Browsing by Author "Ram Narayan Yadav"

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Loss Aware Federated Learning for Service Migration in Multimodal E-Health Services
(Institute of Electrical and Electronics Engineers Inc., 2024) Himanshu Singh; Ajay Pratap; Ram Narayan Yadav; Debasis Das
In an emergency healthcare situation, delay between injury and treatment is one of the most critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by processing real-time ambulance data while en route to hospital can cut back the delay in treatment of the patient. However, several research challenges arise in accessing real-time patient data from ambulance to hospital while moving along different Road Side Units (RSUs). Due to the severity of medical data, there is a need to minimize computational losses along with costs due to migration and ambulance perceived latency. Considering the above scenarios, this paper formulates an average cost minimization problem keeping latency, energy, and loss function into deliberation as NP-hard. To solve the formulated problem, Minimum Cost Algorithm (MCA) using Federated Averaging (FedAvg) algorithm utilizing RSUs for effectively transferring real-time patient data to hospitals has been proposed considering above stated constraints altogether. Moreover, to handle imbalances in health data across different hospitals during processing, FedAvg algorithm combines augmentation techniques. Through experimental and prototype demonstration, the efficacy of proposed framework is shown by achieving 12.5 \%, 27 \%,12.5%,27%, and 38 \%38% reduction in an average total cost compared to other state-of-the-art techniques on real-world data sets, respectively. © 2008-2012 IEEE.
Optimizing Resource Utilization and QoS-Conscious Application Deployment Through AHP in Edge Computing
(Institute of Electrical and Electronics Engineers Inc., 2024) Yasasvitha Koganti; Ram Narayan Yadav; Ajay Pratap
Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be in the proximity of IoT users. Thus, the required constraints related to tasks such as resource requirements, and the quality of service can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers while satisfying the Quality of Service (QoS) requirement of IoT users. In any matching algorithm, the main concern is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, and edge server resources used. The simulation results confirm that significant profit and satisfied users can be achieved by the proposed algorithm. © 2024 IEEE.
QoS-Aware Application Assignment and Resource Utilization Maximization Using AHP in Edge Computing
(Institute of Electrical and Electronics Engineers Inc., 2025) Yasasvitha Koganti; Vidhyuth Sridhar; Ram Narayan Yadav; Ajay Pratap
Edge computing (EC) has emerged as a promising technology to meet the demand for computational resources in Internet of Things (IoT) networks. With EC, the processing of massive data-intensive tasks can occur in proximity to IoT users. Thus, required constraints related to tasks, such as latency and Quality of Service (QoS) can be guaranteed. However, determining the task offloading strategy under various constraints, including resources, distance, and cost, remains an open issue. In this article, we study the task offloading problem from a matching perspective and propose an edge-user assignment algorithm (EUAA) that aims to maximize the resource utilization of edge servers and the number of assigned IoT users. A key concern in any matching algorithm is how to generate the preference order for either side. To generate preference orders for edge servers, we apply the analytical hierarchy process (AHP), considering criteria, such as distance from users to the server, latency, resource requirements, and pricing. This approach establishes the priority of users for matching to edge servers. From the IoT users’ perspective, we use cost and QoS parameters to enhance their satisfaction. We evaluate the performance of the proposed model based on the number of assigned users, server profit, number of satisfied users, edge server resource utilization, and execution time, comparing it with state-of-the-art schemes. © 2014 IEEE.