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Today there exist no standardized payment solution for performing micropayments between Internet of Things (IoT) devices. This study was conducted to examine whether Distributed Ledger Technology (DLT) could be suitable as a micropayment solution for IoT. Also, a more general demand for a scalable micropayment solution was examined, along with its potential. A qualitative study was performed by first conducting eight unstructured interviews regarding the subjects DLT and IoT, to be used as a complement to the literature research. Then, one unstructured and five semi-structured interviews were held to answer the research questions. The Bitcoin blockchain does not work as a micropayment solution, due to scalability issues. This study identified a positive outlook on the idea of Lightning Network, solving the scalability problems with off-chain transactions. However, since a fully functioning network is yet to be implemented, there exist uncertainties, for example regarding how decentralized it will really become. Also, issues considering the usage of DLT:s on small IoT devices arose, stemming from CPU and storage constraints. A demand of a sustainable micropayment solution was identified, possibly being a catalyst of the emergence of pay-per-use business models. Considering more powerful IoT devices, the Lightning Network could function as a micropayment solution. Such a technology is sought after, and its applicability will only increase as IoT devices evolve. / Det finns idag ingen standardiserad betalningslösning för att genomföra mikrobetalningar mellan Internet of Things (IoT) enheter. Denna studie genomfördes för att undersöka huruvida Distributed Ledger Technology (DLT) skulle kunna användas som en mikrobetalningslösning för IoT. En mer generell eterfrågan för en skalbar mikrobetalningslösning, och effekterna av en sådan, undersöktes. En kvalitativ studie genofördes, där åtta ostrukturerade intervjuer gällande ämnena DLT och IoT, hölls för att komplementera litteraturstudierna. Sedan genomfördes en ostrukturerad och fem semi-strukturerade intervjuer för att kunna besvara de frågeställningar som definierats. Bitcoin blockkedjan funderar inte som en mikrobetalningslösning på grund utav dess skalbarhetsproblem. Studien identifierar en positiv syn på Lightning Network, som löserskalbarhetsproblemen genom att använda sig av transaktioner utanför kedjan. Denna lösning är dock inte fullständigt implementerad, vilket leder till flera osäkerheter angående exempelvis hur decentraliserat nätverket verkligen kommer att bli. Utöver detta finns även svårigheter med användandet av DLT:s för små IoT-enheter, vilket härstämmar ifrån deras CPU- och lagringsbegränsningar. En efterfrågan på en hållbar mikrobetalningslösning identifieras, och denna skulle kunna fungera som en katalysator för etablerandet av pay-per-use affärsmodeller. Tittar vi på mer kraftfulla IoT-enheter skulle Lightning Network fungera som en mikrobetalningslösning. En sådan teknologi är eftertraktad och dess användbarhet kommer bara att växa i och med utvecklingen av IoT-enheter.

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In this thesis, we study large-scale dynamic optimization problems in the context of inventory management. We analyze inventory problems with constraints coupling the items or facility locations in the inventory systems, and we propose efficient solutions that are asymptotically optimal or empirically near-optimal. In Chapter 1, we analyze multi-item, single-location inventory systems with storage capacity limits which are formulated as both unconditional expected value constraints and unconditional probability constraints. We first show that problems with unconditional expected value constraints only can be solved to optimality through Lagrangian relaxation. Then, under an assumption on the correlation structure of the demands that is valid under most practical setting, we show that the original problem can be sandwiched between two other problems with expected value constraints only. One of these problems yields a feasible solution to the original problem that is asymptotically optimal as the number of items grows. In Chapter 2, we consider the same problem but with conditional probability constraints, that impose limits on overflow frequency for every possible state in each period. We construct an efficient feasible solution in two steps. First, we solve an unconditional expected value constrained problem with reduced capacity. Second, in each period, given the state information, we solve a single-period convex optimization problem with a conditional expected value constraint. We further show that the heuristic is asymptotically optimal as number of items I grows. In addition, we design another efficient method for moderate values of I, which works empirically well in an extensive numerical study. Moreover, we extract key managerial insights from the numerical study which are critical to decision making in real business problems. In Chapter 3, we analyze single-item, multi-location systems on inventory networks that can be described by directed acyclic graphs (DAG). We propose an innovative reformulation of the problem so that Lagrangian relaxation can still be applied, which, instead of decomposing the problem by facility location, aggregates the state information, leading to a tractable lower bound approximation for the problem. The Lagrange multiplier, which provides information on the value function from the lower bound dynamic program, is used in designing a feasible heuristic. An extensive numerical study is conducted which suggests that both the lower bound approximation and upper bound heuristic perform very well.

