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

Wireless 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 on the sensor nodes

can consume a significant amount of the inherently restricted node resources. This paper

presents an agent migration approach towards resource-efficient wireless sensor networks.

Autonomous software agents, referred to as “on-board agents”, are embedded into the

wireless sensor nodes employed for structural health monitoring performing simple

resource-efficient routines to continuously analyze, aggregate, and communicate 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;

2/46

(“migrating agents”) that physically migrate to the sensor nodes to analyze the suspected

anomaly on demand. In addition to the localized data analyses, a central information pool

available on the central server is accessible by the software agents (and by human users),

facilitating a distributed-cooperative assessment of the global condition of the monitored

structure. 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], the 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 than $2.6 trillion (€2.0 trillion) are

needed. Other 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 their own structural conditions [3-5].

Structural health monitoring systems, consisting of sensors, data acquisition units,

computer systems and connecting cables, are designed to detect structural changes before

they reach critical 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 costs for management, maintenance and repair throughout the

structures’ life cycles [6]. However, in conventional SHM systems 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 alternative 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, aggregate and

communicate vast amounts of sensor data. The sensor nodes, integrating advanced

embedded systems technologies, are capable of self-interrogating collected sensor data for

signs of structural changes [12, 13]. In essence, the sensor data is first analyzed and

aggregated on the nodes, from high-bandwidth raw sensor data to low-bandwidth streams

of processed results. The analysis results are then transferred 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 data 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 of complex

engineering analyses. Therefore, there have been active research efforts in the past several

years towards reducing the power consumption of wireless sensor nodes by optimizing the

sensor node hardware as well as the software embedded into the nodes. For example, new

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 structural 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 changes in the global

structural response and behavior (such as altered stiffnesses and structural stability) should

also be considered in 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) strategies are needed to assess local and global structural changes.

The goals of the research presented herein are twofold. First, the resource consumption of

the sensor nodes is to be reduced with respect to memory utilization and power

5/46

consumption. Second, a SHM system prototype, capable of holistically monitoring local as

well as global structural phenomena, is to be implemented. 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

as well as the performance of the prototype system. The 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 on mobile multi-agent 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 application domains of multi-agent systems includes, e.g., process control,

air traffic control, business process management, health care, water resource management,

traffic and transportation engineering, building control, power engineering applications, and

structural design [18-25]. More 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 the term “agent” has often been misused as well as overused [30], one definition

has been widely accepted in the artificial intelligence community; the “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 over its actions and internal states (“autonomy”),

interacts with other agents through agent communication languages (“social

ability”),

perceives its environment, e.g. the physical world or a software environment,

and responds in a timely fashion to environmental changes (“reactivity”) and

exhibits goal-directed behavior by taking initiatives (“pro-activeness”).

Multiple interacting software agents in association with the agent environment 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

In the last decade, considerable success has been reported in porting multi-agent technology

on mobile devices such as cell phones, smart phones, or wireless sensor nodes (“mobile

multi-agent systems”) [32-34]. The distinctive strengths of multi-agent systems –

scalability, modularity, flexibility and extensibility – are utilized in mobile applications

facilitating distributed-cooperative problem solving in highly dynamic environments. To

adequately deal with the constraints associated with developing applications on small

devices, the majority of mobile 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].

CLDC, a fundamental part of the “Java Platform, Micro Edition” (Java ME), defines the

most basic libraries and virtual machine features for resource-constrained devices. It is

worth mentioning that CLDC, although offering all major advantages provided by the Java

language such as object orientation, portability, robustness and security, in its current

version 1.1 requires only 160 kB of non-volatile memory to be allocated for the CLDC

libraries and for the Java virtual machine, and needs only 32 kB of volatile memory for the

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

needed 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 MB1 160 kB

8/46

1Windows 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 platforms for mobile devices essentially provide lightweight subsets of

Java classes supporting basic agent services for communication, for multitasking, or – if

embedded into wireless sensor nodes – for accessing the node resources (e.g. sensors or

memory). Detailed reviews as well as comparisons of architectures, programming models

and performances 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 behavior 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 code 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 environment, 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 attention.

9/46

3 An 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 information pool, facilitating 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

As de

wirel

of mo

by a h

netwo

interf

and i

comp

local

Furth

conne

epicted in F

less sensor n

obile agents

head node,

ork features

face betwee

information

puter for per

computer

hermore, the

ected to the

Fig. 1, the ag

nodes, a bas

s and forms

which perfo

s, but does n

en the wirel

n, assembled

rsistent stora

are commu

e local com

wireless se

Fig. 1.

gent-based S

se station, a

a cluster to

orms admin

not collect o

less sensor n

d by the ag

age and furt

municated vi

mputer prov

ensor networ

. Architecture o

10/46

SHM system

and the loca

gether with

nistrative tas

or analyze se

nodes and t

gents, from

ther process

ia the base

vides user in

rk.

