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11 0_cfg_misc/d_code_highlighting.tex
@@ -51,6 +51,17 @@
commentstyle=\color{forestGreen}\ttfamily
}
+\lstnewenvironment{ini-code}[2]
+{
+ \lstset{
+ caption=#1,
+ label=#2,
+ style=workflowStyle,
+ captionpos=t,
+ xleftmargin=2em,
+ xrightmargin=2em
+ }
+}{}
\lstnewenvironment{workflow-code}[2]{
\lstset{
View
12 0_cfg_misc/r_2_contents.tex
@@ -4,9 +4,9 @@ \subsection{Monitoring using Video or Audio}
\label{chap21}
Current smart home environments consist of several appliances and other devices, with sensors, actuators and/or biomedical monitors. These systems are used by the residents in a daily basis. In some situations the house is monitored using video and audio technologies, even tho they present some disadvantages like: high costs due to sophisticated equipments and specialized deployment, the need for a large bandwidth or privacy issues.
Several state-of-the-art solutions were reviewed. In \cite{2} falls are detected. In order to reduce the number of false alarms, the system integrates a \acs{WSN} and a video system. Cameras activated by a wireless sensor tracking mechanism, are able to interpret the video signal and make decisions whether to call an emergency number or not. A voice communication \acs{IEEE} 802.15.4 is also discussed through the usage of state-of-the-art radios capable of transmitting voice.\\
-In \cite{3} and \cite{4} an installed surveillance system is used to infer about the position of a resident. No interaction with the system is needed in order to locate the person. The usage of \textit{Smart Cameras} allows to resolve the privacy issue of data transmission through air, with the possibility of some kind of spoofing existing, which would presents serious security concerns to the user.\\
+In \cite{3} and \cite{4} an installed surveillance system is used to infer about the position of a resident. No interaction with the system is needed in order to locate the person. The usage of \textit{Smart Cameras} allows to resolve the privacy issue of data transmission through air, with the possibility of spoofing, which would present serious security concerns to the user.\\
\cite{5} deploys another monitoring application in a care home. It refers to the need for more healthcare professionals and the small amount of time that each one of them has available for each elder. The volume of biomedical data gathered can improve the way that the case manager follows it's dependent.\\
-\cite{6} refers to the term \textit{aging in place} which represent a movement where elders live in an independent and safe manner in their own homes. Monitoring of falls but also utilitarian functionalities are implemented such as object detection, calendar, video-conference and address book.\cite{7} uses video and audio to correctly deduce if a fall has happen.
+\cite{6} refers to the term \textit{aging in place} which represents a movement where elders live in an independent and safe manner in their own homes. Monitoring of falls but also utilitarian functionalities are implemented such as object detection, calendar, video-conference and address book.\cite{7} uses video and audio to correctly deduce if a fall has happen.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -14,7 +14,7 @@ \subsection{Monitoring using Video or Audio}
\subsection{Monitoring using Wearable Sensors}
\label{chap22}
The reduction in size of wireless sensors is bringing to the market solutions that can track a person's health, independently of his location or activity. The possibility of smart clothes with built-in sensors sufficiently small and light to be carried without any discomfort, enable the mass usage of such equipments in a medium term.
-In \cite{8} the \acf{BSN} is addressed, as a solution to early detect heart problems with sensors capable of measuring temperature, acceleration or building an electrocardiogram, connected to a central coordinator node using Bluetooth (Figure \ref{fig:4:bsn}).
+In \cite{8} the \acf{BSN} is addressed, as a solution to early detect heart problems with sensors capable of measuring temperature, acceleration or building an electrocardiogram all connected to a central coordinator node using Bluetooth (Figure \ref{fig:4:bsn}).
