Abstract This paper proposes SHIODEG, a hybrid metaheuristic that integrates the success-history intelligent optimizer (SHIO) with differential evolution (DE) and a Gaussian transformation (GT) to tackle two persistent challenges in optimization for engineering design: (i) the absence of a universally best optimizer across problem classes (as implied by the No-Free-Lunch perspective) and (ii) the limited ability of purely gradient-based methods to produce substantial improvements in complex, constrained, and often non-smooth real-world problems, motivating hybrid strategies that balance exploration and exploitation. SHIODEG follows a staged search process in which DE generates diverse trial solutions, GT injects normally distributed perturbations to reduce premature convergence and diversity collapse, and SHIO refines promising regions using success-history guidance from the best three leaders. SHIODEG is evaluated on the IEEE CEC2022 benchmark suite (12 functions) using 30 independent runs, a population size of 100, and a budget of 1000D function evaluations. The results show that SHIODEG consistently delivers top-tier performance across the benchmark suite, showing strong competitiveness, low variability, and statistically significant improvements over a wide range of alternative optimizers. It also demonstrates robust effectiveness on multiple constrained engineering design problems, achieving high-quality solutions across diverse real-world constraints.

Federated Learning (FL) enables decentralized learning while preserving data privacy. However, ensuring security and optimizing resource utilization in FL remains challenging, particularly in untrusted environments. To address this, we propose SecureFedPROM, a novel zero-trust FL framework that integrates Attribute-Based Access Control (ABAC) for secure client authorization and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) for dynamic, multi-criteria client selection. Unlike traditional FL client selection methods that prioritize security or efficiency, SecureFedPROM optimizes trustworthiness, computational efficiency, and performance, ensuring robust participation in each training round. We evaluate SecureFedPROM across multiple real-world datasets, demonstrating its superiority over state-of-the-art client selection protocols. Our results show that SecureFedPROM achieves a 7.19% improvement in model accuracy, accelerates convergence, and reduces the number of training rounds. Additionally, it minimizes wall-clock time and computational overhead, making it highly scalable for edge AI environments. These findings highlight the importance of integrating zero-trust security principles with multi-criteria decision-making to enhance security and efficiency in FL.

The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatics data. However, this complicated process has become the system bottleneck, requiring an amal-gamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., Base Recalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and proces-sor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper's findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.

Leveraging Big Data (BD) processing frameworks to process large-scale Resource Description Framework (RDF) datasets holds a great interest in optimizing query performance. Modern BD services are complicated data systems, where tuning the configurations notably affects the performance. Benchmarking different frameworks and configurations provides the community with best practices towards selecting the most suitable configurations. However, most of these benchmarking efforts are classified as descriptive or diagnostic analytics. Moreover, there is no standardization for comparing and contrasting these benchmarks based on quantitative ranking techniques. This paper aims to fill this timely research gap by proposing ranking criteria (called Bench-ranking) that provide prescriptive analytics via ranking functions. In particular, Bench-ranking starts by describing the current state-of-the-art single-dimensional ranking limitations. Next, we discuss the recent benchmarking requirements for sophisticated approaches over multi-dimensional ranking. Finally, we discuss the ranking criteria goodness by reviewing its conformance and coherence metrics. We validate Bench-ranking by conducting an empirical study using large RDF datasets under a relational BD engine, i.e., Apache Spark-SQL. The proposed ranking techniques provide the practitioners with clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.

Smart cities' vision will encompass connected industrial vehicles, which will offer data-driven and intelligent services to the user. Such interaction within dispersed connected objects are sometimes referred as the industrial Internet-of-Vehicles (IIoV). The prime motivation of an intelligent transportation system (ITS) is ensuring the safety of the drivers and offering a comfortable experience to the user. However, such complex infrastructures opens broad attack surfaces to the adversaries, which can remotely exploit and control the critical mechanics in the smart vehicles, including engine and brake systems. Security and privacy concerns are significant barriers to the wide adoption of this revolutionary technology that has to be addressed before a comprehensive implementation of the real vision of ITS. This article is a stepping stone to address access control issues in the IIoV ecosystem and propose a formal attribute-based access control system (referred to ITS-ABACG). The proposed model introduces the notion of groups, which are assigned to various smart entities based on the different attributes. It also offers the implementation of fine-grained security policies and considers individualized privacy preferences along with system-wide policies to accept or reject notification, alerts, and advertisements from different participating smart entities. We present the prototype implementation of our proposed model in the Amazon Web Services IoT platform together with extensive performance to reflect the practicality and wide-scale adoption of the proposed system.

The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCs.

This paper intends to propose a trustworthy model for authenticating users and services over a Big Data Federation deployment architecture. The main goal of this model is to provide a Single-Sign-on (SSO) approach for the latest Hadoop 3.x platform. To achieve this, a conceptual model is proposed combining Hadoop access control primitives and the Apache Knox framework. The paper provides various insights regarding the latest ongoing developments and open challenges in this domain.