f the agent-base

m is compos

al computer.

other senso

sks, such as

ensor data. T

the local co

m the wirele

sing. Vice v

e station to

nterfaces, a

ed wireless SHM

sed of three

. Each senso

or nodes. A

managemen

The base sta

omputer, for

ess sensor n

versa, comm

o the wirel

and external

M system.

basic comp

or node hos

cluster is m

nt of hardw

ation, servin

rwards sens

nodes to th

mands sent fr

less sensor

l resources

ponents,

sts a set

managed

ware and

ng as an

sor data

he local

rom the

nodes.

can be

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

“migrating agents”, are embedded into 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, analyze,

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 rates. Thereupon, specific algorithms and further

knowledge, required for more comprehensive analyses 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 requested on demand to physically migrate to the

respective sensor node. Automatically composed during runtime, the migrating agents are

assembled with the required algorithms and specific expert knowledge, which enables the

agents making appropriate decisions directly on a wireless sensor node.

3.2 H

For t

SunS

pract

advan

usual

sensin

Hardware of

the prototy

SPOT sensin

icability an

ntage, unlik

lly written i

ng units com

Fig. 2. Prop

f the wireles

ype implem

ng units are

nd performa

ke common

in low-leve

mprise of a

posed operationa

ss sensor ne

mentation of

deployed [

ance in a m

n embedded

l native lan

fully capabl

12/46

al workflow in

etwork

f the wirel

49, 50]. Th

multitude of

d application

nguages suc

le J2ME CL

the agent-based

ess sensor

e sensing un

scientific p

ns for wirel

ch as C/C++

LDC 1.1 Jav

d SHM system.

network, J

nits have al

projects [51-

less sensor

+ and assem

va virtual m

Java-based

lready prove

-57]. As a

networks t

mbly langua

achine.

Oracle

en their

distinct

that are

age, the

13/46

The computational core of the sensing units 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 host port, USB device port, Ethernet MAC,

programmable I/O controller, serial peripheral interface controller, I2C bus, etc. Memory of

the sensing units 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, operating on the

2.4 GHz unregulated industrial, scientific and medical (ISM) band. Power supply is

provided by an internal, rechargeable 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 units [59].

Consisting of a micro-electro-mechanical system (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 50 μg/Hz1/2 enabling a resolution of 0.5 mg over

100 Hz. In addition to the three-axis accelerometer, the sensing units comprise of an

integrated temperature sensor operating from −40°C to 125°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 multiple embedded applications on the sensing

units [60]. Operating system functionalities are provided by the Squawk virtual machine,

14/46

which executes directly out of the flash memory. The 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 units 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, whereas 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, extendibility and maintainability as well as an ease of debugging is achieved,

which makes Squawk a powerful basis for prototyping mobile multi-agent systems for

wireless structural health monitoring.

3.3 Prototype implementation 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

dynam

inter-

migra

Whil

comp

that c

behav

syste

behav

agent

the c

transf

condi

mic manage

-agent comm

ation of age

e the mobil

ponents, the

consist of s

vior of a m

m. A funda

vior using E

t system are

condition se

ferring the a

ition is sati

ement of se

munication,

ents.

Fig. 3. Mai

le agents, b

e dynamic a

everal func

mobile agen

amental par

Event-Cond

e represente

et and A ar

automaton i

isfied. In th

ensor nodes

and (vii) th

in components o

both on-boar

gent behavi

tions, varia

nt correspon

rt of a plan

dition-Action

ed by the tri

e the atomi

in the next s

he impleme

15/46

s, (vi) the m

he mobile ag

of the MAPS-ba

rd agents an

iors are mod

ables, and pl

nding to the

ne is an aut

n (ECA) ru

iplet rMMAS =

ic actions t

state, is trig

ented mobil

mobile agen

gent migrati

ased multi-agen

nd migratin

deled throug

lanes [64, 6

e agent’s ro

tomaton tha

ules. ECA r

= (E, C, A),

to be taken

ggered when

le multi-age

nt communic

ion manage

nt architecture.

ng agents, ar

gh multi-pla

65]. One pla

ole in the m

at controls t

rules within

where E is

n. An action

n the event

ent system,

cation chan

er for execut

re impleme

ane state ma

ane represe

mobile mult

the dynamic

the mobile

the event s

n of an EC

is detected

, the events

nnel for

ting the

ented as

achines

nts one

ti-agent

c agent

e multi-

set, C is

A rule,

and the

s of an

agent

and c

agent

Fig. 4

illust

the fo

migra

On-b

t, triggering

communicat

t communic

4 shows an

rated using

ollowing sub

ating agents

board agents

g actions of o

ted asynchr

cation.

n abridged d

the Unified

ubsections to

s in more de

Fig. 4. Abri

ts

other agents

ronously be

diagram of

d Modeling

o describe th

etail.

