
structural design

[
18
-
25
]
. M
ore recently,
multi
-
agent systems have also been applied
in
different branches of structural health monitoring, such as monitoring of
bridges, dams, and
wind turbines [
26
-
29
]
.



6
/
46

Although t
he term “agent” has often been misused
as well as overused
[
30
]
,
one

definition
has been widely accepted
in the artificial intelligence community
;

t
he
“weak notion of
agency”
,

proposed by
Wooldridge and Jennings

[
31
]
, defines an agent
as a computer
program

possessing four
essential
properties
. An agent




operates without the direct intervention of
humans and
,
unlike
a common
software object
,

has control o
ver its actions an
d internal states (“autonomy”),




interacts with other agents through agent communicatio
n languages (“social
ability”),




perceives its environment, e.g. the physical world

or a software environment
,
and responds in a timely fashion to
environmen
tal changes (“reactivity”) and




exhibits goal
-
directed behavior by taking initiative
s

(“pro
-
activeness”).


M
ultiple interacting
software agents
in association with the
agent
en
vironment

form a

multi
-
agent system
.
Due
to

the above mentioned agent properties
, multi
-
agent systems are
characterized by a high degree of scalability,
modularity
, flexibility

and
extensibility
, which
makes
multi
-
agent technology

a suitable basis for solving distributed engineering problems
as in
structural health monitoring.



7
/
46

I
n the last decade
, c
onsiderable success has been
reported

i
n porting multi
-
agent technology
on mobile devices such as cell phones, smart phones
,

or wireless sensor nodes
(“mobile
multi
-
agent systems”)
[
32
-
34
].
T
he distinctive strengths of multi
-
agent systems



scalability,
modularity, flexibility and

extensibility



are utilized i
n
mobile applications
facilitating
distributed
-
cooperative problem solving in highly dynamic environments
.
To
adequately deal with the constraints associated with developin
g applications on small
devices
, t
he majority of mobi
le devices supports some form of the Java programming
language [
35
].
Accordingly
, most approaches towards mobile multi
-
agent systems are
based on Java, typically
using

the Connected Limited Device Configuration (CLDC)

[
36
]
.
C
LDC
, a

fundamental part of the

Java Platform, Micro Edition


(J
ava
ME)
, defines

the
most basic libraries and virtual

machine features
for resource
-
constrained devices.

It is
worth mentioning that CLDC, a
lthough offering
all
major
advantage
s

provided by the Java

language
such as
object

orientation, portability, robustness and security
,
in its current
version 1.1 requires
only
160 k
B
of non
-
volatile memory

to be allocated

for the
CLDC
libraries

and
for
the Java
virtual

machine
, and
needs
only
32 kB of v
olatile
memory

f
or the
virtual machine runtime
[
36
]
. As can be seen from
Table
1
, the total memory budget

nee
ded by the CLDC specification
, compared with the “Java Platform, Standard Edition”
(Java SE)
for desktop and server environments
,
is
as

little as 0.07

% [
37
]


Table
1
.
Minimum system requirements of Java SE and Java ME.



Java Platform, Standard Edition

Java Platform, Mirco Edition



(Java SE 7)

(Java ME, CLDC 1.1)

Processor

266

MHz


16

MHz

Disk space

126

MB


32

kB

Memory

128

MB
1

160

kB


8
/
46

1
Windows 64
-
bit operating systems
.


Several
Java
-
based
agent
platforms for mobile devices, supporting the development of
mobile multi
-
agent systems
, are currently available
.
Examples

include

DARPA
CougaarME [
32
],

AFME [
38
], SPRINGS [
39
],
3APL
-
M [
40
]
,
JADE
-
LEAP [
41
,
42
]
,
and
MAPS [
43
].
Agent

platf
orms
for mobile devices
essentially provide lightweight subsets of
Java classes support
ing

basic

agent

services
for

communication,
for
multitasking,
or



if
embedded into wireless sensor nodes


for
access
ing

the node resources (e.g. sensors

or
memory
).
Detailed reviews
as well as

comparisons of architecture
s
, programming models
and performance
s

of
agent
platforms
for mobile devices
can be found in [
34
,
44
,
45
].


It

has been recognized
in recent years
that the performance and the dynamic beha
vior of
mobile multi
-
agent systems can further be enhanced by
wireless

code migration

[
46
]
.
Having demonstrated high effectiveness in conventional wired decentralized systems, code
migration represents an emerging and powerful paradigm
, which

is

already

supported by
some
state
-
of
-
the
-
art
Java
-
based
agent
platforms

[
38
,
43
,
47
].
Wireless c
ode migration, i.e.
agents physically migrating from one mobile device to another including dynamic
agent
behavior, actual state and specific knowledge, enables mobile multi
-
agent
systems to
dynamically adapt to changes and altered conditions of their environm
ent,
resulting in a
substantial reduction of
network load,
latency
, and resource consumption
.
While
agent
migration
in
mobile multi
-
agent systems

has
already been
developed and

prototypically
implemented

in related research areas
[
48
]
,
agent

migration
in
wireless sensor networks
deployed
for structural health monitoring has received little a
ttention
.