Advanced architecture processors provide features such as caches and branch prediction that result in improved, but variable, execution time of software. Hard real-time systems require tasks to complete within timing constraints. Consequently, hard real-time systems are typically designed conservatively through the use of tasks? worst-case execution times (WCET) in order to compute deterministic schedules that guarantee task?s execution within giving time constraints. This use of pessimistic execution time assumptions provides real-time guarantees at the cost of decreased performance and resource utilization. In soft real-time systems, however, meeting deadlines is not an absolute requirement (i.e., missing a few deadlines does not severely degrade system performance or cause catastrophic failure). In such systems, a guaranteed minimum probability of completing by the deadline is sufficient. Therefore, there is considerable latitude in such systems for improving resource utilization and performance as compared with hard real-time systems, through the use of more realistic execution time assumptions. Given probability distribution functions (PDFs) representing tasks? execution time requirements, and tasks? communication and precedence requirements, represented as a directed acyclic graph (DAG), this dissertation proposes and investigates algorithms for constructing non-preemptive stochastic schedules. New PDF manipulation operators developed in this dissertation are used to compute tasks? start and completion time PDFs during schedule construction. PDFs of the schedules? completion times are also computed and used to systematically trade the probability of meeting end-to-end deadlines for schedule length and jitter in task completion times. Because of the NP-hard nature of the non-preemptive DAG scheduling problem, the new stochastic scheduling algorithms extend traditional heuristic list scheduling and genetic list scheduling algorithms for DAGs by using PDFs instead of fixed time values for task execution requirements. The stochastic scheduling algorithms also account for delays caused by communication contention, typically ignored in prior DAG scheduling research. Extensive experimental results are used to demonstrate the efficacy of the new algorithms in constructing stochastic schedules. Results also show that through the use of the techniques developed in this dissertation, the probability of meeting deadlines can be usefully traded for performance and jitter in soft real-time systems.

This research presents a hybrid algorithm that combines List Scheduling (LS) with a Genetic Algorithm (GA) for constructing non-preemptive schedules for soft real-time parallel applications represented as directed acyclic graphs (DAGs). The execution time requirements of the applications' tasks are assumed to be stochastic and are represented as probability distribution functions. The performance in terms of schedule lengths for three different genetic representation schemes are evaluated and compared for a number of different DAGs. The approaches presented in this research produce shorter schedules than HLFET, a popular LS approach for all of the sample problems. Of the three genetic representation schemes investigated, PosCT, the technique that allows the GA to learn which tasks to delay in order to allow other tasks to complete produced the shortest schedules for a majority of the sample DAGs.

Bayesian network is a popular machine learning tool for modeling uncertain dependence relationships among the random factors of a domain. It represents the relations qualitatively by using a directed acyclic graph (DAG) and quantitatively by using a set of conditional probability distributions. Several exact algorithms for learning optimal Bayesian networks from data have been developed recently. However, these algorithms are still inefficient to some extent. This is not surprising because learning Bayesian network has been proven to be an NP-Hard problem. Based on a critique of these algorithms, this thesis introduces a new algorithm based on heuristic search for learning optimal Bayesian.

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This dissertation focuses on the computational investigation of gold(I) acyclic diaminocarbene (ADC) complexes and their application in homogeneous gold(I) catalysis. Chapter 2 is an in-depth computational investigation of the σ- and π-bonding interactions that make up the gold-carbene bond. Due to the inherent conformation flexibility of ADC ligands, distortions of the carbene plane can arise that disrupt orbital overlap between the lone pairs on the adjacent nitrogen atoms and the empty p-orbital of the carbene. This study investigated the affect these distortions have on the strength of the σ- and π-bonding interactions. This investigation demonstrated that while these distortions can affect the σ- and π-bonding interactions, the ADC ligand have to become highly distorted before any significant change in energy of either the σ- or π-bonding interactions occurs. Chapter 3 is a collaborative investigation between experimental and computational methods, DFT calculations were employed to support the experimental catalytic results and determine the role that steric effects have in controlling the regioselectivity of a long-standing electronically controlled gold(I)-catalyzed tandem 1,6-enyne cyclization/hydroarylation reaction with indole. This study demonstrated that by sterically hindering nucleophilic attack of indole at the favored position, nucleophilic attack would occur at a secondary position leading to the selective formation of the electronically unfavored product. Chapter 4 is a collaborative investigation between experimental and computational methods. DFT calculations were employed to investigate and rationalize the importance of secondary non-covalent interactions and their influence on the enantioselectivity of a gold(I)-catalyzed intramolecular hydroamination of allene reaction. Through computational investigation of the enantiodetermining step, and the non-covalent interactions present between 2′-aryl substituent and the rest of the catalyst, it was determined that the presence of CF3 group on the 3,5-position of the 2′-aryl ring is crucial to maintaining a more rigid chiral pocket leading to higher enantiomeric excesses in this dynamic system. This increased rigidity is believed to be attributable to the several weak non-covalent interactions that arise between the allene substrate or diisopropyl N-substituent and the fluorine atoms of the CF3 groups.