idged UML clas

16/46

s, are delive

etween the

the main cl

Language (

he prototype

ss diagram of th

ered by the m

agents usin

lasses of th

(UML). The

e implemen

he mobile multi

mobile agen

ng unicast o

he mobile m

e class diagr

ntation of the

i-agent system.

nt execution

or broadcas

multi-agent s

ram will be

e on-board

n engine

st inter-

system,

used in

and the

17/46

As shown in Fig. 4, two 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 configurations. The

TemperatureAnalysisAgent, prototypically embedded into the sensor nodes, is designed to

continuously collect and analyze temperature data from the observed structural system. Its

purpose is to detect anomalies, i.e. abnormal temperature changes, based on resource-

efficient embedded algorithms. For continuous temperature interrogations, the

TemperatureAnalysisAgent periodically senses temperature data by accessing the sensor

node’s temperature sensors and compares the recorded measurements with threshold

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 the 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

implemented in the TemperatureAnalysisCompositeBehavior class (Fig. 4) in terms of a

state machine, as illustrated in Fig. 5.

Migr

As de

the o

state,

their

condi

comp

nodes

moni

To im

are a

repre

Cons

rating agent

escribed ear

on-board ag

, and specif

inherent a

ition and s

puter, the in

s, is assemb

itored struct

mplement th

advantageou

esents each

sequently, ob

Fig. 5. D

ts

rlier, the mi

gents from o

fic knowled

analysis ca

end the an

nformation,

bled to prov

tural system

he agent mi

usly utilized

application

bjects runni

Dynamic agent b

grating agen

one node to

dge. Upon a

apabilities t

nalysis resul

together w

vide a holis

m.

igration, the

d. Squawk

n as an obj

ing on a wi

18/46

behavior implem

nts are capa

o another, i

arrival on a

to achieve

lts to the c

with further

stic picture a

e characteris

employs a

ject being

reless senso

mented as ECA

able of phys

including th

sensor nod

new infor

connected l

information

about the cu

stics of the

an applicati

completely

or node, suc

automaton.

sically migra

heir dynami

de, the migr

rmation abo

local compu

n received

urrent globa

Squawk Jav

ion isolation

isolated fr

ch as migrat

ating on req

ic behavior,

rating agent

out the str

uter. On th

from other

al condition

va virtual m

n mechanis

rom other o

ting agents,

quest of

, actual

ts apply

ructural

he local

r sensor

n of the

machine

sm that

objects.

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 protocol. The radiostream protocol,

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

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 radiostream

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, dehibernates 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 converting sensor

measurements from the time domain into the frequency domain [66]. Specifically, for

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 compute 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

FFTAgentBehavior, which aggregates the CooleyTukey class and is associated with the

FrequencyResponse class that handles the calculated frequency response functions. Upon

completion of the on-board analyses, the 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 and 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 creating, modifying or deleting data, is integrated into the SHM system.

Furthermore, to enable the mobile agents autonomously accessing the database

management system, the Java-based data access technology “JDBC” (Java Database

Connectivity) is utilized.

Techn

imple

the m

objec

statio

such

datab

prope

datab

the m

(“top

labele

IDs,

the un

nically, the

emented in o

mobile agen

cts. The Jav

on, are conv

as a measu

base table. T

erties, such

base and the

monitoring

pology”) are

ed “t_a1”, th

their locatio

ndamaged s

monitoring

one single d

nts is handle

va objects, a

verted into

urement, is

The element

as time and

e information

database

e shown. W

the second ta

ons within t

structural sy

F

g database a

database (Fi

ed and store

after being t

database ta

represented

ts ai of a tu

d value of a

n pool is illu

(“t_a1”) an

While the fir

able specifi

the monitor

ystem observ

Fig. 6. Monitori

21/46

and the info

ig. 6). The s

ed on the w

transmitted

ables, i.e. in

d as a tupl

uple represen

measureme

ustrated in F

nd one da

rst table co

es the syste

red structura

ved.

ing database and

rmation poo

ensor data r

wireless sens

from a wir

nto relations

e (a1, ..., an

nt the attrib

ent. The bas

Fig. 6. Exem

atabase tab

ontains sens

em topology

al system, a

d information p

ol in the cur

recorded and

sor nodes in

reless senso

s, in which

n) being sto

butes of the

sic structure

mplarily, on

le of the

or data rec

y defining, fo

and the natu

pool.

rrent protot

d pre-proce

n the form

or node to th

one single

ored in a ro

object defin

e of the mon

ne database t

informatio

orded by a

for example,

ural frequen

type are

ssed by

of Java

he base

object,

ow of a

ning its

nitoring

table of

n pool

a sensor

, sensor

ncies of

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). Consequently, 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 capabilities of the mobile agents. The 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 from the test structure.