9
/
46


3
A
n
agent
-
based
structural health monitoring

system


This section describes the
basic concept, the architecture, and the prototype
implementation
of an agent
-
based
wireless
SHM system
.
When developing the SHM system
, two main
goals are pursued
,


(i)

to reduce the
resource

consumption of the sensor nodes with respect to
on
-
board memory utilization
and

wireless
data communication
, and


(ii)

to enhance the reliability of the SHM system enabling automated assessment
of both local and global
conditions

of the

observed structural system.


These goals are to be achieved by integrating

a mobile multi
-
agent system, allowing for
dynamic agent migration,

into the wireless sensor nodes
. In addition
, a

central

information
pool
is
installed on the
local

computer
.
The i
nformation pool, f
acilitating
a collaborative

assessment of the global
structural condition
,
provides
information
on modal properties of
the structural system, information on sensor nodes installed, and a catalog of data analysis
algorithms
.

Last but not least, a monitoring database is deployed to persistently store the
sensor data
that is
continuously
recorded from the structural system.


3.1
Architecture of the
structural health monitoring

system



10
/
46

As
depicted in
Fig.
1
, t
he
agent
-
based
SHM system is composed of three basic components
,
wireless sensor nodes, a base station
,

and
the

local computer
.

Each
sensor
node hosts a set
of
mobile
agents

and forms a cluster together with other sensor nodes
.
A cluster
is managed
by a head node
, which
performs administrative tasks
,

such as management of hardware and
network features
,
but does not collect or analyz
e sensor data
.

T
he base station
, serving

as an
interface between the wireless sensor nodes and the local computer
,
forwards sensor data
and information, assembled by the agents, from the wireless sensor nodes to the local
computer

for persistent storage and further processing. Vice vers
a, commands sent from the
local
computer are communicated via the base station to the wireless sensor nodes
.
Furthermore,
the local computer provides
user interfaces
,

and
external resources
can be
connected
to the wireless sensor network
.



Fig.
1
.

Architecture of the agent
-
based wireless SHM system.


11
/
46


To reduce the quantities of communicated
sensor

data and to economically utilize the
restricted computing resources,

two types of
mobile agents
,
“on
-
board agents”

and
“migratin
g agents”,
are embedded in
to

the nodes.
Fig.
2

illustrates the dynamic interaction
of the agents involved and
the
proposed operational workflow.
The
on
-
board agents
,
autonomously executing
relatively simple yet resource
-
efficient algorithms at relatively low
sampling rates
, are installed on the wireless sensor nodes to continuously

collect,

analyz
e,
aggregate and communicate the
sensor data
. If having
detected

(potential) anomalies

on a
sensor node,
the on
-
board agents proactively adapt their behavior to the new situation, e.g.
by modifying the
sensor

sampling rate
s
. Thereupon,
specific algorithms and further
knowledge
,
required for more comprehensive
a
nalyses
of

the
sensor
data
, are requested by
the on
-
board agents from the head nodes
of

the SHM system; instead of heaving extensive
collections of engineering algorithms installed on every wireless sensor node a priori
,
specialized migrating agents

are re
quested on

demand to
physically migrate to the
respective sensor node.

Automatically composed during runtime
,
the
migrating agent
s

are
assembled with

the required algorithms

and specific expert knowledge
, which enables the
agents
making a
ppropriate decisio
n
s

directly on a
wireless
sensor node.



12
/
46


Fig.
2
.

Proposed operational workflow in the agent
-
based SHM system
.


3.2
Hardware of the
wireless sensor network


For the prototype implementation of
the
wireless sensor
network
,
Java
-
based
Oracle
SunSPOT
sensing
units

are deployed

[
49
,
50
]
.
The

sensing units

have already proven their
practicability and performance in a multitude of scientific projects [
51
-
57
]. As a distinct
advantage, unlike common embedded applications for wireless sensor networks
that
are
usually written in low
-
level native languages
such as C/C++ and assembly language, the
sensing units comprise
of
a fully capable

J2ME CLDC 1.1 Java
virtual machine
.



