The unmanned aerial vehicles (UAVs) are also known as drones. Recently, UAVs have attracted the next generation researchers due to their flexible, dynamic, and cost-effective deployment, etc. Moreover, the UAVs have a wide range of application domains, such as rescue operation in the remote area, military surveillance, emergency application, etc. Given the UAVs are appropriately deployed, the UAVs provide continuous and reliable connectivity, on-demand, and cost-effective features to the desired destination in the wireless communication system. Thus, the UAVs can be a great choice to deploy as a mobile relay in co-existence with the base stations (BSs) on the ground to serve the 5G wireless users. In this thesis, the UAV-assisted mobile relay (UAV-MR) in the next generation wireless networks has been studied, which also considers the UAV-MR physical layer security. The proposed system also considers one ground user, one BS on the ground, and active presence of multiple eavesdroppers, situated nearby the ground user. The locations of these nodes (i.e., the ground user, the BS, and the eavesdroppers) are considered fixed on the ground. Moreover, the locations of the eavesdroppers are not precisely known to the UAV-MR. Thus, this thesis aims to maximize the achievable secrecy rate, while the BS sends the secure information to the ground user via the UAV-MR. However, the UAV-MR has some challenges to deploy in wireless networks, such as 3D deployment, robust resource allocation, secure UAV-MR to ground communication, the channel modeling, the UAV-MR flight duration, and the UAV-MR robust trajectory design, etc. Thus, this project investigates the UAV-MR assisted wireless networks, which addresses those technical challenges to guarantee efficient UAV-MR communication. Moreover, the mathematical frameworks are formulated to support the proposed model. An efficient algorithm is proposed to maximize the UAV-MR achievable secrecy rate. Finally, the simulation results show the improved performance for the UAV-MR assisted next-generation networks.

This paper examines the funding procedure in Ontario long-term care facilities and seeks to identify whether current resources for protecting the elderly from mistreatment is allocated fairly and effectively. The research also observes how the political economy may influence the needs-based allocation built to protect seniors from mistreatment in institutional care settings and the consequences of these resources on residents’ autonomy. The topic is also viewed through the lens of the current COVID-19 pandemic. Five experts in the area of long-term care participated in this research work including academics, scholars and institutional or agency advocates. Interviews lasting up to 60 minutes interviews were conducted, transcribed and analyzed using a political economy lens. Participants described their knowledge and experience with the funding procedure for long term-care facilities, particularly in Ontario and provided their view on areas that they felt could be improved. The analysis identified four themes including whether the issue is under-resourced, poor allocation of resources; funding according to need; the struggle to define and assess the quality of care; and general work conditions in long-term care. The result of this research will help us to better understand the resource allocation of Ontario long-term care facilities which could in turn highlight improvements that could be made to create better quality of life for residents as well as frontline workers. / Thesis / Master of Arts (MA) / This paper examines the funding procedure in Ontario long-term care facilities and seeks to identify whether current resources for protecting the elderly from mistreatment is allocated fairly and effectively. The topic is viewed through the lens of the COVID-19 pandemic. The analysis of the five expert interviews identified four themes including whether the issue is under-resourced, poor allocation of resources; funding according to need; the struggle to define and assess the quality of care; and general work conditions in long-term care. The result of this research will help us to better understand the resource allocation of Ontario long-term care facilities which could in turn highlight improvements that could be made to create better quality of life for residents as well as frontline workers.

