UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Equilibrium Programming for Improved Management of Water-Resource Systems(2024) Boyd, Nathan Tyler; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Effective water-resources management requires the joint consideration of multiple decision-makers as well as the physical flow of water in both built and natural environments. Traditionally, game-theory models were developed to explain the interactions of water decision-makers such as states, cities, industries, and regulators. These models account for socio-economic factors such as water supply and demand. However, they often lack insight into how water or pollution should be physically managed with respect to overland flow, streams, reservoirs, and infrastructure. Conversely, optimization-based models have accounted for these physical features but usually assume a single decision-maker who acts as a central planner. Equilibrium programming, which was developed in the field of operations research, provides a solution to this modeling dilemma. First, it can incorporate the optimization problems of multiple decision-makers into a single model. Second, the socio-economic interactions of these decision-makers can be modeled as well such as a market for balancing water supply and demand. Equilibrium programming has been widely applied to energy problems, but a few recent works have begun to explore applications in water-resource systems. These works model water-allocation markets subject to the flow of water supply from upstream to downstream as well as the nexus of water-quality management with energy markets. This dissertation applies equilibrium programming to a broader set of physical characteristics and socio-economic interactions than these recent works. Chapter 2 also focuses on the flow of water from upstream to downstream but incorporates markets for water recycling and reuse. Chapter 3 also focuses on water-quality management but uses a credit market to implement water-pollution regulations in a globally optimal manner. Chapter 4 explores alternative conceptions for socio-economic interactions beyond market-based approaches. Specifically, social learning is modeled as a means to lower the cost of water-treatment technologies. This dissertation's research contributions are significant to both the operations research community and the water-resources community. For the operations research community, this dissertation could serve as model archetypes for future research into equilibrium programming and water-resource systems. For instance, Chapter 1 organizes the research in this dissertation in terms of three themes: stream, land, and sea. For the water-resources community, this dissertation could make equilibrium programming more relevant in practice. Chapter 2 applies equilibrium programming to the Duck River Watershed (Tennessee, USA), and Chapter 3 applies it to the Anacostia River Watershed (Washington DC and Maryland, USA). The results also reinforce the importance of the relationships between socio-economic interactions and physical features in water resource systems. However, the risk aversion of the players acts as an important mediating role in the significance of these relationships. Future research could investigate mechanisms for the emergence of altruistic decision-making to improve equity among the players in water-resource systems.Item Efficient Media Access Control and Distributed Channel-aware Scheduling for Wireless Ad-Hoc Networks(2013) Chen, Hua; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)We address the problem of channel-aware scheduling for wireless ad-hoc networks, where the channel state information (CSI) are utilized to improve the overall system performance instead of the individual link performance. In our framework, multiple links cooperate to schedule data transmission in a decentralized and opportunistic manner, where channel probing is adopted to resolve collisions in the wireless medium. In the first part of the dissertation, we study this problem under the assumption that we know the channel statistics but not the instant CSI. In this problem, channel probing is followed by a transmission scheduling procedure executed independently within each link in the network. We study this problem for the popular block-fading channel model, where channel dependencies are inevitable between different time instances during the channel probing phase. We use optimal stopping theory to formulate this problem, but at carefully chosen time instances at which effective decisions are made. The problem can then be solved by a new stopping rule problem where the observations are independent between different time instances. We first characterize the system performance assuming the stopping rule problem has infinite stages. We then develop a measure to check how well the problem can be analyzed as an infinite horizon problem, and characterize the achievable system performance if we ignore the finite horizon constraint and design stopping rules based on the infinite horizon analysis. We then analyze the problem using backward induction when the finite horizon constraint cannot be ignored. We develop one recursive approach to solve the problem and show that the computational complexity is linear with respect to network size. We present an improved protocol to reduce the probing costs which requires no additional cost. Based on our analysis on single-channel networks, we extend the problem to ad-hoc networks where the wireless spectrum can be divided into multiple independent sub-channels for better efficiency. We start with a naive multi-channel protocol where the scheduling scheme is working independently within each sub-channel. We show that the naive protocol