2. Thesis and Dissertations

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Subject: search.filters.subject.Optimization

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    Improved Nature Inspired Algorithms For Optimization Problems In Wireless Sensor Networks
    (National Institute of Technology Karnataka, Surathkal, 2022) Kanchan, Pradeep; D, Pushparaj Shetty
    In a Wireless Sensor Network (WSN), the nodes are placed in random positions and con- nected to each other through networks. The nodes collect data from each other, perform pro- cessing and the results are sent to a Base Station (BS). In simple words, Optimization is selecting the best element, with respect to some criterion, from a given set of alternatives. Most of the research in the field of WSNs have concentrated on optimizing clustering, energy efficiency, network lifetime, coverage, load balancing, fault tolerance, quality of service, etc. Multi Objective Optimization deals with optimizing more than one objective at the same time. This thesis concentrates on developing nature inspired algorithms for energy efficient clus- tering and for improving network lifetime in conjunction with Quantum computing. Also, the aim is to develop an efficient nature inspired algorithm for optimizing target coverage in Ho- mogeneous as well as Heterogeneous WSN using Quantum Computing. For achieving the first 2 objectives (Optimizing Energy Efficiency and Improving Network Lifetime), the nature inspired algorithm, PSO (Particle Swarm Optimization) is used in con- junction with Quantum computing. For the 3rd objective (Optimizing Target Coverage), an- other nature inspired algorithm, MOEAD (Multi Objective Evolutionary Algorithm with De- composition) is used in conjunction with quantum computing.
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    Workload Optimization In Federated Cloud Environment
    (National Institute of Technology Karnataka, Surathkal, 2022) S R, Shishira; A., Kandasamy
    Cloud computing is an essential paradigm for processing, computing, storing, and com- munication bandwidth. It offers services on an on-demand basis for the user, that is, pay per use. Cloud computing consists of numerous resources, including the provision of networks, databases for storage, servers, virtual machines, and potential application. It is a widely used technique to handle large amounts of data as it provides versatility and functionality for optimization. Customers submit their request for data exchange and to store it in an existing cloud environment. The customer has a huge advantage in paying for the currently required services. In a federated cloud environment, one or more cloud service providers share their servers to handle user requests. It promotes cost savings, service utilization, and performance enhancement. Clients would bene- fit as a Service Quality Agreement exists between the two. The Cloud federation is an evolving technology through which cloud service providers cooperate to provide clients with customized services to enjoy the real benefits of Cloud Computing. The federated service provider achieve better resource usage and Quality of Service by cooperation, thereby enhancing their market prospects. Workloads are the collection of raw inputs provided to the processing arhcitecture. Based on the successful processing of workloads, efficiency can be assessed. Differ- ent workloads have distinct feature sets. The secret to making optimal configuration decisions and improving system performance is by recognizing the characteristics of workloads. Multiple requests are handled quickly under the dynamic cloud environ- ment, which contributes to the resource allocation problem.The cloud will maintain the workflow active through the proper allocation of resources, virtualization software, or repositories. However, the precise load estimation model is important for efficient management of resources. i It is hard to manage a large number of workloads in an enterprise cloud system. Workloads are the sum of data for processing that are provided to the hardware resource. Its behavior and characteristics play an important role in the efficient processing of resource requests. It is also difficult to predict the existence of workloads if they alter excessively. In this thesis, we propose a conceptual framework for efficient prediction and optimization of workloads that can be easily adapted to a system to address this problem to address this problem. Serving the request in considerably less time leads to an issue with resource allocation. In order to auto-scale the resources, it is more comfortable to have previous awareness of the incoming loads. For the better prediction of workloads in the cloud world, a novel architecture is proposed. Predicted workloads could also be configured smoothly for better use without waving off, the SLA negotiated between the provider and customers. Three essentials for the management of cloud resources are considered in the proposed Fitness Function Extraction Model, i.e. CPU, Disk, and Memory storage. This thesis proposes a BeeM-NN architecture by incorporating Workload Neural Network Algorithm and Novel Bee Mutation Optimization Algorithm into a cloud en- vironment for optimized workload prediction. The proposed model initially includes the Fitness Function Extraction Algorithm to retrieve the attribute samples from the Microsoft Azure traces. With the Novel Bee Mutation Optimization Algorithm in the cloud, the expected QoS are optimized. The developed model is tested using the feder- ated cloud service providers workload data traces and is analyzed with the benchmark methods. The result indicated that the proposed model obtained higher accuracy than the existing systems with optimum efficiency in resource and cost usage.
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    Characterization of Magneto-Rheological Fluid and Monotube Damper through Experimental and Computational Analysis
    (National Institute of Technology Karnataka, Surathkal, 2018) T. M, Gurubasavaraju; Kumar, Hemantha; M, Arun
    Magnetorheological fluid belongs to a class of smart materials which exhibit change in their rheological properties, when exposed to an external magnetic field and these properties are completely reversible. By utilizing these special characteristics, the damping force of the MR damper can be controlled and varied in real time applications. The main objective of this research work is to investigate the characteristics of MR fluid and MR damper through experimental as well as computational methods and to evaluate the semi-active suspension with MR dampers performance in terms of ride comfort and road holding of vehicles, when subjected to random road conditions. The rheological characterization of the MR fluid samples under different magnetic fields and fluid gap has been evaluated through experimentation. The measured fluid properties were used for computing the damping force of MR damper. Using single and multi-objective particle swarm optimization techniques, the optimal proportion of iron particles for MR damper application was determined to maximize the shear stress and damping force. The dynamic characterization of MR damper through experimental approach using dynamic test facility at 1.5 Hz and 2 Hz frequencies has been carried out. Also, the influence of material properties of MR damper components on the induced magnetic flux density and geometrical parameters on the damping force was investigated through finite element analysis as well as analytical methods. Multi-objective genetic algorithm and screening optimization techniques were employed to maximize the magnetic flux density and to identify the optimal values of the design variables. Using the analytical method, damping force of the damper was computed for the obtained optimal values of the design variables. It was observed that the damping force of the MR damper whose cylinder is made up of magnetic material was 2.79 times greater than that of MR damper whose cylinder is made up of non-magnetic material. Further, a coupled finite element analysis (FEA) and computational fluid dynamics (CFD) analysis was used for estimating the magnetic flux density and damping force for different input currents. The credibility of the shear mode monotube MR damperanalysis results were validated with experimental results. To overcome certain limitations of shear mode damper, an attempt has been made to realize the mixed mode damper by combining the flow and shear mode operations. The variations in the damping characteristics of flow and mixed mode MR damper under different input were compared with shear mode MR damper. Results showed that combination of two modes of operation could enhance the damping force to a significant level. The damping force of mixed mode MR damper was found to be 3 times greater than that of shear mode MR damper at 2 Hz frequency and 0.4 A current. Based on results obtained from computational analyses, a non-parametric representative model exhibiting the hysteretic behavior of MR damper was developed. The developed nonparametric model was implemented in a quarter car semi-active suspension to determine the dynamic response of the vehicle subjected to random road excitations. Further, this model was implemented in three-wheeler vehicle semi-active suspension system to evaluate its dynamic performance. The outcome showed that the vehicle with non-parametric based MR suspension system provided good vibration isolation for semi-active suspension than passive suspension system in terms of rice comfort and road holding.