\cite{9} discusses the the need for three types of priorities for messages in a \acs{WBAN}: \textit{On-demand} requested by a doctor or physician in order to monitor the patient vital signs, \textit{Emergency} initiated by the sensors when some critical threshold has been exceeded and \textit{Normal} with the lowest priority.In \cite{10} it is discussed the problems that arise from the usage of Zigbee in a crowed \acs{WLAN} environment. An algorithm is suggested to solve this issue in which the Zigbee forces an \acs{AP} to leave from an occupied frequency.
\begin{figure}[!htb]
@@ -32,7 +32,7 @@ \subsection{Monitoring using Wearable Sensors}
\subsection{Monitoring using domestic sensors}
\label{chap23}
-In this type of monitoring, information is gathered anonymously, using sensors with very minimal computational capabilities. Proximity sensors or sensors installed in home appliances are accessed in order to understand if the person is at home or has passed through a certain corridor. This type of monitoring has less granularity which can be a problem if more information is needed.\cite{13} uses pressure sensors to help Alzheimer's patients reaching their destination inside house through the use of TV screens. \textit{MediaCup} is introduced in \cite{12} as fully ubiquous device. With a battery capable of charging using an electromagnetic field, the \textit{MediaCup} is fully hidden from user and coupled with a few sensors that can track, for instance, movement or acceleration (
+In this type of monitoring, information is gathered anonymously, using sensors with very minimal computational capabilities. Proximity sensors or sensors installed in home appliances are accessed in order to understand if the person is at home or has passed through a certain corridor. This type of monitoring has less granularity, which can be a problem if more information is needed.\cite{13} uses pressure sensors to help Alzheimer's patients reaching their destination inside house through the use of TV screens. \textit{MediaCup} is introduced in \cite{12} as fully ubiquous device. With a battery capable of charging using an electromagnetic field, the \textit{MediaCup} is fully hidden from user and coupled with a few sensors that can track, for instance, movement or acceleration (
\begin{figure}[!htb]
\centering
@@ -41,13 +41,13 @@ \subsection{Monitoring using domestic sensors}
\label{fig:6:mediacup}
\end{figure}
-A lot of research has been done focusing falls, vital signs tracking and ubiquous computing. The recent development in \acp{BSN}s has brought to the scene new sensors and new concepts that can even be used for entertainment. Also the need for unobtrusive equipments and the lack of privacy of devices that get too much information, like cameras is a problem to be discussed.
+A lot of research has been done focusing falls, vital signs tracking and ubiquous computing. The recent development in \acp{BSN}s has brought to the scene new sensors and new concepts that can even be used for entertainment. Also the need for unobtrusive equipments and the lack of privacy of devices that store too much information, like cameras is a problem to be discussed.
\section{Related Work}
\subsection{Elder Home Monitoring}
\label{chap31}
-In study named \``The Activities of Daily Living Study''\ presented at \cite{16} several questionnaires are delivered to \acfp{CM} ( professionals that give assistance to elder people living at home). This study refers to the existence of a group of \acfp{ADL} which \acp{CM} keep track. These include getting up in the morning, dressing or feeding. Through these \acp{ADL} healthcare professionals are able to keep track of their elders mental and physical state. This study also sets some of the most valuable features which a monitoring system can present to elders. Features like panic buttons and security improvement measures seem to have success while others like cameras don't seem as accepted.\\
+In study named \``The Activities of Daily Living Study''\ presented at \cite{16} several questionnaires were delivered to \acfp{CM} ( professionals that give assistance to elder people living at home). This study refers to the existence of a group of \acfp{ADL} which \acp{CM} keep track. These include getting up in the morning, dressing or feeding. Through these \acp{ADL} healthcare professionals are able to keep track of their elders mental and physical state. This study also sets some of the most valuable features which a monitoring system can present to elders. Features like panic buttons and security improvement measures seem to have success while others like cameras don't seem as accepted.\\
The study also enumerates the some of the main needs in monitoring elder people in-home. Location tracking to know if the elder got up of his bed, better scheduling of visits the \acs{CM} being able to know if the elder is at home, house occupancy to understand the elder need for companionship.