4.1 L

The t

being

accel

head

the w

subdi

SC, ar

of the

Every

name

besid

migra

agent

Laboratory t

test structur

g clamped,

lerometers. F

node and th

wireless sens

ivided into t

re installed

e three sens

y sensor nod

ely the Tem

des an Adm

ating FFTA

ts that are si

test setup

re, a 900 m

is instrume

For the exp

hree sensor

sor nodes to

three monit

on the fixe

sor nodes is

de hosts the

mperatureAn

ministratorAg

Agent, such t

ituated on th

mm × 540 m

ented with

erimental te

nodes form

o the local c

oring sectio

d end, in th

responsible

e previously

nalysisAgent

gent, the pr

that the mo

he sensor no

Fig. 7. Overvie

24/46

mm aluminu

an array of

est, the agen

ming a cluste

computer. A

ons A, B, and

he middle, a

e for monito

y introduced

t and the A

rototypically

bile multi-a

odes and on

ew of the protot

um plate (t

f 9 precisio

nt-based SH

er as well as

As illustrated

d C. The se

and on the f

oring that se

d (and relati

Administrato

y implemen

agent system

the head no

type SHM syste

= 0.635 m

on temperat

HM system i

s a base stat

d in Fig. 7, t

nsor nodes,

free end of t

ection, in w

ively simple

orAgent. The

nted (and re

m, in total, i

ode.

em

mm) with on

ture sensors

s composed

tion for con

the test stru

labeled SA,

the structure

which it is in

e) on-board

e head node

elatively co

is composed

ne edge

s and 3

d of one

nnecting

ucture is

, SB and

e. Each

nstalled.

agents,

e hosts,

omplex)

d of the

For t

nodes

three

the an

by N

LM3

make

to me

senso

struct

(a

4.2 A

the accelera

s, labeled a

external te

nalog input

National Sem

35A sensor

es the LM33

easure a vol

or nodes an

ture.

a) Wireless sen

tempera

Autonomous

ation measur

as aA, aB and

emperature s

ts. For that p

miconductor

rs have a lin

35A a perfec

ltage range b

nd the exter

sor node and ex

ature sensor.

Fig. 8. Insta

s monitoring

rements, th

d aC in Fig

sensors (tA,1

purpose, LM

r, are select

near output

ct match for

between 0 V

rnal temper

xternal

allation of the a

g based on

25/46

e integrated

. 7, are util

1, ..., tC,3) ar

M335A prec

ted, which

and produc

r the sensor

V and 3 V. F

rature senso

(b) SHM

agent-based SHM

agent migr

d three-axis

ized. For th

re attached

cision temp

operate from

ce an output

r nodes, who

Fig. 8 show

or as well a

M system mount

M system for va

ration

accelerome

he temperatu

to every se

erature sens

m −40°C to

t voltage of

ose analog i

ws the assem

as the fully

ted on the test st

alidation tests.

eters of the

ure measure

ensor node t

sors, manuf

o 100°C [6

f about 3 V,

inputs are de

mbly of the w

y instrument

tructure.

e sensor

ements,

through

factured

8]. The

, which

esigned

wireless

ted test

In the

sensin

collec

comp

depic

B be

Tcrit =

may

physi

temp

meas

e laboratory

ng tempera

cted tempe

putations, fo

cted in Fig.

elow temper

= 60°C is p

be required

ical limitati

erature dis

surements co

y tests, the o

ature measur

erature mea

orwarded to

7, heat is in

rature sens

re-defined,

d by the SH

ions of the

stribution a

ollected by t

Fig. 9. Tempe

on-board ag

rements usi

asurements

o the local c

ntroduced u

or tB,1. For

indicating

HM system

e nodes, is

at the time

the on-boar

erature distribut

26/46

gents operat

ing the exte

are locally

computer, an

underneath t

r the labora

that an ano

m. The valu

given to t

e t = t(Tcrit)

rd agents.

tion (°C) on the

ting on the s

ernally attac

y analyzed

nd stored in

the aluminu

atory tests,

omaly may

ue Tcrit, that

the on-boar

) as calcu

e upper side of t

sensor node

ched temper

based on

n the monito

um plate in m

a critical

occur and

t is pre-defi

rd agents.

ulated from

the test structure

es are contin

rature senso

simple th

oring datab

monitoring

plate temp

that further

fined based

Fig. 9 sho

m the temp

e.

nuously

ors. The

hreshold

ase. As

section

perature

r action

on the

ows the

perature

27/46

The monitoring procedure carried out in response to the detection of the abnormal increase

in temperature is shown in Fig. 10. As Tcrit is first exceeded in monitoring section B (Fig.

9), the on-board agents of sensor node SB notify 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 computer 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. Based 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; in this case, sensor node SC, instead of

sensor node SB where the anomaly has first been detected, is defined as the target node for

the agent migration. The reason is that sensor node SC 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 SB when acquiring acceleration measurements for analyzing the modal

properties of the structure.