13
/
46

The computational core
of
the

sensing unit
s

is an Atmel AT91RM9200 system on a chip
(SoC) incorporating a 32
-
bit ARM920T ARM processor
with 16 kB instruction and 16 kB
data cache executing at 180 MHz maximum internal clock speed

[
58
]
. The SoC includes
several peripheral interface units such as USB hos
t port, USB device port, Ethernet MAC,
programmable I/O controller, serial peripheral interface controller, I
2
C
bus
, etc. Memory of
the
sensin
g

unit
s

is

a Spansion S71PL032J40
with

4 MB flash memory and 512 kB RAM.

For wireless communication,
an integrated

radio transceiver,
the IEEE 802.15.4
-
compliant
Texas Instruments (Chipcon) CC2420 single
-
chip transceiver
,

is deployed
,
oper
ating

on
the
2.4 GHz unregulated industrial, scientific and medical (ISM) band.
Power supply is
provided by an internal, rechargeab
le lithium
-
ion battery (3.7

V, 720

mAh).


For acceleration measurements,
a

low
-
power
three
-
axis linear accelerometer
, type

LIS3L02AQ

manufactured by STMicroelectronics, is
integrated into
the sensing unit
s

[
59
]
.
Consisting of a
m
icro
-
e
lectro
-
m
echanical
s
ystem (MEMS) sensor element, the
accelerometer

measures a bandwidth of 4.0 kHz in x
-

and y
-
axis and 2.5 kHz in z
-
axis over
a scale of ± 6 g.

It has

a

noise
density

of

5
0

μg/Hz
1/2

enabling

a resolution of 0.5 mg over
100 Hz.

In addition

to the
three
-
axis
accelerometer
, the
sensing
unit
s

comprise

of
an
integrated temperature sensor

operating from
−40°C to 1
25
°C
,
two

momentary switches for
user interaction
, 5 general purpose I/O pins, 4 high current output pins,
and 6 analog inputs
.


On the software side, a
Squawk virtual machine
, running

without an underlying operating
system
, ensures a lightweight execution of multi
ple embedded applications on the sensing
unit
s

[
60
]
. O
perating system functionalities are
provided by the
Squawk virtual machine
,

14
/
46

which

executes directly out of the f
lash memory
. T
he Squawk virtual machine
offers
features relevant to resource
-
efficient, agent
-
based SHM, such as garbage collector, thread
scheduler, and interrupt handler.
By running without an underlying operating system
,
memory
of the sensing unit
s

is saved that would otherwise be consumed by the operating
system. As Squawk is mostly written in Java,
additional

memory savings arise because Java
byte code is a more efficient representation than its equivalent in
machine

code.
Furthermore, w
hereas most

Java virtual machines run a single application,
the
Squawk
virtual machine
can run multiple applications, each being represented as a

Java

object and
completely isolated from all other applications
[
61
]
. In total, a high degree of portability,
flexibility, exten
d
ib
i
lity and maintainability as well as an ease of debugging is achieved
,
which makes Squawk a powerful
basis for prototyping mobile
multi
-
agent systems for
wi
reless structural health monitoring.


3.3
Prototype i
mplementation

of the structural health monitoring system


The

mobile multi
-
agent system is
implemented and
embedded into the wireless sensor
nodes following the multi
-
agent architecture proposed
by Smarsly
et al.

[
62
]
. The
architecture, based on the MAPS platform [
43
],
is
characterized by components
that
interact through events
.
As shown in
Fig.
3
,
the
main components

include

(i) the
mobile
agents
, (ii) the
mobile agent

execution engine

for executing mobile agents and fulfilling
service requests issued by the agents, (iii) the
resource manager

for accessing sensor node
resources

(e.g. sensors, actuators, battery, or flash memory)
, (iv) the
timer manager

for
timing agent
actions, (v) the
mobile agent naming

for consistent naming
of agents
and

15
/
46

dynamic management
of sensor nodes, (v
i) the
mobile agent communication channel

for
inter
-
agent communication, and (vii) the
mobile agent migration manager

for
executing the
migration

of agents.



Fig.
3
.

Main components of the MAPS
-
based multi
-
agent architecture.


While the mobile agents,

both on
-
board agents and migrating agents
,

are implemented as
components
, t
he dynamic
agent
behavior
s

are
modeled through
multi
-
plane state machines

that
consist

of several functions, variables
,

and planes

[
64
,
65
]
. One plane represents one
behavior of
a mobile

agent corresponding to the agent’s role in the mobile multi
-
agent
system. A fundamental part of a plane is an automaton that controls the dynam
ic
agent
behavior using Event
-
Condition
-
Action (ECA) rules. ECA rules within the mobile multi
-
agent system are represented by the triplet

r
MMAS

=

(
E
,

C
,

A
)
,
where
E

is the event set,
C

is
the condition set and
A

are the atomic actions to be taken. An actio
n of an ECA rule,
transferring the automaton in the next state, is triggered when the event is detected and the
condition is satisfied
. In the implemented mobile multi
-
agent system, t
he events of an

16
/
46

agent, tri
ggering actions of other agents
, are
delivered
by the mobile agent execution engine
and
communicated asynchronously be
tween the agents using unicast
or broadcast inter
-
agent communication.