\subsection{Position tracking for Wireless Sensor Networks}
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2  1_capa-resumo-indice/1_capa.tex
@@ -38,7 +38,7 @@
% Always remember that 'Professor' should only be used for a juri member with a Cathedra
\presidentofjury{\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_}
\vogalone{\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_}
-\vogaltwo{\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_}
+%\vogaltwo{\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_}
% \vogalthree{Doutor whatever full name 4}
% \vogalfour{Doutor whatever full name 5}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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13 1_capa-resumo-indice/2_agradecimentos.tex
@@ -15,5 +15,16 @@
\thispagestyle{plain}
\pdfbookmark{Agradecimentos}{Acknowledgments}
\begin{acknowledgments}
-%Obrigado ao Pedro Tomás, o autor original do template para \LaTeX\ (versão inglesa).
+No culminar de uma longa etapa, são várias as pessoas que faço questão de mencionar neste trabalho, expressando os meus sinceros agradecimentos.
+
+À minha família, em especial à minha mulher, Ana Bernardo pelo seu constante apoio e ao meu filho, Tomás, por me ter dado, ainda dentro da barriga da mãe, a força e motivação para concluir tão longa etapa.
+
+Por ter aceite orientar a minha tese e por toda a ajuda proporcionada, ao meu Orientador, Professor Renato Nunes, pela disponibilidade demonstradas e pelas ideias e opiniões sugeridas que me proporcionaram reflexão e determinação para concluir este trabalho.
+
+Ao meu Co-Orientador, Professor António Grilo, por me ter dado a conhecer nas suas aulas o mundo fantástico das redes móveis e um simulador tão poderoso como o OMNeT++.
+
+Ao meu patrão e amigo, Pedro Dias, aos meus amigos Rodrigo Dias e Daniel Zacarias por me terem dado o tempo e a serenidade para terminar este trabalho.
+
+
+
\end{acknowledgments}
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4 2_texto_principal/1_intro.tex
@@ -54,9 +54,9 @@ \section{Organiza
No \autoref{chap:3} é feita uma recolha de trabalho relacionado, considerado relevante para este trabalho. Temas como, a monitorização de idosos e a forma como estes aceitam a tecnologia, a visão dos prestadores de cuidados de saúde, são abordados, bem como as métricas necessárias em contexto de pessoas idosas. Seguidamente fala-se da localização em redes \acs{WSN}, das possibilidade de localização, usando a potência do sinal recebido, o ângulo de chegada e são analisados três sistemas de localização. São ainda estudados os protocolos de encaminhamento, as vantagens e desvantagens de cada um.
-No \autoref{chap:4} é feita uma introdução ao ambiente trabalho, ao processo de escolha e às decisões tomadas na simulação de obstáculos, bem como o modelo usado. É analisado ao pormenor o mecanismo de tratamento de sinal recebido do \acs{MiXiM}.
+No \autoref{chap:4} é feita uma introdução ao ambiente trabalho, ao processo de escolha e às decisões tomadas na simulação de obstáculos.É analisado ao pormenor o mecanismo de tratamento de sinal recebido no \acs{MiXiM} e escolhido o modelo de propagação de sinal a usar.
-No \autoref{chap:5} é descrita a arquitectura do sistema \acs{EMoS}, dos seus componentes, da camada \textit{NIC}, da camada \textit{network} onde é implementado o \acs{AODV} e da camada de aplicação onde se implementa o sistema de localização HORUS. Faz-se uma descrição completa do funcionamento das duas camadas recorrendo para isso a fluxogramas exemplificativos.
+No \autoref{chap:5} é descrita a arquitectura do sistema \acs{EMoS}, dos seus componentes, da camada \textit{network} onde é implementado o \acs{AODV} e da camada de aplicação onde se implementa o sistema de localização HORUS. Faz-se uma descrição completa do funcionamento das duas camadas recorrendo para isso a fluxogramas exemplificativos.
No \autoref{chap:7} faz-se a conclusão deste trabalho e lançam-se algumas ideias trabalho futuro.