After

accel

funct

frequ

does

diagr

after

visua

this s

SC vi

repor

Fig. 10.

r having mig

lerometer, s

tion at loca

uency respon

not signific

rams in Fig.

exposing it

alizing the d

study. The r

ia the base s

rt that is acc

Monitoring pro

grated to se

senses acce

ation SC fro

nse function

cantly differ

. 11 show th

t to heat. It

diagrams ha

results of the

station to th

cessible by a

ocedure automat

nsor node S

eleration me

om the acc

n, the agent

from the fir

he frequenc

should be m

ave been tra

e on-board a

he local com

any responsi

28/46

tically executed

SC, the FFTA

easurements

celeration t

identifies th

rst modal fr

cy response

mentioned t

ansmitted so

analyses are

mputer, whe

ible individu

d in consequenc

Agent acces

s and comp

time history

he first mod

requency of

functions o

that the cor

olely for do

e sent by the

ere they are

uals (Fig. 12

ce of the detecte

sses the sen

putes the fr

y data. Usi

dal frequenc

f the undama

of the test st

rresponding

ocumentatio

e FFTAgent

stored in th

2).

ed anomaly.

nsor node’s i

requency re

ing the cal

cy as 1.6 Hz

aged structu

tructure befo

data sets u

on purposes

t from senso

he form of a

internal

esponse

lculated

z, which

ure. The

fore and

used for

s within

or node

a safety

Fig

4.3 E

In the

been

partic

been

g. 11. Frequency

Experimenta

e laboratory

compared t

cular, the si

recorded. A

y response funct

Fig. 12. Exam

al results

y tests, perf

to current ap

ize of the tr

As a result o

tion for sensor l

mple safety repo

formance da

pproaches c

ansmitted d

of the perfo

29/46

location SC befo

(right).

ort generated on

ata collected

commonly im

data sets and

rmance ana

ore (left) and aft

n behalf of the m

d from the

mplemented

d the utilize

alyses, a tota

fter exposing the

migrating agent

agent-based

d in wireless

ed internal n

al of 71 kB

e test structure t

t.

d SHM syst

s SHM syst

node memor

on-board m

to heat

tem has

tems. In

ry have

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 objects

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 achieved 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 that 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 data processing. The

performance tests on the power consumption included 100 migration procedures in order to

obtain sufficient performance data. In total, 3 tests were conducted, resulting in 300

migration procedures. Essentially, the performance tests were 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 the 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 agents 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. Rather, the initialization of

individual 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. Furthermore,

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 anomalies. As has been demonstrated in the

laboratory tests, it 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 conclusions

In this paper, the 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, referred to as “mobile agents”, has been embedded

into the wireless sensor network. Whereas some mobile agents (“on-board agents”)

permanently reside on the wireless sensor nodes for continuous, autonomous monitoring,

other mobile agents (“migrating agents”) physically migrate from one sensor node to

another on demand. Instead of having 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 specific expert knowledge – execute individual engineering

algorithms directly on the sensor nodes 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, the 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 behavior of the mobile agents. As a

result, the changes in temperature, slowly increasing over time, have been detected by the

mobile agents. Furthermore, the condition of the test structure has autonomously been

assessed by migrating agents based on modal analyses 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

problems, the efficiency of the proposed approach could likely increase with increasing

complexity 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 grant CMMI-0824977,

awarded to Professor Kincho H. Law. Any opinions expressed in this paper are those of the

authors and do not necessarily reflect the opinions of the German Research Foundation and

the National Science Foundation.

References

1 American Society of Civil Engineers. Failure to Act: The Economic Impact of

Current Investment Trends in Surface Transportation Infrastructure. Report. Reston,

VA, USA: American Society of Civil Engineers; 2011.

2 Urban Land Institute and Ernst & Young. Infrastructure 2012: Spotlight on

Leadership. Report. Washington, DC, USA: Urban Land Institute; 2012.

3 Ni YQ, Li B, Lam KH, Zhu DP, Wang Y, Lynch JP, et al. In-construction vibration

monitoring of a supertall structure using a long-range wireless sensing system.

Smart Struct Syst 2011; 7:83-19.

4 Chong KP, Carino NJ, Washer G. Health monitoring of civil infrastructures, Smart

Mater Struct 2003; 12:483-11.

35/46

5 Farrar CR, Sohn H, Hemez FM, Anderson MC, Bement MT, Cornwell PJ, et al.

Damage Prognosis: Current Status and Future Needs. Report. Los Alamos, NM,

USA: Los Alamos National Laboratory; 2003.

6 Smarsly K, Law KH, Hartmann D. Towards Life-Cycle Management of Wind

Turbines based on Structural Health Monitoring. In: Proceedings of the First

International Conference on Performance-Based Life-Cycle Structural Engineering.