Fig.
4

shows an abridged diagram
of the
main classes of the mobile multi
-
agent system
,
illustrated using the Unified Modeling Language (UML).

The class diagram will be used in
the following subsecti
ons to
describe

the
prototype
implementation of the

on
-
board and
the
migrating agents

in more detail.



Fig.
4
.

Abridged UML class diagram o
f the mobile multi
-
agent system
.


On
-
board agents



17
/
46

As shown in
Fig.
4
, t
wo
categories of
on
-
board agents, the

AdministratorAgent
and the
TemperatureAnalysisAgent
, are prototypically implemented

into

the
wireless
nodes. The
AdministratorAgent
, running on every head and sensor node, is
responsible

for the
administration of
a
node
;
it manages, for example, hardware and network features and
provides information about memory usage, battery status
,

and radio configurati
ons. The
TemperatureAnalysisAgent
,
prototypically
embedded into
the
sensor node
s
,

is designed to
continuously collect and analyze temperature data from the observed structur
al system
. Its
purpose is to detect anomalies, i.e. abnormal temperature changes, b
ased on resource
-
efficient embedded algorithms.
F
or continuous temperature interrogations
,

the
TemperatureAnalysisAgent

periodically

senses

temperature data
by accessing the sensor
node’s temperature sensors
and compares the
recorded
measurements with thre
shold
values. Threshold values as well as
sensor
sampling rates can be modified by the agent
itself
or, through the local computer, by human individuals. In case of detected anomalies,
the
TemperatureAnalysisAgent

communicates the observed symptoms
from th
e sensor
node
to
a

head node and requests specialized migrating agents
to investigate

the observed
anomaly in detail. Simultaneously, the
TemperatureAnalysisAgent

increases the
temperature sampling rate. The dynamic agent behavior described is
modularly
im
plemented in the TemperatureAnalysisCompositeBehavior class
(
Fig.
4
)
in terms of

a
state machine
,

as
illustrated in
Fig.
5
.



18
/
46


Fig.
5
.

Dynamic agent behavior implemented as ECA automaton.


Migrating agents


As described earlier, t
he migrating

agents are capable of physically mig
rating on request of
the on
-
board agents from one node to another,
including their dynamic behavior, actual
state
,

and specific knowledge.
Upon arrival

on a sensor node, the migrating agents apply
their inherent analysi
s capabilities to achieve new information about the structural
condition and send the analysis results to the connected local computer. On the local
computer, the information
,

together with
further

information received from other sensor
nodes
,
is

assembled

to provide

a holistic picture about the current
global
condition

of the
monitored structural system
.


To implement the agent migration, t
he characteristics of the

Squawk Java virtual mach
ine
are advantageously utilized.

Squawk employs an application isolation mechanism
that

represents each

application as an object

being completely isolated from other objects.
Consequently, o
bjects

running on a
wireless sensor
node, such as migrating agents, can be

19
/
46

paused, serialized and



together with agent behaviors, agent states and required algorithms



physically

transferred to Squawk instances running on other nodes.


Assuming agent migration from a head node
H

(source node) to a regular sensor node
S

(destination node), the destination node is contacted by the source node through a message.
Next, a socket is opened based on the radiostream prot
ocol. The radiostream protocol,
which is

a peer
-
to
-
peer protocol implemented on top of the MAC layer

of the s
tandard
IEEE 802.15.4,

ensures

a reliable, buffered and stream
-
based communication between
S

and
H
. After having received the message from
the
source node
H
, the destination node
S

sends an acknowledgement back to the node
H
, whereupon
H

establishes a radi
ostream
connection with node
S
. The migrating agent
assembled on
node

H

is paused, hibernated,
serialized into a byte array and sent in a message to the destination node
S

(
including all
relevant data and execution state
)
. After having received the message
, the destination node
S

deserializes, dehi
bernates and activates the migrating agent.

Now operating on node
S
, the
migrating agent starts analyzing the local sensor data.


For the prototype implementation of the agent
-
based SHM system, the so called FFTAgent
is implemented; the FFTAgent is a migrating agent capable of analyzing modal properties
of structural systems based on fast Fourier transforms (FFT) that allow convertin
g sensor
measurements

from the time domain into the frequency domain

[
66
]
.