The cost of reproduction hypothesis suggests that allocation into current reproduction constrains future reproduction. How organisms accrue reproductive costs may differ between species and with varying levels of resource quality. Burying beetles are model organisms for testing for the cost of reproduction because of their unique natural history; beetles utilize small vertebrate carcasses for reproduction and they and their offspring feed exclusively on these discrete resources. Burying beetles also can utilize a large range of carcass sizes for reproduction. We tested for the cost of reproduction in two species of burying beetles, Nicrophorus marginatus and Nicrophorus guttula found in Central Utah by breeding beetles on a range of carcass sizes (5g, 10g, 20g, 30g, 40g, and 50g carcasses). We also used a manipulation experiment to force beetles into over-allocating energy into reproduction to assess reproductive costs. For both species, reproduction was costly, with beetles suffering reduced lifespan and reduced lifetime fecundity with increased resource quality. Both species also showed clear signs of senescence, having reduced brood size and lower efficiency as individuals aged. Females did not show indications of terminal investment in terms of female mass change, unlike the previously studied Nicrophorus orbicollis, which gained less mass after each reproductive attempt as it aged. Nicrophorus marginatus consistently outperformed N. guttula in terms of total number of offspring produced for all carcass sizes. Nicrophorus guttula populations may continue to persist with N. marginatus by exploiting a less desirable but more abundant resource.

This dissertation consists of three chapters on the topic of intrahousehold resource allocation models. The first chapter tests the unitary and general collective models of intrahousehold resource allocation for various household compositions. I find that, for the quasiquadratic Engel curve specification, the overall results support the previous findings in the literature that the unitary model fails to explain how resources are allocated for all household types. However, when using the QUAIDS specification, the results can reject the unitary model only for smaller-sized households. The general collective model, on the other hand, cannot be rejected in either quasi-quadratic or QUAIDS and not in any of the household compositions. Overall, the results support the general collective model of household behavior rather than the unitary model. The second chapter derives and tests restrictions imposed by the collective model for households with more than two decision-makers in the absence of price variation. It extends the two-decision-maker model in chapter one to derive the testable restrictions for households with multiple decision makers using unconditional demand systems. Moreover, for comparison, a particular type of demand system that is conditional on distribution factors is also estimated as an alternative way to test the collective model. The results show that neither unconditional nor conditional demand systems can reject Pareto efficiency. Therefore, both approaches provide consistent outcomes supporting the hypothesis that the multiple-decision-maker households in Thailand behave in the Pareto efficient manner predicted by the collective model. Finally, my third chapter attempts to examine how one can exploit household-level consumption data to recover information about individual household members for situations with iv no price variation. By combining consumption data from single and couple households, I am able to estimate the resource shares and indifference scales (a variation of the standard equivalence scales in the collective settings) for each household member via a system of Engel curves. The results show that, in Thailand, wives are likely to have higher resource shares than husbands in the married-couple households, while wives with higher education have the ability to extract more household resources. However, resource shares for wives are smaller for older-married compared to younger-married couples. Moreover, if a female were to live alone, she would need approximately three-quarters of the couple‟s income to reach the same indifference curve, and hence the same standard of living, that she would attain as a wife in the married-couple household.

The problem of constructing and analyzing systems of intelligent, autonomous agents is becoming more and more important. These agents may include people, physical robots, virtual humans, software programs acting on behalf of human beings, or sensors. In a large class of multi-agent scenarios, agents may have different capabilities, preferences, objectives, and constraints. Therefore, efficient allocation of resources among multiple agents is often difficult to achieve. Automated negotiation (bargaining) is the most widely used approach for multi-agent resource allocation and it has received increasing attention in the recent years. However, information uncertainty, existence of multiple contracting partners and competitors, agents' incentive to maximize individual utilities, and market dynamics make it difficult to calculate agents' rational equilibrium negotiation strategies and develop successful negotiation agents behaving well in practice. To this end, this thesis is concerned with analyzing agents' rational behavior and developing negotiation strategies for a range of complex negotiation contexts. First, we consider the problem of finding agents' rational strategies in bargaining with incomplete information. We focus on the principal alternating-offers finite horizon bargaining protocol with one-sided uncertainty regarding agents' reserve prices. We provide an algorithm based on the combination of game theoretic analysis and search techniques which finds agents' equilibrium in pure strategies when