Hong Kong, China; 2012. pp. 1066-9.

7 Lynch JP, Law KH., Kiremidjian AS, Carryer E, Farrar CR, Sohn H, et al. Design

and performance validation of a wireless sensing unit for structural monitoring

applications, Struct Eng Mech 2004; 17:393-16.

8 Spencer BF, Ruiz-Sandoval ME, Kurata N. Smart sensing technology: opportunities

and challenges, Struct Control Hlth 2004; 11:349-20.

9 Zimmerman AT, Shiraishi M, Swartz RA, Lynch JP. Automated modal parameter

estimation by parallel processing within wireless monitoring systems. J Infrastruct

Syst 2008; 14:102-12.

36/46

10 Rice JA, Mechitov K, Sim SH, Nagayama T, Jang S, Kim R, et al. Flexible smart

sensor framework for autonomous structural health monitoring. Smart Struct Syst

2010; 6:423-16.

11 Kim J, Swartz RA, Lynch JP, Lee JJ, Lee CG. Rapid-to-deploy reconfigurable

wireless structural monitoring systems using extended-range wireless sensors.

Smart Struct Syst 2010; 6:505-20.

12 Lynch JP, Loh K. A Summary Review of Wireless Sensors and Sensor Networks

for Structural Health Monitoring. Shock Vib 2006; 38:91-38.

13 Wang Y, Swartz RA, Lynch JP, Law KH, Lu KC, Loh CH. Decentralized civil

structural control using real-time wireless sensing and embedded computing. Smart

Struct Syst 2007; 3:321-20.

14 Tang Q, Yang L, Giannakis GB, Qin, T. Battery Power Efficiency of PPM and FSK

in Wireless Sensor Networks. IEEE T Wirel Commun 2007; 6:1308-11.

15 Prakash Y, Gupta SKS. Energy Efficient Source Coding and Modulation for

Wireless Applications. In: Proceedings of the Wireless Communications and

Networking Conference. New Orleans, LA, USA; 2003. pp. 212-6.

37/46

16 Laneman JN, Tse DNC, Wornell GW. Cooperative Diversity in Wireless Networks:

Efficient Protocols and Outage Behavior. IEEE T Inform Theory 2004; 50:3062-19.

17 Nwana HS, Ndumu DT. A brief introduction to software agent technology. In:

Jennings NR, Wooldridge MJ, editors. Agent Technology – Foundations,

Applications, and Markets, Berlin, Germany: Springer; 1998, p. 29-19.

18 Jennings NR, Wooldridge MJ. Applications of Intelligent Agents. In: Jennings NR,

Wooldridge MJ, editors. Agent Technology – Foundations, Applications, and

Markets, Berlin, Germany: Springer; 1998, p. 3-26

19 Fuhrmann T, Neuhofer B. Multi-agent systems for environmental control and

intelligent buildings. Report. Salzburg, Austria: Department of Computer Science,

University of Salzburg; 2006.

20 Gunkel A. The application of multi-agent systems for water resources research –

Possibilities and limits. Diploma Thesis. Freiburg, Germany: Institut für Hydrologie

der Albert-Ludwigs-Universität Freiburg i. Br.; 2005.

21 Klügl F, Bazzan ALC. Multi-Agent Systems for Traffic and Transportation

Engineering. 1st ed. Hershey, PA, USA: Information Science Reference; 2009.

38/46

22 Qiao B, Liu K, Guy C. A multi-agent system for building control. In: Proceedings

of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

Hong Kong, China; 2006. pp. 653-7.

23 McArthur SDJ, Davidson EM, Catterson VM, Dimeas AL, Hatziargyriou ND,

Ponci F, et al. Multi-Agent Systems for Power Engineering Applications—Part I:

Concepts, Approaches, and Technical Challenges. IEEE T Power Syst 2007;

22:1743-10.

24 McArthur SDJ, Davidson EM, Catterson VM, Dimeas AL, Hatziargyriou ND,

Ponci F, et al. Multi-Agent Systems for Power Engineering Applications—Part II:

Technologies, Standards, and Tools for Building Multi-agent Systems, IEEE T

Power Syst 2007; 22:1753-7.

25 Bilek J, Mittrup I, Smarsly K, Hartmann D. Agent-based Concepts for the Holistic

Modeling of Concurrent Processes in Structural Engineering. In: Proceedings of the

10th ISPE International Conference on Concurrent Engineering. Madeira, Portugal;

2003. pp. 47-7.

26 Smarsly K, Lehner K, Hartmann D. Structural Health Monitoring based on

Artificial Intelligence Techniques. In: Proceedings of the International Workshop on

Computing in Civil Engineering. Pittsburgh, PA, USA; 2007. pp. 111-8.