View
67 2_texto_principal/6_results.tex
@@ -5,15 +5,74 @@
\fancychapter{Resultados}
\label{chap:6}
-\section{Simulação de Obstáculos}
+\section{\textit{Testbed}}
\label{chap:6:sec:1}
+Por forma a validar os resultados e a simulação desenvolvida, foi necessário criar um cenário de monitorização em ambiente doméstico. No cenário criado foram colocados 9 nós fixos, 1 nó móvel e 1 nó base, com o intuito de monitorizar uma pessoa idosa na sua rotina normal.
-\section{Protocolo de Encaminhamento}
-\label{chap:6:sec:2}
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=0.9\textwidth]{img/06_testbed.png}
+ \caption{Cenário da simulação criada para testar o EMoS.}
+ \label{fig:1:testbed}
+\end{figure}
+
+Na Figura \ref{fig:1:testbed} é possível observar os 9 nós, que estão todos interligados. A ligação entre nós nós não se deve ao facto de estarem todos à distância de recepção, embora neste cenário seja o caso das as distâncias consideradas, mas porque no \acs{MiXiM} todos os nós que estejam à distância de interferência consideram-se ligados, mesmo que seja impossível a recepção.
+
+O cenário escolhido consiste numa casa com cerca de 93.75 m2 de área (12.5 m x 7.5 m) onde foram colocados obstáculos para cada parede interior, visíveis a vermelho na imagem. O conteúdo do ficheiro de configuração de obstáculos está no Anexo \ref{list:b1}.
\section{Sistema de Localização}
-\label{chap:6:sec:3}
+\label{chap:6:sec:2}
+
+Para testar o protocolo de localização foi feito um mapa rádio da \textit{testbed} usando o modelo de mobilidade \textit{TractorMobility} tal como indicado no capítulo anterior.
+
+Foi também necessário criar um caminho para o nó móvel para simular uma pessoa em movimento dentro da casa. Criou-se então o cenário de mobilidade da Figura \ref{fig:2:testbedMobility} onde existe um percurso pela casa, em que o nó móvel executa o percurso indicado pelas setas a vermelho, parando durante algum tempo em alguns dos pontos por onde passou.
+
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=0.8\textwidth]{img/06_testbed_mobility.png}
+ \caption{Cenário de mobilidade para teste do sistema de localização.}
+ \label{fig:2:testbedMobility}
+\end{figure}
+
+O ficheiro criado para o efeito, está disponível no Anexo \ref{list:b2} bem como o ficheiro de configuração da simulação \textit{omnetpp.ini}, no Anexo \ref{list:b3}.
+
+Assim para o mesmo percurso foram feitos diversos testes onde se avaliou o efeito das técnicas de estimação de espaço contínuo.
+
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=1\textwidth]{img/06_horus_simple.png}
+ \caption{Erros de estimativa no sistema HORUS sem técnicas de centro de massa e sem média.}
+ \label{fig:3:horusSimple}
+\end{figure}
+
+Podemos observar na Figura \ref{fig:3:horusSimple} a estima de localização ao longo do percurso, obtida a partir do estimador de espaço discreto. Quanto mais próximo o nó móvel está dos pontos que foram anteriormente medidos na fase \textit{offline}, melhores são as estimativas da posição. Na Figura \ref{fig:4:horusCenterMass}, está o resultado da estimação ao longo do percurso efectuado usando a técnica do centro de massa. Observa-se uma melhoria relativamente ao anterior.
+
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=0.9\textwidth]{img/06_horus_center_mass.png}
+ \caption{Erros de estimativa no sistema HORUS com centro de massa e sem média.}
+ \label{fig:4:horusCenterMass}
+\end{figure}
+
+Por fim na Figura \ref{fig:5:horusCenterMassMean} vemos o melhor valor de precisão alcançado pelas três simulações, no mínimo do gráfico em questão, mas por outro lado vemos um sistema bastante mais afectado por mudanças súbitas da posição estimada.