Specifically, f
or
calculating
the

frequency response functions

from time history data, the
FFTAgent

uses the
computationally efficient Cooley
-
Tukey
FFT
algorithm [
67
]

upon migrating to sensor node
S
.
Thus,
the
FFTAgent

is capable to
c
ompute

the
frequency response functions
of the

20
/
46

structure
as well as the
primary modal frequencies
at the given location, and it can compare
the
actual

frequencies to those
of the healthy
(i.e. undamaged) structure

at the location of
sensor node
S
.


As shown in
Fig.
4
, the
corresponding
agent behavior is encoded in the class
FFTAgent
Behavior, which aggregates the CooleyTukey class and is associated with the
Frequen
cyResponse class
that handles

the calculated frequency response function
s
.
Upon
completion of the on
-
board analyses, t
he diagnostic results obtained by the
FFTAgent

are
sent to the local computer for further processing.


Monitoring database and information

pool


Both the monitoring database and the information pool

of the agent
-
based SHM system
are
implemented using a relational MySQL database management system. The database
management system,
installed

on the local computer, is accessible
by human users an
d by
the mobile agents. To enable human users online accessing the database management
system, the “phpMyAdmin” online tool, which allows remotely performing administrative
tasks such as creat
ing, modifying or deleting data
, is integrated into the SHM syst
em
.
Furthermore,
to enable the mobile agents autonomously accessing the database
management system, the Java
-
based data access technology “JDBC” (Java Database
Connectivity) is utilized.



21
/
46

Technically, the monitoring database and the information pool
in the

current prototype
are
implemented in
one single

database

(
Fig.
6
)
.

The sensor data recorded and pre
-
processed by
the mobile agents is handled and stor
ed
on the wireless sensor nodes
in the form of Java
objects. The Java objects, after being transmitted from a wireless sensor node to the base
station, are converted into database tables, i.e. into relations, in which one single object
,
such as a measureme
nt,

is represented as a tuple (
a
1
,

...,

a
n
) being stored in a row of a
database table. The elements
a
i

of a tuple represent the attributes of
the

object
defining

its
properties, such as time and value of a measurement. The basic structure of the monitoring
database and the information pool is illustrated in
Fig.
6
. Exemplarily, one database table of
the monitoring database (“t_a1”) and one database table of the information pool
(“topology”)
are

shown. While the first table contains sensor data recorded by a sensor
labeled “t_a1”, the second table specifies the
system topology defining, for example, sensor
IDs, their locations within the monitored structural system, and the natural freque
ncies of
the undamaged structural system

observed
.



Fig.
6
.

Monitoring database and information pool.



22
/
46

As can be seen from
Fig.
6
, for

every sensor of the SHM system

one table is
designated
.
The reason for using one table for
each

sensor, as opposed to using one single table for
several sensors, is the autonomy of the wireless sensor network: Unlike conventional SHM
systems, in which
centralized
data acquisition units are deployed to collect sensor data from
different sensors in a
synchronized fashion, the mobile agents of the wireless sensor
network collect and analyze the sensor data independently from each other (and
, for
example,

change the sensor sampling rates if required).
C
onsequen
tly
, the measurements
are usually collected
asynchronously
at different timestamps, which makes the utilization of
different, independent database tables the most efficient alternative.


As an example,

the following listing
shows the modular implementation
enabling the
base
station
to
insert various

measurements, received as Java objects from the mobile agents,
into the monitoring database.


1

public

void

insertMeasurement(String id,
long

timestamp,
double

value){

2


...

3


try
{

4



Statement statement =
connection
.createStatement();

5



statement.executeUpdate(
"INSERT INTO `"
+
DATABASE
+
"`.`"
+





id+
"` (`"
+
TIMESTAMP
+
"`, `"
+
VALUE
+
"`)





VALUES ('"
+timestamp+
"', '"
+value+
"');"
);

6


}
catch

(SQLException sqlException){

7



...

8


}

11

}



23
/
46

In the example, the attributes “timestamp” and “value”

of a received measurement are
stored as a tuple in a row of the database table
, which is

specified by the attribute
“id”. Vice
versa, data stored in the monitoring database or in the information pool, if requested by the
mobile agents, is selected from
the database

in the same way
, converted into Java objects
,

and sent to the agents.


4
Laboratory

tests


Laboratory
tests are

conducted

serving

as a proof of concept of the
agent
-
based

SHM
system.
Corresponding

to the main goals of this research, two
major

objectives are pursued
when

conducting the
laboratory
tests.