can only marginally improve the system performance. We then develop a protocol to jointly consider the opportunistic scheduling behavior across multiple sub-channels. We characterize the optimal stopping rule and present several bounds for the network throughputs of the multi-channel protocol. We show that by joint optimization of the scheduling scheme across multiple sub-channels, the proposed protocol improves the system performance considerably in contrast to that of single-channel systems. In the second part of the dissertation, we study this problem under the assumption that neither the instant CSI nor the channel statistics are known. We formulate the channel-aware scheduling problem using multi-armed bandit (MAB). We first present a semi-distributed MAB protocol which serves as the baseline for performance comparison. We then propose two forms of distributed MAB protocols, where each link keeps a local copy of the observations and plays the MAB game independently. In Protocol I the MAB game is only played once within each block, while in Protocol II it can be played multiple times. We show that the proposed distributed protocols can be considered as a generalized MAB procedure and each link is able to update its local copy of the observations for infinitely many times. We analyze the evolution of the local observations and the regrets of the system. For Protocol I, we show by simulation results that the local observations that are held independently at each link converge to the true parameters and the regret is comparable to that of the semi-distributed protocol. For Protocol II, we prove the convergence of the local observations and show an upper bound of the regret.Item RESOURCE AND ENVIRONMENT AWARE SENSOR COMMUNICATIONS: FRAMEWORK, OPTIMIZATION, AND APPLICATIONS(2005-12-02) Pandana, Charles; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advances in low power integrated circuit devices, micro-electro-mechanical system (MEMS) technologies, and communications technologies have made possible the deployment of low-cost, low power sensors that can be integrated to form wireless sensor networks (WSN). These wireless sensor networks have vast important applications, i.e.: from battlefield surveillance system to modern highway and industry monitoring system; from the emergency rescue system to early forest fire detection and the very sophisticated earthquake early detection system. Having the broad range of applications, the sensor network is becoming an integral part of human lives. However, the success of sensor networks deployment depends on the reliability of the network itself. There are many challenging problems to make the deployed network more reliable. These problems include but not limited to extending network lifetime, increasing each sensor node throughput, efficient collection of information, enforcing nodes to collaboratively accomplish certain network tasks, etc. One important aspect in designing the algorithm is that the algorithm should be completely distributed and scalable. This aspect has posed a tremendous challenge in designing optimal algorithm in sensor networks. This thesis addresses various challenging issues encountered in wireless sensor networks. The most important characteristic in sensor networks is to prolong the network lifetime. However, due to the stringent energy requirement, the network requires highly energy efficient resource allocation. This highly energy-efficient resource allocation requires the application of an energy awareness system. In fact, we envision a broader resource and environment aware optimization in the sensor networks. This framework reconfigures the parameters from different communication layers according to its environment and resource. We first investigate the application of online reinforcement learning in solving the modulation and transmit power selection. We analyze the effectiveness of the learning algorithm by comparing the effective good throughput that is successfully delivered per unit energy as a metric. This metric shows how efficient the energy usage in sensor communication is. In many practical sensor scenarios, maximizing the energy efficient in a single sensor node may not be sufficient. Therefore, we continue to work on the routing problem to maximize the number of delivered packet before the network becomes useless. The useless network is characterized by the disintegrated remaining network. We design a class of energy efficient routing algorithms that explicitly takes the connectivity condition of the remaining network in to account. We also present the distributed asynchronous routing implementation based on reinforcement learning algorithm. This work can be viewed as distributed connectivity-aware energy efficient routing. We then explore the advantages obtained by doing cooperative routing for network lifetime maximization. We propose a power allocation in the cooperative routing called the maximum lifetime power allocation. The proposed allocation takes into account the residual energy in the nodes when doing the cooperation. In fact, our criterion lets the nodes with more energy to help more compared to the nodes with less energy. We continue to look at the problem of cooperation enforcement in ad-hoc network. We show that by combining the repeated game and self learning algorithm, a better cooperation point can be obtained. Finally, we demonstrate an example of channel-aware application for multimedia communication. In all case studies, we employ optimization scheme that is equipped with the resource and environment awareness. We hope that the proposed resource and environment aware optimization framework will serve as the first step towards the realization of intelligent sensor communications.