東華大學圖書館 |

Optimal Control for Data Harvesting and Signal Model Estimation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Control for Data Harvesting and Signal Model Estimation // Yancheng Zhu.
作者:	Zhu, Yancheng,
面頁冊數:	1 electronic resource (176 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31768653
ISBN:	9798302850980

Optimal Control for Data Harvesting and Signal Model Estimation /
Zhu, Yancheng,

Optimal Control for Data Harvesting and Signal Model Estimation /Yancheng Zhu. - 1 electronic resource (176 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

Over the last decade, the application of Wireless Sensor Networks (WSNs) has surged in fields such as environmental monitoring, human health, and smart cities. With this wealth of technologies comes the challenge of how to extract volumes of data collected by such sensor nodes distributed over large, often remote, geographical regions. Data harvesting is the problem of extracting measurements from the remote nodes of WSNs using mobile agents such as ground vehicles or drones. The use of mobile agents can significantly reduce the energy consumption of sensor nodes relative to other modes of extracting the data, extending the lifetime and capabilities of the WSN. Moreover, in remote areas where GPS may not be feasible due to limited power resources on the sensor nodes, the need for accurate sensor node localization and signal broadcasting model estimation becomes critical. Therefore, designing the trajectory of mobile agents is crucial for rapid data collection and information gathering while adhering to vehicle constraints such as dynamics and energy usage. In this thesis, we focus on the application of optimal control methods to design trajectories for mobile agents in data harvesting. This thesis makes contributions in three areas: the creation of a parameterized optimal control policy, the application of a Deep Reinforcement Learning (DRL) based control, and the use of Fisher Information (FI) as a cost matrix in a Receding Horizon Control (RHC) method.Parameterized Optimal Control Policy: Our contributions in this area begin by considering a data harvesting problem in 1-D space. We use a Hamiltonian analysis to show that the optimal control can be described using a parameterized policy and then develop a gradient descent scheme using Infinitesimal Perturbation Analysis (IPA) to calculate the gradients of the cost function with respect to the control parameters. We also consider this problem in a multi-agent setting. To avoid collisions between agents, we apply a Control Barrier Function (CBF) technique to ensure the agents closely track the desired optimal trajectory to complete their mission while avoiding any collisions. Finally, we extend the problem to a mobile sensor scenario. In this more complicated setting we demonstrate that the optimization problem for the control policy parameters can be effectively solved using a heuristic approach. Deep-Reinforcement-Learning based Control: The parametric optimal control approach cannot be easily extended from the 1-D setting to 2-D space. For this reason, we turn to DRL techniques. We utilize Hamiltonian analysis again to get the necessary conditions for optimal control and then translate the problem to a Markov Decision Process (MDP) in discrete time. We apply reinforcement learning techniques, including double deep Q-learning and Proximal Policy Optimization (PPO), to find high-performing solutions across different scenarios. We demonstrate the effectiveness of these methods in 2-D simulations.Fisher-Information-based Receding Horizon Control: For the data harvesting problem in large scale unknown environments, estimating the parameters defining the broadcast model and the location of all the nodes in the environment is critical for efficient extraction of the data. To address that, we start with a Received Signal Strength (RSS) model that relies on a Line-of-Sight (LoS) path-loss model with measurements that are corrupted by Gaussian distributed noise. We first consider a single agent tasked with estimating these unknown parameters in discrete time, and then develop a Fisher Information Matrix (FIM) Receding Horizon (RH) controller for agent motion planning in real time. We also design a Neural Network (NN)-based controller to approximate the optimal solution to the Hamilton-Jacobi-Bellman (HJB) problem, maximizing information gain along a continuous time trajectory. Additionally, a two-stage formation-based RH controller is designed for multi-agent scenarios. The experiments demonstrate that the optimal control policy contribute to the high performance of data collection and the FI-based RHC methods enhance the estimation accuracy in various simulation environments.