+
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=0.9\textwidth]{img/06_horus_center_mass_mean.png}
+ \caption{Erros de estimativa no sistema HORUS com centro de massa e com média.}
+ \label{fig:5:horusCenterMassMean}
+\end{figure}
+
+Outro ponto que pode ser interessante é a taxa com que o nó móvel reúne as assinaturas dos nós fixos e envia tudo para o nó base. Os testes anteriores foram feitos com 15 segundos entre envios. Se for alterado esse valor para 40 s obtemos uma curva bastante mais estável, uma vez que a informação reunida é muito mais semelhante à que está no mapa rádio, dada a correlação existente entre amostras.
+
+\begin{figure}[!htb]
+ \centering
+ \includegraphics[width=0.9\textwidth]{img/06_horus_center_mass_40s.png}
+ \caption{Erros de estimativa no sistema HORUS com centro de massa e sem média para um tempo de amostragem do nó móvel de t=40s.}
+ \label{fig:5:horus40s}
+\end{figure}
+\clearpage
% Ensure that the next chapter starts in a odd page
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8 3_referencias-anexos/2_anexos.tex
@@ -1,9 +1,5 @@
\appendix
-% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% First appendix
-% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\fancychapter{Apêndice 1 - Ficheiros XML de Configuração}
-\label{annex:a}
+\input{3_referencias-anexos/anexoA}
-\input{3_referencias-anexos/anexoA}
+\input{3_referencias-anexos/anexoB}
View
55 3_referencias-anexos/anexoA.tex
@@ -1,26 +1,5 @@
-%XML CONFIG NIC
-\begin{annex-code}{Ficheiro XML de conciguração da camada \acs{NIC}.}{list:a1:xmlNIC}
-<?xml version="1.0" encoding="UTF-8"?>
-<root>
- <!-- ANALOGUE MODELS -->
- <AnalogueModels>
- <AnalogueModel type="SimplePathlossModel">
- <parameter name="alpha" type="double" value="3.0"/>
- <parameter name="carrierFrequency" type="double" value="2.412e+9"/>
- </AnalogueModel>
- <!-- we add a log-normal shadowing effect on top of the IEEE 802.15.4 path loss -->
- <AnalogueModel type="LogNormalShadowing">
- <parameter name="mean" type="double" value="0.0"/>
- <parameter name="stdDev" type="double" value="6"/>
- <parameter name="interval" type="double" value="1"/>
- </AnalogueModel>
- <!-- we add obstacle shadowing on top of the log-normal shadowing effect -->
- <AnalogueModel type="SimpleObstacleShadowing">
- <parameter name="carrierFrequency" type="double" value="2.412E+9"/>
- </AnalogueModel>
- </AnalogueModels>
-</root>
-\end{annex-code}
+\fancychapter{Apêndice 1 - Ficheiros de XML de Exemplo}
+\label{annexA}
%XML CONFIG radio MAP
\begin{annex-code}{Exemplo de ficheiro XML de configuração do mapa rádio.}{list:a1:xmlRadioMap}
@@ -64,6 +43,21 @@
</radioMap>
\end{annex-code}
+\begin{annex-code}{Exemplo de ficheiro XML de configuração do módulo \textit{TurtleMobility} do MiXiM.}{list:a1:xmlTurtleMobility}
+<movement>
+ <set speed="10" angle="180"/>
+ <repeat n="4">
+ <forward d="50"/>
+ <turn angle="90"/>
+ </repeat>
+ <repeat>
+ <set speed="uniform(10,20)"/>
+ <turn angle="uniform(-30,30)"/>
+ <forward t="uniform(0.1,1)"/>
+ </repeat>
+</movement>
+\end{annex-code}
+
\clearpage