39/46

27 Mittrup I, Smarsly K, Hartmann D, Bettzieche V. An Agent-based Approach to

Dam Monitoring. In: Proceedingy of the 20th CIB W78 Conference on Information

Technology in Construction. Auckland, New Zealand; 2003. pp. 239-8.

28 Smarsly K, Law KH, Hartmann D. Multiagent-Based Collaborative Framework for

a Self-Managing Structural Health Monitoring System. J Comput Civil Eng 2012;

26:76-15.

29 Smarsly K, Law KH, Hartmann D. Implementing a Multiagent-Based Self-

Managing Structural Health Monitoring System on a Wind Turbine. In: Proceedings

of the 2011 NSF Engineering Research and Innovation Conference. Atlanta, GA,

USA; 2011. Digital proceedings.

30 Nwana HS. Software agents: An overview. Knowl Eng Rev 1996; 11:1-41.

31 Wooldridge MJ, Jennings NR. Intelligent agents: Theory and practice. Knowl Eng

Rev 1995; 10:115-38.

32 Wright W, Moore D. Design considerations for multiagent systems on very small

platforms. In: Proceedings of the Second International Joint Conference on

Autonomous Agents and Multi-Agent Systems. Melbourne, Australia; 2003. pp.

1160-2.

40/46

33 Cao J, Tse DCK, Chan ATS. PDAgent: A Platform for Developing and Deploying

Mobile Agent-enabled Applications for Wireless Devices. In: Proceedings of the

2004 International Conference on Parallel Processing. Montreal, Canada; 2004. pp.

510-8.

34 Shepherdson J, Lee H, Mihailescu P. Multi-agent Systems as a Middleware to

Automate Mobile Business Processes: State of the Art. In: Proceedings of the 5th

International Conference on Applied Informatics and Communications. Malta;

2007. pp. 187-5.

35 O’Hare GMP, O’Grady MJ, Muldoon C, Bradley JF. Embedded Agents: A

Paradigm for Mobile Services. Int J Web and Grid Services 2006; 2:379-27.

36 Sun Microsystems, Inc. JSR-000139 Connected Limited Device Configuration,

Specification Version 1.1.1, Java Platform, Micro Edition (Java ME). Specification.

Santa Clara, CA, USA: Sun Microsystems, Inc.; 2007.

37 Oracle Corporation. Java SE Documentation - Windows System Requirements for

JDK and JRE. [online] Redwood Shores, CA, USA: Oracle Corporation; 2013.

Available at: <http://docs.oracle.com/javase/7/docs/webnotes/install/windows/-

windows-system-requirements.html> [Accessed January 1, 2013].

41/46

38 Muldoon C, O’Hare GMP, Collier RW, O’Grady MJ. Agent Factory Micro Edition:

A Framework for Ambient Applications. In: Proceedings of The Intelligent Agents

in Computing Systems Workshop. Reading, UK; 2006. pp. 727-8.

39 Illari S, Trillo R, Mena E. SPRINGS: A Scalable Platform for Highly Mobile

Agents in Distributed Computing Environments. In: Proceedings of the 2006

International Symposium on a World of Wireless, Mobile and Multimedia

Networks. Buffalo, NY, USA; 2006. pp. 633-5.

40 Koch F. 3APLM Platform for Deliberative Agents in Mobile Devices. In:

Proceedings of the International Joint Conference on Autonomous Agents and

Multiagent Systems. Utrecht, The Netherlands; 2005. pp. 153-2.

41 Caire G., Pieri, F. LEAP User Guide. LEAP 4.1.1. Torino, Italy: Telecom Italia

S.p.A.; 2011.

42 Bellifemine F, Caire G, Trucco T, Rimassa G. JADE Programmer’s Guide. Torino,

Italy: Telecom Italia S.p.A.; 2010.

43 Aiello F, Fortino G, Gravina R, Guerrieri A. A Java-Based Agent Platform for

Programming Wireless Sensor Networks. Comput J 2011; 54:439-16.

42/46

44 Urra O, Ilarri S, Trillo R, Mena E. Mobile Agents and Mobile Devices: Friendship

or Difficult Relationship? J Phys Agents 2009; 3:27-11.

45 Aiello F, Carbone A, Fortino G, Galzarano S. Java-based Mobile Agent Platforms

for Wireless Sensor Networks. In: Proceedings of the International Multiconference

on Computer Science and Information Technology. Wisla, Poland; 2010. pp. 165-8.

46 Cucurull J, Overeinder BJ, Oey MA, Borrell J, Brazier FMT. Abstract software

migration architecture towards agent middleware interoperability. In: Proceedings

of the International Multiconference on Computer Science and Information

Technology. Wisla, Poland; 2007. pp. 27-11.

47 Warnier M, Oey MA, Timmer RJ, Overeinder BJ, Brazier FMT. Enforcing Integrity

of Agent Migration Paths by Distribution of Trust. Int J of Intelligent Information

and Database Systems 2009; 3:382-15.