English

ISBN: 9798302850980Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Estimation accuracy

Optimal Control for Data Harvesting and Signal Model Estimation /
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500 $a Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
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520 $a Over the last decade, the application of Wireless Sensor Networks (WSNs) has surged in fields such as environmental monitoring, human health, and smart cities. With this wealth of technologies comes the challenge of how to extract volumes of data collected by such sensor nodes distributed over large, often remote, geographical regions. Data harvesting is the problem of extracting measurements from the remote nodes of WSNs using mobile agents such as ground vehicles or drones. The use of mobile agents can significantly reduce the energy consumption of sensor nodes relative to other modes of extracting the data, extending the lifetime and capabilities of the WSN. Moreover, in remote areas where GPS may not be feasible due to limited power resources on the sensor nodes, the need for accurate sensor node localization and signal broadcasting model estimation becomes critical. Therefore, designing the trajectory of mobile agents is crucial for rapid data collection and information gathering while adhering to vehicle constraints such as dynamics and energy usage. In this thesis, we focus on the application of optimal control methods to design trajectories for mobile agents in data harvesting. This thesis makes contributions in three areas: the creation of a parameterized optimal control policy, the application of a Deep Reinforcement Learning (DRL) based control, and the use of Fisher Information (FI) as a cost matrix in a Receding Horizon Control (RHC) method.Parameterized Optimal Control Policy: Our contributions in this area begin by considering a data harvesting problem in 1-D space. We use a Hamiltonian analysis to show that the optimal control can be described using a parameterized policy and then develop a gradient descent scheme using Infinitesimal Perturbation Analysis (IPA) to calculate the gradients of the cost function with respect to the control parameters. We also consider this problem in a multi-agent setting. To avoid collisions between agents, we apply a Control Barrier Function (CBF) technique to ensure the agents closely track the desired optimal trajectory to complete their mission while avoiding any collisions. Finally, we extend the problem to a mobile sensor scenario. In this more complicated setting we demonstrate that the optimization problem for the control policy parameters can be effectively solved using a heuristic approach. Deep-Reinforcement-Learning based Control: The parametric optimal control approach cannot be easily extended from the 1-D setting to 2-D space. For this reason, we turn to DRL techniques. We utilize Hamiltonian analysis again to get the necessary conditions for optimal control and then translate the problem to a Markov Decision Process (MDP) in discrete time. We apply reinforcement learning techniques, including double deep Q-learning and Proximal Policy Optimization (PPO), to find high-performing solutions across different scenarios. We demonstrate the effectiveness of these methods in 2-D simulations.Fisher-Information-based Receding Horizon Control: For the data harvesting problem in large scale unknown environments, estimating the parameters defining the broadcast model and the location of all the nodes in the environment is critical for efficient extraction of the data. To address that, we start with a Received Signal Strength (RSS) model that relies on a Line-of-Sight (LoS) path-loss model with measurements that are corrupted by Gaussian distributed noise. We first consider a single agent tasked with estimating these unknown parameters in discrete time, and then develop a Fisher Information Matrix (FIM) Receding Horizon (RH) controller for agent motion planning in real time. We also design a Neural Network (NN)-based controller to approximate the optimal solution to the Hamilton-Jacobi-Bellman (HJB) problem, maximizing information gain along a continuous time trajectory. Additionally, a two-stage formation-based RH controller is designed for multi-agent scenarios. The experiments demonstrate that the optimal control policy contribute to the high performance of data collection and the FI-based RHC methods enhance the estimation accuracy in various simulation environments.
546 $a English
590 $a School code: 0017
650 4 $a Mechanical engineering. $3 649730
650 4 $a Robotics. $3 519753
650 4 $a Systems science. $3 3168411
650 4 $a Computer engineering. $3 621879
653 $a Estimation accuracy
653 $a Multi-agent system
653 $a Optimal control
653 $a Receding Horizon Control
653 $a Deep Reinforcement Learning
653 $a Wireless Sensor Networks
690 $a 0548
690 $a 0771
690 $a 0790
690 $a 0464
710 2 $a Boston University. $b Mechanical Engineering ENG. $e degree granting institution. $3 3815985
720 1 $a Andersson, Sean B. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-07B.
790 $a 0017
791 $a Ph.D.
792 $a 2025
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