First, system performance data is collected for
evaluating the resource efficiency achieved by the
SHM
system. Second, the
reliability

of
the
mobile multi
-
agent system embedded into the wireless
sensor nodes
is

examined with
respect to detecting changes in
the monitored structural system in
a decentralized
-
cooperative fashion
.

A test
set
-
up

is devised as follows: An
aluminum plate serving as a test
structure
is exposed to heat

that is
to be detected by the
on
-
board agents
in real

time.

Heat
is induced
,

because the natural frequencies vary more with temperature than with other
damage
. Furthermore
, the temperature increases slowly by time, which can be
advantageously used to verify the ca
pabilities of the mobile agents.

T
he structural condition
of the
test structure

that
may be changed due to the induced heat
is to be assessed by
migrating
agents
, which are

automatically assembled during runtime,
based on acceleration
response data taken f
rom the
test structure
.



24
/
46

4.1
Laboratory

test setup


The test structure, a

900

mm

×

540

mm aluminum plate (
t

= 0.635 mm) with one edge
being clamped
, is instrumented with an array of 9 precision temperature sensors

and 3
accelerometers
.

For
the

experimental test
, the
a
g
ent
-
based

SHM
system

is composed of

one
head node

and

three sensor nodes
forming a cluster as well as a base station
for
connect
ing

the wireless
sensor nodes

to the local computer. As
illustrated

in
Fig.
7
, the test structure is
subdivided into three monitoring sections
A
,
B
,

and

C
.

The sensor nodes
, labeled

S
A
,
S
B

and
S
C
,
are installed on the fixed end, in the middle,
and on the free end of the
structure
.
E
ach
of the three sensor nodes

is responsible for monitoring
that

section
, in which it is
installed
.

Every sensor node hosts the previously introduced (and relatively simple) on
-
board agents
,
namely

the
TemperatureAnal
ysis
A
gent and
the
AdministratorAgent.

T
he head node

hosts
,
besides an AdministratorAgent,

the prototypically implemented (and relatively complex)
migrating
FFTAgent
, such that th
e mobile multi
-
agent system
, in total,

is composed of the
agents
that are
situ
ated on the sensor nodes
and on the head node
.




25
/
46

Fig.
7
.

Overview of the prototype SHM system


For the acceleration measurements, the
integrated
three
-
axis accelerometers of the sensor
nodes
, labeled

as

a
A
,
a
B

and
a
C

in
Fig.
7
,

are utilized
.
For the temperature measurements,
three
external
t
emperature
s
ensors

(
t
A
,1
,
...
,

t
C
,3
)
are attached to every sensor node through
the analog inputs. For that purpose, LM335A precision temperature sensors, manufactured
by
National Semiconductor
, are selected, which operate

from
−40°C to 100°C

[
68
]
. The
LM335A sensors have

a
linear output

and produce

an output voltage of about 3

V, which
makes the LM335A a perfect match for the
sensor nodes
, whose analog inputs
are designed
to
measure
a

voltage range between 0

V

and 3

V.

Fig.
8

shows the

assembly of the wireless
sensor nodes and the external temperature sensor

as

well as the fully in
strumented test
structure.




(a) Wireless sensor node
and external
temperature sensor
.

(b) SHM system mounted on the test
structure.

Fig.
8
.

Installation of the
agent
-
based
SHM system for validation tests.



26
/
46

4.2
Auton
omous monitoring based on agent
migration


In the laboratory tests, the on
-
board agents operating
on the sensor nodes are

continuously
sensing temperature measurements
using

the
externally attached
temperature sensors. The
collected temperature measurements are locally analyzed based on

simple threshold
computations, forwarded to the local computer
,

an
d stored in

the

monitoring

database.
As
depicted in
Fig.
7
, heat is introduced

underneath the aluminum plate
in

monitoring section
B

below temperature

sensor
t
B
,1
.
For the laboratory tests, a

critical plate temperature
T
crit

=

60°C is pre
-
defined
,
indicating that an anomaly may occur and
that
further action

may be required by
the

SHM
system.

The value

T
crit
, that is pre
-
defined based on the
physical limitations of the nodes, is given to the on
-
board agents.
Fig.
9

shows

the
temperature distribution
at the

time

t

=

t
(
T
crit
)

as
calculated from

the temperature
measurements c
ollected by the on
-
board agents
.



Fig.
9
.

Temperature distribution (°C) on the upper side of the test structure.