they exist. Our approach is sound, complete and, in principle, can be applied to other uncertainty settings. Simulation results show that there is at least one pure strategy sequential equilibrium in 99.7% of various scenarios. In addition, agents with equilibrium strategies achieved higher utilities than agents with heuristic strategies. Next, we extend the alternating-offers protocol to handle concurrent negotiations in which each agent has multiple trading opportunities and faces market competition. We provide an algorithm based on backward induction to compute the subgame perfect equilibrium of concurrent negotiation. We observe that agents' bargaining power are affected by the proposing ordering and market competition and for a large subset of the space of the parameters, agents' equilibrium strategies depend on the values of a small number of parameters. We also extend our algorithm to find a pure strategy sequential equilibrium in concurrent negotiations where there is one-sided uncertainty regarding the reserve price of one agent. Third, we present the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents are designed to adjust 1) the number of tentative agreements and 2) the amount of concession they are willing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by 1) the likelihood that negotiation will not be successfully completed, 2) the expected agreement price of the resource, and 3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Since agents are permitted to decommit from agreements, a buyer may make more than one tentative agreement for each resource and the maximum number of tentative agreements is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly higher utilities than simpler strategies. Finally, we consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. While numerous auction-based approaches have been proposed in the literature, our work explores an alternative approach where providers and consumers negotiate resource leasing contracts. We propose a distributed negotiation mechanism where agents negotiate over both a contract price and a decommitment penalty, which allows agents to decommit from contracts at a cost. We compare our approach experimentally, using representative scenarios and workloads, to both combinatorial auctions and the fixed-price model, and show that the negotiation model achieves a higher social welfare.

The demand for Internet-based visual content delivery has increased significantly in recent years, triggered mainly by the widespread use of Internet enabled smartphones and portable devices, and by the availability of super HD content.As a consequence, live and on-demand video content has become the most important source of network traffic in mobile and fixed networks alike.In order to be able to efficiently deliver the increasing amount of video traffic, network operators have started to deploy caches and operator-owned CDNs. These solutions do not only reduce the amount of transit traffic of the operators but they may also improve the customers' quality of experience, through bringing the video content closer to customers. Nevertheless, their efficiency is determined by the algorithms and protocols used to allocate resources, both in terms of storage and bandwidth. The work in this thesis addresses the allocation of these two resources for operator-owned content management systems. In the first part of the thesis we consider a cache maintained by a single network operator. We investigate how caching at a network operator affects the content distribution system as a whole, and consequently, the efficiency of content delivery. We propose a model of the decision process undertaken by a network operator that aims at optimizing the efficiency of a cache by actively managing its bandwidth. We design different algorithms that aim at approximating the optimal cache bandwidth allocation and we evaluate them through extensive simulations and experiments. We show that active cache bandwidth allocation can significantly increase traffic savings. We then consider the potential interaction among caches maintained by different network operators.We consider the problem of selfish replication on a graph as a modelof network operators that individually deploy replication systems, and try to leverage their peering agreements so as to minimize the traffic through their transit providers. We use game-theoretical tools to investigate the existence of stable and efficient allocations of content at the network operators. We show that selfish myopic updates of content allocations at different network operators lead the system to a stable state, and that the convergence speed depends on the underlying network topology. In addition, we show that interacting operator-owned caches can reach a stable content allocation without coordination, but coordination leads to more cost efficient content allocations. / <p>QC 20140401</p>

In this thesis, we develop efficient algorithms for the jointly optimal power and channel resource allocation in different wireless cooperative multiple access systems. In addition, in some cases insight into the structure of the optimal allocation enables the development of modified cooperation schemes with better performance, and efficient algorithms are developed for jointly optimal power and channel resource allocation for these modified schemes too. The goal of the jointly optimal allocation algorithms developed in this thesis is to maximize the achievable rate regions of the schemes under consideration. Two cooperative channel models are considered; namely, the cooperative multiple access channel, and the multiple access relay channel. For the cooperative multiple access channel, two relaying strategies are considered; namely decode-and-forward (DF), and amplify-and-forward (AF). For the cooperative multiple access channel with DF relaying, systems