http://heritageit.dspaces.org/jspui/handle/123456789/1063 2026-07-10T03:35:48Z http://heritageit.dspaces.org/jspui/handle/123456789/10959 Title: A Blockchain-Based Distributed and Intelligent Clustering-Enabled Authentication Protocol for UAV Swarms Authors: Karmakar, Raja; Kaddoum, Georges; Akhrif, Ouassima Abstract: Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known as SwarmAuth, for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs’ authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance. 2024-05-05T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/10958 Title: A Novel Federated Learning-Based Smart Power and 3D Trajectory Control for Fairness Optimization in Secure UAV-Assisted MEC Services Authors: Karmakar, Raja; Kaddoum, Georges; Akhrif, Ouassima Abstract: Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism, FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using a deep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing a federated learning (FL) approach. Each UAV uses a reinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs’ offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems. 2024-01-01T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/7541 Title: A decision framework with nonlinear preferences and unknown weight information for cloud vendor selection Authors: Kar, Byabarttya Mahua Abstract: Cloud vendor selection (CVS) is a complex decision-making problem, which actively adheres to human behavior/cognition. The complex nature of the problem is due to personal biases/hesitation, trade-offs among attributes, uncertainty in rating, and the nonlinear relationship among cloud vendors and associated attributes. In recent times, researchers started paying more attention to user/expert behavior, which led to non-linear decision-making. Most of the extant decision models for CVS considered the linear form of decision-making, which is not realistic due to expert opinions' complexity and dynamism. Motivated by the claim, in this paper, a non-linear decision approach is put forward for CVS. Likert scale rating is adopted for rating cloud vendors based on some attributes, which are transformed to polynomial space from the linear fuzzy space. After this, weights of attributes are determined by using CRITIC in the non-linear space. Following this, cloud vendors are ranked in a personalized fashion using the proposed algorithm that encompasses the WASPAS procedure and rank fusion schemes. Finally, a case study is exemplified to validate the usefulness of the decision approach. Comparison and sensitivity analysis showcases the efficacy and robustness of the developed approach. 2023-03-01T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/7540 Title: Up‑Regulated Proteins Have More Protein–Protein Interactions than Down‑Regulated Proteins Authors: Dey, Lopamudra Abstract: Microarray technology has been successfully used in many biology studies to solve the protein–protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the trans-lation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body. 2022-10-01T00:00:00Z