\begin{annex-code}{Exemplo de ficheiro XML de configuração de \textit{clusters} de posições.}{list:a1:xmlRadioMapCluster}
@@ -103,19 +97,4 @@
<position x="3" y="6"/>
</cluster>
</radioMapClusters>
-\end{annex-code}
-
-\begin{annex-code}{Exemplo de ficheiro XML de configuração do módulo \textit{TurtleMobility} do MiXiM.}{list:a1:xmlTurtleMobility}
-<movement>
- <set speed="10" angle="180"/>
- <repeat n="4">
- <forward d="50"/>
- <turn angle="90"/>
- </repeat>
- <repeat>
- <set speed="uniform(10,20)"/>
- <turn angle="uniform(-30,30)"/>
- <forward t="uniform(0.1,1)"/>
- </repeat>
-</movement>
\end{annex-code}
View
211 3_referencias-anexos/anexoB.tex
@@ -0,0 +1,211 @@
+\fancychapter{Apêndice 2 - Ficheiros de configuração usados nas simulações}
+\label{annexB}
+
+%XML CONFIG NIC
+\begin{annex-code}{Ficheiro XML de conciguração da camada \acs{NIC}.}{list:a1:xmlNIC}
+<?xml version="1.0" encoding="UTF-8"?>
+<root>
+ <!-- ANALOGUE MODELS -->
+ <AnalogueModels>
+ <AnalogueModel type="SimplePathlossModel">
+ <parameter name="alpha" type="double" value="3.0"/>
+ <parameter name="carrierFrequency" type="double" value="2.412e+9"/>
+ </AnalogueModel>
+ <!-- we add a log-normal shadowing effect on top of the IEEE 802.15.4 path loss -->
+ <AnalogueModel type="LogNormalShadowing">
+ <parameter name="mean" type="double" value="0.0"/>
+ <parameter name="stdDev" type="double" value="6"/>
+ <parameter name="interval" type="double" value="1"/>
+ </AnalogueModel>
+ <!-- we add obstacle shadowing on top of the log-normal shadowing effect -->
+ <AnalogueModel type="SimpleObstacleShadowing">
+ <parameter name="carrierFrequency" type="double" value="2.412E+9"/>
+ </AnalogueModel>
+ </AnalogueModels>
+</root>
+\end{annex-code}
+
+
+%XML CONFIG NIC
+\begin{annex-code}{Ficheiro de configuração dos objectos da \textit{testbed}.}{list:b1}
+<obstacles>
+ <!--<poly id="wall#0" type="brickWall20cm" color="#F00" shape="x,y x,y x,y x,y" />-->
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="3.29,0 3.47,0 3.47,2.81 3.29,2.81" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="3.29,2.81 3.29,2.69 5.62,2.69 5.62,2.81" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="6.58,0 6.7,0 6.7,2.81 6.58,2.81" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="8.07,0 8.19,0 8.19,2.81 8.07,2.81" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="9.51,0 9.63,0 9.63,1.73 9.51,1.73" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="8,2.81 8,2.69 9.51,2.69 9.51,2.81" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="5.14,3.89 5.26,3.89 5.26,7.5 5.14,7.5" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="5.14,3.89 7.47,3.89 7.47,3.98 5.14,3.98" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="8.37,3.89 8.49,3.89 8.49,7.5 8.37,7.5" />
+ <poly id="wall#0" type="brickWall20cm" color="#F00" shape="9.39,3.89 12.5,3.89 12.5,3.98 9.39,3.98" />
+</obstacles>
+\end{annex-code}
+
+\begin{annex-code}{Ficheiro de configuração para o cenário de mobilidade usado nos testes do sistema de localização.}{list:b2}
+<movement>
+ <wait t="100"/>
+ <set speed="0.8"/>
+ <moveto x="2.39" y="5.98"/>
+ <wait t="300"/> <!-- ver TV -->
+ <moveto x="5.38" y="3.29"/>