with full-duplex nodes and systems with half-duplex nodes are considered. In the case of full-duplex nodes, it is shown that the non-convex formulation of the power allocation problem can be simplified and re-cast in a convex form. In fact, closed-form expressions for the optimal power allocation for each point on the boundary of an achievable rate region are obtained. In the case of half-duplex nodes, we propose a modified version of an existing cooperation scheme that, with jointly optimal power and channel resource allocation, can achieve a large fraction of the achievable rate region of the full-duplex case. An efficient algorithm for the jointly optimal power and channel resource allocation is also developed for that scheme. For the cooperative multiple access channel with AF relaying, we consider optimal power and channel resource allocation for a system of two half-duplex source nodes that transmit orthogonal signals, and an efficient algorithm for the optimal power and channel resource allocation is developed. This efficient algorithm is based on a closed-form solution for the optimal power allocation for a given channel resource allocation and on showing that the channel resource allocation problem is quasi-convex. The analysis of the optimal power allocation for a given channel resource allocation shows that the existing scheme that we consider does not use the channel resource efficiently. Therefore, we propose a modified cooperation scheme that maintains the orthogonality property of the original scheme, but provides larger achievable rate regions than those provided by the original scheme. For the multiple access relay channel, the optimal allocation of the relay power and the channel resource between different source nodes is considered in order to maximize the achievable rate region. Four relaying strategies are used; namely, regenerative decode-and-forward, non-regenerative decode-and-forward, amplify-and-forward, and compress-and-forward. For each of these strategies, an efficient algorithm is developed for the jointly optimal power and channel resource allocation. These algorithms are based on closed-form solutions for the optimal power allocation for a given resource allocation and on proving and exploiting the quasi-convexity of the joint allocation problem. The algorithms developed for the multiple access relay channel can be used for homogeneous (using the same relaying strategy for all users) or heterogeneous (using different relaying strategies with different users) relaying and for any number of users. / Thesis / Doctor of Philosophy (PhD)

Addressing fairness among users and risk mitigation in the context of resource allocation in transportation systems under uncertainties poses a crucial challenge yet to be satisfactorily resolved. This dissertation attempts to address this challenge, focusing on achieving a balance between system-wide efficiency and individual fairness in stochastic transportation resource allocation problems. To study complicated fair and risk-averse resource allocation problems - from public transit to urban air mobility and multi-stage infrastructure maintenance - we develop three models: DrFRAM, FairUAM, and FCMDP. Each of these models, despite being proven NP-hard even in a simplistic case, inspires us to develop efficient solution algorithms. We derive mixed-integer linear programming (MILP) formulations for these models, leveraging the unique properties of each model and linearizing non-linear terms. Additionally, we strengthen these models with valid inequalities. To efficiently solve these models, we design exact algorithms and approximation algorithms capable of obtaining near-optimal solutions. We numerically validate the effectiveness of our proposed models and demonstrate their capability to be applied to real-world case studies to adeptly address the uncertainties and risks arising from transportation systems. This dissertation provides a foundational platform for future inquiries of risk-averse resource allocation strategies under uncertainties for more efficient, equitable, and resilient decision-making. Our adaptable framework can address a variety of transportation-related challenges and can be extended beyond the transportation domain to tackle resource allocation problems in a broader setting. / Doctor of Philosophy / In transportation systems, decision-makers constantly strive to devise the optimal plan for the most beneficial outcomes when facing future uncertainties. When optimizing overall efficiency, individual fairness has often been overlooked. Besides, the uncertainties in the transportation systems raise serious questions about the adaptability of the allocation plan. In response to these issues, we introduce the concept of fair and risk-averse resource allocation under uncertainties in this dissertation. Our goal is to formulate the optimal allocation plan that is both fair and risk-averse amid uncertainties. To tackle the complexities of fair and risk-averse resource allocation problems, we propose innovative methods and practical algorithms, including creating novel formulations as well as deriving super-fast algorithms. These solution approaches are designed to accommodate the fairness, uncertainties, and risks typically in transportation systems. Beyond theoretical results, we apply our frameworks and algorithms to real-world case studies, thus demonstrating our approaches' adaptability to various transportation systems and ability to achieve various optimization goals. Ultimately, this dissertation aims to contribute to fairer, more efficient, and more robust transportation systems. We believe our research findings can help decision-makers with well-informed choices about resource allocation in transportation systems, which, in turn, lead to the development of more equitable and reliable systems, benefiting all the stakeholders.

