http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/197 Mon, 06 Apr 2026 17:47:52 GMT 2026-04-06T17:47:52Z http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4663 Analysis and Synthesis of Bangla Phonemes for Computer Speech Recognition Hossain, Syed Akhter This thesis is submitted for the degree of Doctor of Philosophy. Tue, 27 May 2025 00:00:00 GMT http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4663 2025-05-27T00:00:00Z http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4662 Dynamic Traffic Engineering for high-Throughput Data Delivery III Wireless Mesh Networks Islam, Maheen This thesis is submitted for the degree of Doctor of Philosophy. Tue, 27 May 2025 00:00:00 GMT http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4662 2025-05-27T00:00:00Z http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4073 Quality of Service Aware Data Delivery Protocol in Narrow Band Internet of Things Enabled Healthcare Systems Sultana, Nahar The need for establishing smart hospitals is becoming increasingly evident due to a number of reasons driven by modern healthcare and technological breakthroughs. Internet of Things (IoT), Medical Sensors, Low Power Wide Area Network Technol ogy (LPWAN), Artificial intelligence (AI), Digital technologies, and Reliable Data Transmission Techniques are used by smart hospitals to improve patient care, opti mize resource management and streamline hospital operations. In order to handle the increasing complexity of healthcare delivery, smart hospitals are essential for various purposes. They improve automation in patient demand, increase oper ational effectiveness, lower the costs, and increase accessibility to healthcare by leveraging advanced technologies. In this dissertation, a suitable licensed LPWAN technology, namely Narrow band Internet of Things (NB-IoT) is chosen as a promising technology for health care applications since it reduces end to end latency. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing a data transmission framework that offers differentiated Quality of Services (QoS) to the critical and non-critical data packets is challenging. The existing literature studies suffer from insufficient access scheduling considering heterogeneous data packets and relationship among them in healthcare applications. The first contribution of i Abstract ii this thesis is to develop an optimal resource allocation framework for NB-IoT that maximizes a user’s utility through event prioritization, rate enhancement, and in terference mitigation. The proposed Priority Aware Utility Maximization (PAUM) system ensures weighted fair access to resources. In second contribution, the utilization of Device-to-Device (D2D) communi cation among Narrowband Internet of Things (NB-IoT) devices offers significant potential for advancing intelligent healthcare systems by extending its superior data rates, low power consumption. In D2D communication, strategies to miti gate interference and ensure coexistence with cellular networks are crucial. These strategies are aimed at enhancing user data rates by optimally allocating spectrum and managing the transmission power of D2D devices, presenting a complex engi neering challenge. Existing studies are limited either by the inadequate integration of NB-IoT D2D communication methods for healthcare, lacking intelligent, dis tributed, and autonomous decision-making for reliable data transmission, or by in sufficient healthcare event management policies during resource allocation in smart healthcare systems. In this work, we introduce an Intelligent Resource Allocation for Smart Healthcare (iRASH) system, designed to optimize D2D communication within NB-IoT environments. The iRASH innovatively integrates the Density based Spatial Clustering of Applications with Noise (DBSCAN) and Ant Colony Optimization (ACO) algorithms to effectively address the unique requirements of healthcare applications. The proposed system utilizes Belief-Desire-Intention (BDI) agents for dynamic and intelligent clustering of D2D devices, facilitating autonomous decision-making and efficient resource allocation. This approach not only enhances data transmission rates but also reduces power consumption, and is formulated as a Multi-objective Integer Linear Programming (MILP) problem. Abstract iii Given the NP-hard nature of this problem, iRASH incorporates a polynomial-time meta-heuristic-based ACO algorithm, which provides a suboptimal solution. This algorithm adheres to the principles of distributed D2D communication, promoting equitable resource distribution and substantial improvements in utility, energy effi ciency, and scalability. Finally, its performances are validated through simulations on the Network Simulator version 3 (NS-3) platform, demonstrating significant ad vancements over state-of-the-art solutions in terms of utility, delay, fair resource distribution, data rate, power efficiency,and system adaptability. As high as im provements of 65% in utility, 45% in fair sharing of resources, 25% in delay, 15% in packet delivery ratio observed by PAUM system and 35% in utility cost and 50% in energy cost are demonstrated by the iRASH system compared to the benchmark, proving their effectiveness This thesis is submitted for the degree of Doctor of Philosophy. Thu, 10 Apr 2025 00:00:00 GMT http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4073 2025-04-10T00:00:00Z http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4072 AnEfficient Worker Selection and Task Allocation Scheme for Mobile Crowdsourcing System Huq, Farhana The rapid proliferation of mobile computing devices has facilitated mobile crowd sourcing systems (MCS) to emerge as a powerful model for allocating tasks to a distributed workforce. The efficiency of such systems is significantly dependent on two critical factors: the selection of appropriate workers and the distribution of tasks to workers. Addressing these challenges is crucial for optimizing task accom plishment, minimizing task completion time, and improving satisfaction for both workers and task requesters. Online Food delivery (OFD), a specialized applica tion of mobile crowdsourcing, represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. Assigning food delivery orders to workers in a way that optimizes service quality by maximizing workers’ profit while minimizing order completion time to simultaneously enhance customer satisfaction is a challenging problem. The growing complexity of order fulfillment features and rising expecta tions for service quality have made the task of efficiently assigning riders for long distance, cross regional deliveries a major engineering challenge. Existing studies in the literature are limited as they tend to focus solely on either reducing order completion time or minimizing order delivery costs, failing to meet both objectives thoroughly. Prior research frequently depended on conventional order allocation i Abstract ii methods that either failed to notice varying capacities, or utilized non-intelligent systems that inadequately addressed fluctuating order demands and service delays. In this dissertation, we aim to develop a framework for optimal assignment of food delivery orders for both regional and cross regional online food delivery sys tem. At first, we propose a framework for the optimal assignment of food delivery orders to workers, formulated as a multi-objective linear programming (MOLP) problem, which balances the trade-off between maximizing worker profit and en hancing customer satisfaction, providing a comprehensive solution that addresses both objectives simultaneously. A Water Wave Optimization based metaheuristic assignment algorithm is developed for the online Food Delivery system that bal ances worker’s profit and customer satisfaction by selecting appropriate workers to complete the orders. The experiment results show the assignment significantly improves the performance of the OFD system in terms of average worker profit, customer satisfaction, average service time. The second contribution of this thesis is the development of the system compo nents and functional architecture of a cross regional online food delivery (XROFD) system, designed to facilitate real-time deliveries across regions efficiently. A Mixed Integer Linear Programming (MILP) optimization framework has been designed to minimize the total service time and delivery cost for cross regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. In the XROFD system, food orders are primarily transported by transfer vehicles, such as pickup vans, from restaurants to meeting point locations. To enhance the predictive accuracy of the XROFD sys tem, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands Abstract iii accurately, reflecting the dynamic nature of the marketplace. Additionally, Ex treme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and re source allocation within the MILP framework. These machine-learning techniques significantly bolster the MILP framework by providing detailed, accurate predic tions that improve decision-making processes and adaptability to real-time condi tions. Proven that the above MILP is an NP-hard problem, we further enhance our approach by integrating a metaheuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehi cles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders This thesis is submitted for the degree of Doctor of Philosophy. Thu, 10 Apr 2025 00:00:00 GMT http://reposit.library.du.ac.bd:8080/xmlui/xmlui/handle/123456789/4072 2025-04-10T00:00:00Z