The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security concerns. Lightweight cryptography (LWC) has evolved to be a promising solution to improve the privacy and confidentiality aspect of IoT devices. The challenge is to choose the right algorithm from a plethora of choices. This work aims to compare three different LWC algorithms: AES-128, SPECK, and ASCON. The comparison is made by measuring various criteria such as execution time, memory utilization, latency, throughput, and security robustness of the algorithms in IoT boards with constrained computational capabilities and power. These metrics are crucial to determine the suitability and help in making informed decisions on choosing the right cryptographic algorithms to strike a balance between security and performance. Through the evaluation it is observed that SPECK exhibits better performance in resource-constrained IoT devices.

In the dynamic landscape of healthcare, Digital Twin (DT) technology has emerged as a transformative force, holding the promise of revolutionizing patient care and industry practices. This article surveys the literature over the period 2020 to 2023 on a comprehensive exploration of DT in healthcare, elucidating its roles, benefits, and implications for smart personalized healthcare. The study addresses fundamental questions concerning the transformative potential of DT, investigating its varied roles and benefits in healthcare, its revolutionary impact on the industry, and the essential requirements for crafting a DT system tailored to the demands of smart personalized healthcare. The research further unveils the key layers necessary for implementing a DT smart healthcare system, examining potential applications that extend from diagnostics to treatment strategies. Methodologically, the paper navigates through different model discussions, providing a structured approach to understanding the implementation of DT in healthcare. Despite the transformative potential, the research delves into the limitations and challenges faced by DT technology, offering a balanced perspective on its current state. In conclusion, the paper synthesizes key findings, outlines methodologies, discusses challenges, and sets the stage for future research, presenting a holistic overview of the potential, pitfalls, and pathways for integrating DT in the healthcare industry.

Supply chain as an industry has gone through four-fold changes in the last century. Born as a bare-bones structure in 1.0 it grew to incorporate some form of record preservation in 2.0 and then integrated communication between two entities in 3.0. Supply chain 4.0, the current one, has total global integration of multiple entities with the records digitised. But increasing entities and pipelines, means increasing complexities, overhead and soft spots. In this paper, a systematic literature review is done with the objective of analysing existing Supply Chain 4.0. The focus of the paper is the usage of blockchain technology in the electronic industry to provide a decentralised architecture. Several papers were compared on the basis of different schemas like the type of blockchain network used, platform deployed on, security of frameworks, representation of unique identity, testing authenticity, working implementation, cost of implementation, etc. The pros and cons of various privacy and security methodologies are also explored and discussed. The paper also discusses the open issues and challenges in the same area of interest. Finally, the paper outlines the future scope to be delved into as a part of the future research.

Real-time systems are becoming pervasive with the growing global connectivity and rising consumer demands. The need for real-time processing has become a crucial part of many business applications worldwide. A key factor that determines the time taken for an application to give out the result hinges on its ability to prioritize, manage, and execute real-time workloads. However, there are several difficulties and constraints connected with implementing tasks in a real-time context. This research study primarily focuses on load balancing for mixed real-time tasks on a multi-core system, one of the major challenges for executing real-time workloads. The purpose of load balancing is to distribute the load evenly among the processor(s) and maximize their utility while minimizing overall execution time. The goal of this paper is to present a critical analysis of existing load balancing techniques for both periodic and aperiodic tasks. The paper explores several factors including throughput, performance, migration time, response time, overhead, resource utilization, scalability, fault tolerance, power efficiency, and other variables that play a crucial role in assessing the efficacy of load balancing in real-time systems. The proposed has contributed in four folds. Firstly, the state-of-the-art of various load balancing algorithms are discussed followed by the architecture involved in real-time multi-core systems. Later, different load balancing based scheduling algorithms were compared on the basis of different schemas and metrics for algorithm evaluation is also provided. Finally, the paper also identifies areas that warrant further exploration or investigation, suggesting potential avenues for future research, and highlighting emerging trends or developments that may shape the field.

Amidst the rising tide of cyber threats, the software industry navigates towards a pivotal solution: the widespread adoption of DevSecOps. In response to the evolving application development needs, DevSecOps enables developers and enterprises to seamlessly integrate security and, fortify digital realms against modern cybersecurity challenges. This study attempts to identify and review various threat models that can be combined with components of DevSecOps, such as its principles, methodology, tools, and workflows. It explores the integration of security and monitoring practices throughout the development pipeline. Underscoring the necessity of early security considerations in the software development life cycle, the discussion then delves into the details of adaptable strategies and best practices to mitigate potential threats proactively. Additionally, it emphasizes the pivotal role of monitoring and incident response in the prompt detection and mitigation of potential risks. Moreover, this study focuses on identifying the challenges associated with adopting traditional threat models in the contemporary landscape of multi-cloud architecture, a prevalent paradigm in current technological landscapes. Recognizing the intricacies of securing applications across diverse cloud environments, this scholarly work proposes a layered framework for multi-cloud which could be adopted by the implementers to address the security challenges of multi-cloud environment. Hence, this investigation furnishes valuable insights into the requirements, workflow and threat models of DevSecOps in single-cloud and emerging multi-cloud environment.

Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL’s potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.