+ <moveto x="7.72" y="3.29"/>
+ <moveto x="7.72" y="1.5"/>
+ <wait t="600"/> <!-- WC -->
+ <moveto x="7.72" y="3.29"/>
+ <moveto x="1.61" y="3.29"/>
+ <moveto x="1.44" y="1.73"/>
+ <wait t="900"/> <!-- jantar -->
+ <moveto x="0.6" y="3.89"/> <!--tocaram a campainha -->
+ <wait t="60"/>
+ <moveto x="1.44" y="1.73"/>
+ <wait t="300"/> <!-- termina jantar -->
+ <moveto x="1" y="3.29"/>
+ <moveto x="11.36" y="3.29"/>
+ <moveto x="11.36" y="0.9"/>
+ <wait t="650"/> <!-- dormir um bocado -->
+</movement>
+\end{annex-code}
+
+\begin{ini-code}{Ficheiro de configuração omnetpp.ini para testes de localização.}{list:b3}
+[General]
+network = EMoS01
+repeat = 1
+ned-path=/home/mnobrega/workspace/mixim/src;/home/mnobrega/workspace/mixim
+tkenv-image-path=/home/mnobrega/workspace/mixim/images/EMoS
+load-libs=/home/mnobrega/workspace/mixim/src/modules/miximmodules /home/mnobrega/workspace/mixim/src/base/miximbase
+
+**.coreDebug = false
+
+## WORLD - ANNOTATIONS ##
+*.annotations.draw = true
+**.world.use2D = true
+**.playgroundSizeX = 12.5 m
+**.playgroundSizeY = 7.5 m
+**.playgroundSizeZ = 0 m
+
+## CONNECTION ##
+**.connectionManager.sendDirect = false
+**.connectionManager.pMax = 1.1mW
+**.connectionManager.sat = -100dBm
+**.connectionManager.alpha = 2.0
+**.connectionManager.carrierFrequency = 2.4E+9Hz
+
+## OBSTACLES ##
+*.obstacles.debug = true
+
+## NODES ##
+***.app.debug = true
+***.net.debug = true
+
+#NICs config
+***.nic.phy.usePropagationDelay = true
+***.nic.phy.useThermalNoise = true
+***.nic.phy.analogueModels = xmldoc("xml_config_files/config.nic.xml")
+***.nic.phy.decider = xmldoc("xml_config_files/config.nic.xml")
+**.mobileNode[*].nic.phy.maxTXPower = 1 mW #XBEE
+**.staticNode[*].nic.phy.maxTXPower = 1.1 mW #CC2420
+**.baseStation[*].nic.phy.maxTXPower = 1 mW #XBEE
+**.mobileNode[*].nic.phy.sensitivity = -92dBm #XBEE
+**.staticNode[*].nic.phy.sensitivity = -95dBm #CC2420
+**.baseStation[*].nic.phy.sensitivity = -92dBm #XBEE
+
+***.netwType = "AODVRoute"
+***.net.headerLength = 24 bit
+***.mobility.z = 0
+**.mobileNode[*].arp.offset = 10 #max base stations = 9
+**.staticNode[*].arp.offset = 1000 #max mobile nodes = 989
+**.staticNode[*].battery.capacity = 99999mAh #connected to the electric network
+**.staticNode[*].battery.nominal = 99999mAh #connected to the electric network
+**.baseStation[*].battery.capacity = 99999mAh #connected to the electric network
+**.baseStation[*].battery.nominal = 99999mAh #connected to the electric network
+
+
+[Config testbed1]
+**.numHostsStatic = 9
+**.numHostsMobile = 1
+**.numHostsBase = 1
+**.staticNode[0].mobility.x = 1.49
+**.staticNode[0].mobility.y = 1.49
+**.staticNode[1].mobility.x = 2.39
+**.staticNode[1].mobility.y = 4.78
+**.staticNode[2].mobility.x = 4.78
+**.staticNode[2].mobility.y = 1.49
+**.staticNode[3].mobility.x = 6.88
+**.staticNode[3].mobility.y = 5.68
+**.staticNode[4].mobility.x = 6.88
+**.staticNode[4].mobility.y = 3.29
+**.staticNode[5].mobility.x = 7.48
+**.staticNode[5].mobility.y = 1.49
+**.staticNode[6].mobility.x = 8.75
+**.staticNode[6].mobility.y = 1.49
+**.staticNode[7].mobility.x = 10.47
