final v1

chapters/abstract.tex

@@ -3,9 +3,7 @@
 \vspace{5mm}
 \end{center}
 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-This research investigates power optimization in Thread mesh wireless networks by conducting a series of experiments aimed at reducing overall power consumption while maintaining reliable network performance. Transmission power serves as a key parameter for achieving energy efficiency, and the study focuses on two algorithmic approaches: the \gls{MCM} and the \gls{GA}. The research involves determining the optimal network configuration and transmission power constraints, selecting appropriate hardware, building the network, and developing the algorithms. Data is collected and analyzed from various network modes and devices across two locations, including lab and home environments, to ensure diverse and representative results. \gls{MCM} emphasizes optimal network configuration alongside initial transmission power, while \gls{GA} targets optimal transmission power settings. The findings indicate that both \gls{MCM} and \gls{GA} outperform the maximum method in power optimization, with \gls{GA} offering the best results. By effectively minimizing energy usage, \gls{GA} ensures network performance is not compromised. The research emphasizes the importance of sustainability by promoting energy-efficient solutions that minimize environmental impact. The project's focus on energy efficiency and reduced power consumption makes it environmentally friendly and sustainable, contributing to reduced energy waste and lowering the carbon footprint associated with IoT networks. Additionally, the research process involves the application and development of professional skills, such as data analysis, algorithm design, and critical thinking, to ensure the reliability and relevance of the results. While the ethical aspects of the research may not be directly evident, the focus on sustainability and responsible technological development inherently involves ethical considerations, such as resource conservation and minimizing negative impacts on society and the environment. The findings contribute to the development of energy-efficient IoT networks and serve as a foundation for further exploration into power optimization techniques, encouraging the expansion of sustainable IoT ecosystems.
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+This research investigates power optimization in Thread mesh wireless networks through an algorithmic approach, aiming to reduce overall power consumption while maintaining reliable network performance. Transmission power serves as a key parameter for achieving energy efficiency, and the study focuses on two algorithmic approaches: the \gls{MCM} and the \gls{GA}. The research involves determining the optimal network configuration and transmission power constraints, selecting appropriate hardware, building the network, and developing the algorithms. Data is collected and analyzed from various network modes and devices across two locations, including lab and home environments, to ensure diverse and representative results. \gls{MCM} emphasizes optimal network configuration alongside initial transmission power, while \gls{GA} targets optimal transmission power settings. The findings indicate that both \gls{MCM} and \gls{GA} outperform the maximum method in power optimization, with \gls{GA} offering the best results. By effectively minimizing energy usage, \gls{GA} ensures network performance is not compromised. The research emphasizes the importance of sustainability by promoting energy-efficient solutions that minimize environmental impact. The project's focus on energy efficiency and reduced power consumption makes it environmentally friendly and sustainable, contributing to reduced energy waste and lowering the carbon footprint associated with \gls{IoT} networks. Additionally, the research process involves the application and development of professional skills, such as data analysis, algorithm design, and critical thinking, to ensure the reliability and relevance of the results. While the ethical aspects of the research may not be directly evident, the focus on sustainability and responsible technological development inherently involves ethical considerations, such as resource conservation and minimizing negative impacts on society and the environment. The findings contribute to the development of energy-efficient \gls{IoT} networks and serve as a foundation for further exploration into power optimization techniques, encouraging the expansion of sustainable \gls{IoT} ecosystems.
 
 \vspace{5mm}
-\noindent {\bf Keywords:} Thread mesh network, parameter optimization, power optimization, transmission power, MOOD-Sense.
+\noindent {\bf Keywords:} Thread mesh network, parameter optimization, power optimization, transmission power, monte carlo method optimization, genetic algorithm optimization, MOOD-Sense.

chapters/acknowledgements.tex

@@ -3,7 +3,7 @@
     \vspace{5mm} %5mm vertical space
 \end{center}
 
-I want to express my profound gratitude to my research supervisor, Dr. Mart Johan Deuzeman, Bryan Williams, and Dr. Jair Adriano Lima Silva, for their invaluable guidance, support, and mentorship throughout this research project. Their expertise, insights, and encouragement have been instrumental in shaping my work and helping me achieve my objectives.
+I want to express my profound gratitude to my research supervisor, Dr. Mart Johan Deuzeman, Dr. Bryan Williams, and Dr. Jair Adriano Lima Silva, for their invaluable guidance, support, and mentorship throughout this research project. Their expertise, insights, and encouragement have been instrumental in shaping my work and helping me achieve my objectives.
 