27
/
46

The monitoring procedure

carried out in response to the
detection of the
abnormal increase
in temperature is shown in
Fig.
10
.
As
T
crit

is
first exceeded in monitoring section
B

(
Fig.
9
)
,
the
on
-
board agents of sensor

node
S
B

n
otify the head

node about the observed

abnormal

situation. As

soon as having received this information
, on the head node a migrating agent
is

individually
composed and instantiated in order to analyze the current condition of

the
test structure in more detail. First, the information pool installed on the local

comput
er is
queried for appropriate actions to be undertaken. In this example, the

Cooley
-
Tukey FFT
algorithm

[
67
]
, and consequently the
FFTAgent
,

is selected to analyze the structural
condition
by

determining the
actual
modal parameters of the test structure.
B
ased on the
information provided by the information pool
, modal properties of the undamaged test
structure, such as first modal frequencies,

are passed to the
FFTAgent on the head node
.
Furthermore, details on the migration are specified; i
n this case, sensor node
S
C
, instead of
sensor node
S
B

where the

anomaly has first been detected, is defined as the target node for
the agent

migration. The

reason is that sensor node
S
C

along with its internal accelerometer
is

installed at the free end of the aluminum test structure and can most likely generate

more
sensitive results than
S
B

when acquiring acceleration measurements for

analyzing the modal
pr
operties of the structure.



28
/
46


Fig.
10
.

Monitoring procedure automatically executed in consequence of the detected anomaly.


After having migrated to

sensor node
S
C
, the
FFTAgent

accesses the sensor node’s internal

accelerometer,
senses acceleration measurements

and computes the frequency response
function at location
S
C

from the acceleration time history data
.

Using the calculated
frequency response function, the agent identifies the first modal frequency as

1.6 Hz, which
does not

significantly differ from the first modal frequency of the undamaged
structure
.
The
diagrams in
Fig.
11

show the frequency response
function
s

of the test structure before and
after exposing it to heat.
It should be mentioned that the corresponding data sets used for
visualizing
the

diagrams hav
e been transmitted
solely
for
documentation
purpose
s

with
in
this study
.
The

results
of the on
-
board analyses
are sent by

the
FFTAgent

from sensor
node

S
C

via the base station
to the local computer, where they are stored in the form of a

safety
report

that
is

accessible by any responsible individuals

(
Fig.
12
)
.



29
/
46


Fig.
11
.

Frequency response function
for sensor location
S
C

before (left) and after exposing the test structure to heat
(right).



Fig.
12
.

Example safety report generated on behalf of
the

migrating agent.


4.3
Experimental results


In

the
laboratory

tests, performance data collected from the agent
-
based SHM system
has
been
compared to current approaches commonly implemented in
wireless
SHM systems.
In
particular, t
he

size of
the
transmitted data
sets and the utilized
internal node memory
have

been

re
corded.
As a result of the performance
analyses
, a total of 71 kB on
-
board memory

30
/
46

was needed for the
migration
-
based
monitoring procedure
conducted

in the laboratory
tests
,
including agent migration and on
-
board FFT analysis
.

More specifically, the o
bjects

representing the acceleration measurements
on the nodes, which are
required for data
analysis
,

had a size of 0.02 kB (17 B) each
,

resulting in

a total of 67.8 kB needed for the
on
-
board FFT analysis. 3.2 kB (3,276 B) were needed for all other objects that

were
automatically created on a wireless sensor node within the migration
-
based monitoring
procedure

(including migrating agent, agent behavior, and further agent attributes)
.
As a
result
, a reduction of wirelessly transmitted data of more than 95%
was

ac
hieved
as
compared
with

conventional approaches
that send
the collected sensor data
, here 67.8 kB,

to a remote computer

for centralized data analyses.


While the performance data on the memory consumption and on the
data transmission

recorded in the laboratory
tests

is very accurate
, the laboratory
tests


even if conducted
several times



do

not provide performance data

on the power consumption
reliable enough
to be
representative; due to the
limited quantities of
performance
data tha
t can be collected

in

the

laboratory tests
,
the measurable power consumptions
are

too small to be captured
accurately.
Therefore, performance tests on the power consumption were conducted in

addition

to the laboratory
tests

simulating the
migration
-
based
d
ata

processing. The

performance te
sts on the power consumption
in
cluded 1
00 migration procedures

in order to
obtain
sufficient performance data. In total,
3

tests
were

conducted, resulting in
3
00
migration procedures.

E
ssentially,
t
he p
erformance tests

wer
e composed of a
simulated
migration
-
based
procedure
(
Fig.
10
)
and
, for comparison,

a

conventional
monitoring
procedure,
in which all raw data
was

sent to a central server. As a result
,
the
battery

31
/
46

capacity consumed
in
one

migration
-
based procedure
was

on the average

0.14

mAh, as
opposed to 3.70 mAh

consumed

in the conventional
case
, which is a 96%
reduction of
power consumption.