+**.staticNode[7].mobility.y = 5.68
+**.staticNode[8].mobility.x = 10.77
+**.staticNode[8].mobility.y = 1.79
+**.mobileNode[0].mobility.x = 0.5
+**.mobileNode[0].mobility.y = 0.5
+**.baseStation[0].mobility.x = 0.6
+**.baseStation[0].mobility.y = 0.6
+
+
+#offline phase
+[Config offline]
+sim-time-limit = 86 min # for calibration
+extends = testbed1
+**.stats = false
+*.obstacles.obstacles = xmldoc("xml_config_files/config.obstacles-01.xml")
+**.mobileNode[0].app.minimumStaticNodesForSample = 5
+**.mobileNode[0].app.clusterKeySize = 1
+**.mobileNode[0].app.maxPositionPDFsSize = 4
+**.mobileNode[0].app.calibrationMode = true
+**.mobileNode[0].mobilityType = "TractorMobility"
+**.mobileNode[0].mobility.updateInterval = 40 s
+**.mobileNode[0].mobility.x1 = 0.5m
+**.mobileNode[0].mobility.y1 = 0.5m
+**.mobileNode[0].mobility.x2 = 12m
+**.mobileNode[0].mobility.y2 = 7m
+**.mobileNode[0].mobility.rows = 10 # d= 2*(7-0.5) + 10*(12-0.5) = 128 m
+**.mobileNode[0].mobility.speed = 0.025 mps # 128/0.025 = 5120 s -> 85.3 min to complete the run
+**.mobileNode[0].mobility.debug = true
+**.mobileNode[0].mobility.x = 2
+**.mobileNode[0].mobility.y = 2
+**.mobileNode[0].app.firstPosX = 2
+**.mobileNode[0].app.firstPosY = 2
+**.staticNode[*].app.beaconMode = true
+**.staticNode[*].app.nodeSigStartingTime = 1
+**.staticNode[*].app.nodeSigPeriod = 1
+
+#online phase
+[Config online]
+extends = testbed1
+sim-time-limit = 40 min
+**.stats = false
+*.obstacles.obstacles = xmldoc("xml_config_files/config.obstacles-01.xml")
+**.mobileNode[0].app.calibrationMode = false
+**.mobileNode[0].app.minimumStaticNodesForSample = 5
+**.mobileNode[0].app.collectedDataSendingTimePeriod = 40
+**.mobileNode[0].mobility.x = 4
+**.mobileNode[0].mobility.y = 4
+**.mobileNode[0].app.firstPosX = 4
+**.mobileNode[0].app.firstPosY = 4
+**.mobileNode[0].mobilityType = "TurtleMobility"
+**.mobileNode[0].mobility.updateInterval = 0.1 s
+**.mobileNode[0].mobility.turtleScript = xmldoc("xml_config_files/config.mobility-01.xml")
+**.baseStation[0].app.radioMapXML = xmldoc("xml_data_files/radioMap.xml")
+**.baseStation[0].app.radioMapClustersXML = xmldoc("xml_data_files/radioMapClusters.xml")
+**.baseStation[0].app.normalStandardDistribXML = xmldoc("xml_data_files/normalStandard.xml")
+**.baseStation[0].app.useClustering = true
+**.baseStation[0].app.useCenterOfMass = true
+**.baseStation[0].app.usePhySpaceTimeAvg = false
+**.baseStation[0].app.recordStats = true
+**.baseStation[0].app.maxCenterOfMassPositions = 6 #HORUS VALUE
+**.baseStation[0].app.phySpaceTimeAvgWindowSize = 10 #HORUS VALUE
+**.staticNode[*].app.beaconMode = true
+**.staticNode[*].app.nodeSigStartingTime = 1
+**.staticNode[*].app.nodeSigPeriod = 1
+
+\end{ini-code}
+
+\clearpage
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2  dissertacao.tex
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% -18pt for 1 1/2 spacing
% -24pt for double spacing
-%\input{1_capa-resumo-indice/2_agradecimentos}
+\input{1_capa-resumo-indice/2_agradecimentos}
\input{1_capa-resumo-indice/3_resumo_abstract}
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