48 Herbert J, O’Donoghue J, Ling G, Fei K, Fok CL. Mobile Agent Architecture

Integration for a Wireless Sensor Medical Application. In: Proceedings of the 2006

IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent

Agent Technology. Hong Kong, China; 2006. pp. 235-4.

49 Sun Microsystems, Inc. Sun Small Programmable Object Technology (Sun SPOT).

Developer’s Guide. Santa Clara, CA, USA: Sun Microsystems, Inc.; 2009.

43/46

50 Sun Microsystems, Inc. Sun SPOT Owner’s Manual. Owner’s Manual. Santa Clara,

CA, USA: Sun Microsystems, Inc.; 2009.

51 Gupta V. Experiments with a Solar-powered Sun SPOT. Technical Report. Menlo

Park, CA, USA: Sun Microsystems Laboratories; 2009.

52 Bortenschlager M, Haid, E, Wagner A. Rapid Prototyping of Sensor-Based

Applications with SunSPOTs. ERCIM News 2009; 76:36-2.

53 Smarsly K, Law KH, König M. Resource-Efficient Wireless Monitoring based on

Mobile Agent Migration. In: Proceedings of the SPIE (Vol. 7984): Health

Monitoring of Structural and Biological Systems. San Diego, CA, USA; 2011. pp.

26-12.

54 Mahadeokar J. Java Sun SPOTS in the Rain Forests of India. [online] Cary, NC,

USA: DZone, Inc. Available at: <http://www.dzone.com/links/java_sun_spots_in_-

the_rain_forests_of_india.html> [Accessed January 3, 2013].

55 Smarsly K., Georgieva K., König M, Law KH. Monitoring of Slope Movements

coupling Autonomous Wireless Sensor Networks and Web Services. In:

Proceedings of the First International Conference on Performance-Based Life-Cycle

Structural Engineering. Hong Kong, China; 2012. pp. 1096-7.

44/46

56 Georgieva K, Smarsly K, König M, Law KH. An Autonomous Landslide

Monitoring System based on Wireless Sensor Networks. In: Proceedings of the

2012 ASCE International Conference on Computing in Civil Engineering.

Clearwater Beach, FL, USA; 2012. pp. 145-8.

57 Alarcon-Ramirez A, Martinez-Pabon M, Kim C. Design and Prototype of a

Wireless Tailgate Detection System Using Sun Spot Platform. Int J Embedded

Systems and Applications 2011; 1:1-14.

58 Sun Microsystems, Inc. SunSPOT Theory of Operation. Specification. Santa Clara,

CA, USA: Sun Microsystems, Inc.; 2009.

59 STMicroelectronics. LIS3L02AQ. Datasheet. Geneva, Switzerland:

STMicroelectronics N.V.; 2004.

60 Simon D, Cifuentes C, Cleal D, Daniels J, White D. Java on the bare metal of

wireless sensor devices: the Squawk Java virtual machine. In: Proceedings of the

2nd international conference on Virtual execution environments. Ottawa, Canada;

2006. pp. 78-11.

45/46

61 Simon D, Cifuentes C. The Squawk virtual machine: Java on the bare metal. In:

Proceedings of the 20th Conference on Object-Oriented Programming, Systems,

Languages, and Applications. San Diego, CA, USA; 2005. pp. 150-2.

62 Smarsly K, Law KH, König M. Autonomous Structural Condition Monitoring based

on Dynamic Code Migration and Cooperative Information Processing in Wireless

Sensor Networks. In: Proceedings of the 8th International Workshop on Structural

Health Monitoring. Stanford, CA, USA; 2011. pp. 1996-8.

63 Aiello F, Carbone A, Fortino G, Galzarano S. Java-based Mobile Agent Platforms

for Wireless Sensor Networks. In: Proceedings of the International Multiconference

on Computer Science and Information Technology. Wisla, Poland; 2010. pp. 165-8.

64 Bölöni L, Marinescu DC. A Multi-Plane State Machine Agent Model. Technical

Report. West Lafayette, IN, USA: Purdue University; 1999.

65 Bölöni L, Marinescu DC. A Multi-Plane State Machine Agent Model. In:

Proceedings of Fourth International Conference on Autonomous Agents. Barcelona,

Spain; 2000. pp. 80-1.

66 Press, WH, Teukolsky, SA, Vetterling, WT. Numerical Recipes in C: The Art of

Scientific Computing. Second edition. Cambridge, UK: Cambridge University

Press, 2002.

46/46

67 Cooley JW, Tukey JW. An Algorithm for the Machine Calculation of Complex

Fourier Series. Math Comput 1965; 19:297-5.

68 National Semiconductor Corporation. LM135/LM235/LM335, LM135A/LM235A/-

LM335A Precision Temperature Sensors. Datasheet. Santa Clara, CA, USA:

National Semiconductor Corporation; 1999.