 My sincere appreciation goes to my colleagues and fellow researchers who have provided constructive feedback, shared their knowledge, and offered technical assistance when needed. Their collaboration has enriched my understanding of the subject matter and contributed to the quality of my research.
 

chapters/acronyms.tex

@@ -26,3 +26,6 @@
 \newacronym{RCP}{RCP}{Radio Coprocessor}
 \newacronym{ICMP}{ICMP}{Internet Control Message Protocol}
 \newacronym{UWB}{UWB}{Ultra-Wide Band}
+\newacronym{ARM}{ARM}{Advanced RISC Machine}
+\newacronym{SA}{SA}{Simulated Annealing}
+\newacronym{WMN}{WMN}{Wireless Mesh Network}

chapters/conceptual_model.tex

@@ -2,16 +2,20 @@ \chapter{Conceptual Model}\label{chap:conceptual_model}
 
 This section introduces the central theme of the research, which revolves around transmission power optimization in Thread mesh wireless networks as a part of the MOOD-Sense initiative. With a primary focus on improving energy efficiency in \gls{IoT} applications utilizing the Thread protocol, the research delves into the investigation and evaluation of transmission power optimization with optimal network configuration using an algorithmic approach. This examination sheds light on the techniques' impact on overall network performance and contributes to the development of energy-efficient \gls{IoT} networks.
 
-The system will be developed based on the Thread protocol, a low-power, \gls{IPv6}-based networking protocol designed for \gls{IoT} applications. To optimize the power consumption of Thread networks, the project will employ a two-step process. First, the \gls{MCM} will be used to find the optimal network configuration and initial transmission power. This step will involve a thorough analysis of different network configurations based on different constraints. The project will leverage \gls{MCM}'s strengths in randomness and theoretical justification to ensure the reliability of the results. Next, the \gls{GA} will take the final output from \gls{MCM} and focus on finding the lowest transmission power possible. The use of \gls{GA} will help improve the overall energy efficiency and performance of the Thread network by taking into account the network's constraints. The following diagram illustrates the flow of the entire process, from \gls{MCM} and \gls{GA} optimization to the implementation of optimized transmission power in the Thread network that shows a clear visual representation of the project's methodology.
+The system will be developed based on the Thread protocol, a low-power, \gls{IPv6}-based networking protocol designed for \gls{IoT} applications. To optimize the power consumption of Thread networks, the project will employ a two-step process. First, the \gls{MCM} will be used to find the optimal network configuration and initial transmission power. This step will involve a thorough analysis of different network configurations based on different constraints. The project will leverage \gls{MCM}'s strengths in randomness and theoretical justification to ensure the reliability of the results.
+
+Next, the \gls{GA} will take the final output from \gls{MCM} and focus on finding the lowest transmission power possible. The use of \gls{GA} will help improve the overall energy efficiency and performance of the Thread network by taking into account the network's constraints. The following diagram illustrates the flow of the entire process, from \gls{MCM} and \gls{GA} optimization to the implementation of optimized transmission power in the Thread network that shows a clear visual representation of the project's methodology.
 
 \begin{figure}[H]
     \centering
-    \includegraphics[width=0.8\textwidth]{images/conceptual_model/Thread_Network_Power_Optimization_Conceptual_Model.png}
+    \includegraphics[width=1\textwidth]{images/conceptual_model/conceptual-model.png}
     \caption{Thread network power optimization conceptual model.}
     \label{fig:conceptual_model}
 \end{figure}
 
-The project will consider the cost of hardware components, software development, testing, and deployment while maintaining a balance between cost-effectiveness and performance. The power consumption will be measured using \gls{PPK} II explained in hardware section. The research will measure output power in different scenarios to validate the effectiveness of the power optimization techniques employed. These scenarios will be categorized based on the method, location, type, mode, duration, and ping used for power optimization and measurement.
+The project will consider the cost of hardware components, software development, testing, and deployment while maintaining a balance between cost-effectiveness and performance. It is important to take into account the computational time and hardware requirements when implementing the \gls{MCM} and \gls{GA}. The \gls{MCM} generally provides an initial solution more quickly, while the \gls{GA} refines this solution and converges to an optimal one over a longer period due to its iterative nature and the use of genetic operators like crossover and mutation. The hardware requirements for both methods depend on the complexity of the problem and the size of the search space. However, modern computational resources are typically sufficient to handle the demands of these algorithms for the given research problem. Ultimately, this research emphasizes the importance of balancing the algorithmic approach with the underlying computational resources when optimizing power consumption in Thread networks.
+
+The power consumption will be measured using \gls{PPK} II, as explained in the hardware section. The research will measure output power in different scenarios to validate the effectiveness of the power optimization techniques employed. These scenarios will be categorized based on the method, location, type, mode, duration, and ping used for power optimization and measurement.
 
 \begin{enumerate}
   \item \textbf{Method}: The power consumption will be measured in two primary scenarios - Maximum and Optimized. The maximum scenario represents the baseline power consumption, where no optimization techniques are applied. The Optimized scenario will measure power consumption after implementing the \gls{MCM} and \gls{GA} optimization techniques.