The reasons for th
e achieved resource efficiency are twofold: First, sensing and on
-
board
storage of unnecessary measurements as well as wireless transmissions of these data sets
are largely avoided. Second, on
-
board calculations are only executed by specialized
migrating a
gents if anomalies are suspected.
As
described earlier
, both the collected
measurements and the migrating agents are
technically
realized as Java objects
. These
objects
are

not a priori initialized on a
wireless
sensor node
.

R
ather, the initialization of
i
ndividual objects
is

performed on the head nodes


only if necessary


using the
central
information pool
. As a result,
no on
-
board memory is allocated for the objects unless a
migrating agent has been sent

from a head node

to the respective sensor node
.
F
urthermore,
all objects related to the migration
-
based monitoring

procedure, if no longer needed, are
automatically marked and swept by the garbage collector of the embedded virtual
machines.


Last but not least, the
central
information pool installed

on the local computer

incorporates

global information on the observed structure and
a catalog of
engineering algorithms

suitable for efficiently analyzing
suspected
anomal
ies
.
As has been demonstrated in the
laboratory tests
, i
t
was
possible to achieve a
holistic picture during runtime, which
would

not be
possible

without dynamic agent migration using static objects and algorithms that
are stored on each
wireless
sensor node a priori.


32
/
46


5
Summary and c
onclusions


In this paper, t
he
design

and
implementation

of an agent
-
based wireless
structural health
monitoring
system
,

comprising

of a wireless sensor network and software programs
running on a connected computer
, have been presented
.
To achieve resource efficiency and
reliability of the agent
-
based SHM
system, a

mobile multi
-
agent system composed of
several autonomous software entities
, refer
r
ed to as

“mobile agents”, has been embedded
into
the

wireless sensor network.
Whereas some mobile agents (“on
-
board agents”)
permanently reside

on the wireless sens
or nodes for continuous
, autonomous

monitoring,
other
mobile agents
(“migrating agents”)
physically
migrate

from one sensor node to
another on demand.

Instead of
h
aving extensive collections of engineering algorithms
installed on every sensor node a priori
,
the
specialized
migrating agents

are requested
in
real

time if anomalies
of the monitored structural system are suspected. Without the need
for transmitting large amounts of sensor data, the
migrating
agents


assembled during
runtime and provided with s
pecific
expert knowledge



execute

individual engineering
algorithms directly on
the
sensor
node
s analyzing

the local sensor data
according
to the

suspected anomaly
.


For the

proof of concept of the proposed approach,
laboratory tests
have been conducted
(
i)
to collect

system performance data for evaluating the resource efficiency achieved by the
prototype
SHM

system

and (ii) to examine the
reliability of the mobile multi
-
agent system
embedded

into the wireless sensor nodes
.
In the
laboratory

tests, t
he agent
-
based SHM

33
/
46

system has been installed on an aluminum plate
serving as a test structure
.

Because natural
frequencies vary more with temperature than with damage, heat has
been
induced

into the
test structure

to evaluate the dynamic
, cooperative

behav
ior of the mobile agents. As a
result,
t
he
changes in
temperature
, slowly increasing over time,

have
been
detected by the
mobile agents
.
Furthermore, t
he condition of the
test structure has
autonomously

been
assessed by
migrating
agents

based on modal anal
yses of acceleration response data

recorded from the structure

on demand
.


In summary, the resource consumption of the wireless sensor nodes, compared with
traditional approaches commonly implemented in wireless SHM systems, could
significantly be reduced.

The wirelessly transmitted

data
and the power consumption have
been reduced by 95% and by 96%, respectively, as compared with transmitting all raw
sensor data to a remote computer for central data analysis. At the same time, as compared
with conventional
approaches hosting data analysis algorithms directly on board, the
accuracy and the reliability of monitoring could be increased, because the agent
-
based
condition assessment is performed in a distributed
-
cooperative fashion incorporating global
properties

of the observed structural system taken from the information pool. Therefore,
with respect to
scaling

up from the laboratory
tests

to relatively complex real
-
world
SHM
problem
s, the efficiency of the proposed approach could likely increase with increasing

complex
ity of

the SHM system, the observed structure, and the
collection of data analysis
and structural monitoring algorithms
.


Acknowledgments


34
/
46


This research is partially funded by the German Research Foundation (DFG) under grant
SM 281/1
-
1
and

under grant

SM 281/2
-
1
,

awarded to

Dr. Kay Smarsly. The research is also
partially supported by the U
.
S
.

National Science Foundation

under
g
rant
CMMI
-
0824977
,

awarded to Professor Kincho H. Law
. Any opinions expressed in this paper are those of the
author
s and do not necessarily reflect the opinions of the German Research Foundation and
the National Science Foundation.


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Ni
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