In the push for clean and renewable fuels, timber derived biomass is a promising frontier for biofuel production in the United States. This thesis approaches the established timberlands biofuel implementation problem with three different mathematical programming studies, each testing feasibility and sustainability in different economic and supply related situations. In the first study, a competitive game theory approach was utilized to provide new insights into the behavior within a timberlands supply chain. We utilized Stackelberg game theory modeled with bilevel programming to represent the competing harvesting and manufacturing sectors. In the second study, the initial bilevel model was utilized in a larger two stage multiperiod model with parameter uncertainty. In this more realistic model, the first stage contained logistical decisions around biorefinery investments, such as location and capacity, while the second stage was composed of multiple discrete bilevel scenarios representing potential situations in the timberlands system. The final study focused on long term land management strategies for the timberlands supply chain. Introduction of a new biorefinery investment meant that management strategies must be altered to ensure consistent material flows to manufacturers as well as sustain the new production facility. A modified cyclic scheduling formulation was used to model a timberlands system and its planting and harvesting schedule to accommodate a new biorefinery. This cyclic model added an initial startup period to initiate biofuel production and provide time to adapt land management. The overall contribution of these studies was to analyze a biorefinery's impact on the established behavior in a timberlands supply chain. In particular, the goals of these models were to develop introductory decision making tools for timberlands supply chain managers.

Most of the cellular tra c (voice and data) is generated indoors. Due to attenuation from walls, quality of service (QoS) of di erent applications degrades for indoor tra c. Thus in order to provide QoS for such users the Macro base station (MBS) has to transmit at high power. This increases recurring costs to the service provider and contributes to green house emissions. Hence, Femtocells (FC) are considered as an option. Femto Access Points (FAP) are low cost, low powered, small base stations deployed indoors by customers. A substantial part of indoor tra c is diverted from the Macrocell (MC) through the FAP. Since the FCs also use the same channels as the MC, deployment of FCs causes interference to not only its neighbouring FCs but also to the users in the MC. Thus, we need better interference management techniques for this system. In this thesis, we consider a system with multiple Femtocells operating in a Macrocell. FCs and MC use same set of multiple channels and support multiple users. Each user may have a minimum rate requirement. To limit interference to the MC, there is a peak power constraint on each channel. In the rst part of the thesis, we consider sparsely deployed FCs where the interference between the FCs is negligible. For this we formulate the problem of channel allocation and power control in each FC. We develop computationally e cient, suboptimal algorithms to satisfy QoS of each user in the FC. If QoS of each user is not satis ed, we provide solutions which are fair to all the users. In the second part of the thesis, we consider the case of densely deployed FCs where we formulate the problem of channel allocation and power control in each Femtocell as a noncooperative Game. We develop e cient decentralized algorithms to obtain a Nash equilibrium (NE) at which QoS of each user is satis ed. We also obtain e cient decentralized algorithms to obtain fair NE when it may not be feasible to satisfy the QoS of all the users in the FC. Finally, we extend our algorithms to the case where there may be voice and data users in the system. In the third part of the thesis, we continue to study the problem setup in the second part, where we develop algorithms which can simultaneously consider the cases where QoS of users can be satis ed or not. We provide algorithms to compute Coarse Correlated Equilibrium (CCE), Pareto optimal points and Nash bargaining solutions. In the nal part of the thesis, we consider interference limit at the MBS and model FCs as sel sh nodes. The MBS protects itself via pricing subchannels per usage. We obtain a Stackelberg equilibrium (SE) by considering MBS as a leader and FCs as followers.

<p dir="ltr">Amidst the rising popularity of digital marketplaces, addressing issues such as non-<br>payment/non-delivery crimes, centralization risks, hacking threats, and the complexity of<br>ownership transfers has become imperative. Many existing studies exploring blockchain<br>technology in digital marketplaces and asset management merely touch upon various application scenarios without establishing a unified platform that ensures trustworthiness and<br>efficiency across the product life cycle. In this thesis, we focus on designing a reliable and efficient e-commerce model to trade various assets. To enhance customer engagement through<br>consensus, we utilize the XGBoost algorithm to identify loyal nodes from the platform entities pool. Alongside appointed nodes, these loyal nodes actively participate in the consensus<br>process. The consensus algorithm guarantees that all involved nodes reach an agreement on<br>the blockchain’s current state. We introduce a novel consensus mechanism named Modified-<br>Practical Byzantine Fault Tolerance (M-PBFT), derived from the Practical Byzantine Fault<br>Tolerance (PBFT) protocol to minimize communication overhead and improve overall efficiency. The modifications primarily target the leader election process and the communication<br>protocols between leader and follower nodes within the PBFT consensus framework.</p><p dir="ltr"><br>In the domain of tangible assets, our primary objective is to elevate trust among various<br>stakeholders and bolster the reputation of sellers. As a result, we aim to validate secondhand<br>products and their descriptions provided by the sellers before the secondhand products are<br>exchanged. This validation process also holds various entities accountable for their actions.<br>We employ validators based on their location and qualifications to validate the products’<br>descriptions and generate validation certificates for the products, which are then securely<br>recorded on the blockchain. To incentivize the participation of validator nodes and up-<br>hold honest validation of product quality, we introduce an incentive mechanism leveraging<br>Stackelberg game theory.</p><p dir="ltr"><br>On the other hand, for optimizing intangible assets management, we employ Non-Fungible<br>Tokens (NFT) technology to tokenize these assets. This approach enhances traceability of<br>ownership, transactions, and historical data, while also automating processes like dividend<br>distributions, royalty payments, and ownership transfers through smart contracts. Initially,<br>sellers mint NFTs and utilize the InterPlanetary File System (IPFS) to store the files related<br>to NFTs, NFT metadata, or both since IPFS provides resilience and decentralized storage solutions to our network. The data stored in IPFS is encrypted for security purposes.<br>Further, to aid sellers in pricing their NFTs efficiently, we employ the Stackelberg mechanism. Furthermore, to achieve finer access control in NFTs containing sensitive data and<br>increase sellers’ profits, we propose a Popularity-based Adaptive NFT Management Scheme<br>(PANMS) utilizing Reinforcement Learning (RL). To facilitate prompt and effective asset<br>sales, we design a smart contract-powered auction mechanism.</p><p dir="ltr"><br>Also, to enhance data recording and event response efficiency, we introduce a weighted<br>L-H index algorithm and transaction prioritization features in the network. The weighted<br>L-H index algorithm determines efficient nodes to broadcast transactions. Transaction prior-<br>itization prioritizes certain transactions such as payments, verdicts during conflicts between<br>sellers and validators, and validation reports to improve the efficiency of the platform. Simulation experiments are conducted to demonstrate the accuracy and efficiency of our proposed<br>schemes.<br></p>