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The limited energy and computing power of small smart devices restricts their ability to support a wide range of applications, especially those needing quick responses. Mobile edge computing offers a potential solution by providing computing resources at the network access points that can be shared by the devices. This enables the devices to offload some of their computational tasks to the access points. To make this work well for multiple devices, we need to judiciously allocate the available communication and computing resources among the devices. The main focus of this thesis is on (near) optimal resource allocation in a K-user offloading system that employs the time division multiple access (TDMA) scheme. In this thesis, we develop effective algorithms for the resource allocation problem that aim to minimize the overall (cost of the) energy that the devices consume in completing their computational tasks within the specified deadlines while respecting the devices' constraints. This problem is tackled for tasks that cannot be divided and hence the system must make a binary decision as to whether or not a task should be offloaded. This implies the need to develop an effective decision-making algorithm to identify a suitable group of devices for offloading. This thesis commences by developing efficient communication resource algorithms that incorporate the impact of integer finite block length in low-latency computational offloading systems with reserved computing resources. In particular, it addresses the challenge of minimizing total energy consumption in a binary offloading scenario involving K users. The approach considers different approximations of the fundamental rate limit in the finite block length regime, departing from the conventional asymptotic rate limits developed by Shannon. Two such alternatives, namely the normal approximation and the SNR-gap approximation, are explored. A decomposition approach is employed, dividing the problem into an inner component that seeks an optimal solution for the communication resource allocation within a defined set of offloading devices, and an outer component aimed at identifying a suitable set of offloading devices. Given the finiteness of the block length and its integer nature, various relaxation techniques are employed to determine an appropriate communication resource allocation. These include incremental and independent roundings, alongside an extended search that utilizes randomization-based methods in both rounding schemes. The findings reveal that incremental randomized rounding, when applied to the normal approximation of the rate limits, enhances system performance in terms of reducing the energy consumption of the offloading users. Furthermore, customized pruned greedy search techniques for selecting the offloading devices efficiently generate good decisions. Indeed, the proposed approach outperforms a number of existing approaches. In the second contribution, we develop efficient algorithms that address the challenge of jointly allocating both computation and communication resources in a binary offloading system. We employ a similar decomposition methodology as in the previous work to perform the decision-making, but this is now done along with joint computation and communication resource allocation. For the inner resource allocation problem, we divide the problem into two components: determining the allocation of computation resources and the optimal allocation of communication resources for the given allocation of computation resources. The allocation of the computation resources implicitly determines a suitable order for data transmission, which facilitates the subsequent optimal allocation of the communication resources. In this thesis, we introduce two heuristic approaches for allocating the computation resources. These approaches sequentially maximize the allowable transmission time for the devices in sequence, starting from the largest leading to a reduction in total offloading energy. We demonstrate that the proposed heuristics substantially lower the computational burden associated with solving the joint computation--communication resource allocation problem while maintaining a low total energy. In particular, its use results in substantially lower energy consumption than other simple heuristics. Additionally, the heuristics narrow the energy gap in comparison to a fictitious scenario in which each task has access to the whole computation resource without the need for sharing. / Thesis / Master of Applied Science (MASc)