A Deep Dive into Blockchain: A Systematic Literature Review on Transparency, Security, and Scalability Challenges

The systematic collection and analysis of data relevant to the field of education are referred to as educational research. Research writing is a laborious practice that methodically approaches the solution to a given research challenge. It differs from educational institutions and is typically led by a format. This study aims to analyze methodological explanation of variables, background to the study, literature review & research design as ways of promoting vigorous scholarship. This research uses the Systematic Literature Review (SLR) method. The results of this research explain many types of in-depth research methodology. These resources' main goal is to assist young academics and education professionals in learning about the background of the study, literature review, research design, and methodological explanation of variables. Educational research plays a crucial role in the development and improvement of educational practices and policies. It enables educators and policymakers to make informed decisions based on evidence and data. These offer beneficial fundamental knowledge for those who wish to pursue careers as researchers.

PurposeThe continuation of rapid changes in Web 2.0 has transformed the practice of business communication and stakeholders’ expectations. This paper aims to paramount one issue facing corporate communicators is stakeholders’ social media fatigue that leads to online disengagement and social media strategy ineffectiveness.Design/methodology/approachA systematic review of research literature and a deep dive in the professional reports regarding corporate communication and social media strategies are conducted.FindingsTo tackle the challenge of stakeholder social media fatigue, a conceptual model is provided to guide the development of alternative social media strategies that capitalize on the impact of vicarious interaction and reenergize stakeholders via trialogue based on the corporate-influencer-stakeholder (parasocial) relationships.Originality/valueA model for overcoming stakeholder social media fatigue via optimizing corporate-influencer-stakeholder (parasocial) relationship is proposed and elaborated, with actionable social media strategies recommended for corporate communicators to use.

Biomedical engineering is an interdisciplinary field that merges the principles of engineering and medical sciences to develop innovative solutions for healthcare challenges. Over the past decade, rapid technological advancements have transformed the way health conditions are diagnosed, monitored, and treated. This research provides a comprehensive review of recent developments in biomedical technologies, focusing on four primary areas: wearable medical devices, advanced medical imaging, robotic-assisted surgery, and tissue engineering. The study employs a thematic qualitative methodology based on a systematic review of peer-reviewed literature, analyzing key innovations, their clinical impacts, and integration challenges. The findings reveal that wearable devices have significantly improved chronic disease management through real-time physiological monitoring, while imaging technologies, enhanced by artificial intelligence, have elevated diagnostic precision and early disease detection. Robotic-assisted surgeries have led to reduced complications and faster recovery times, and tissue engineering shows promising potential in regenerative medicine despite ongoing challenges in scalability and biocompatibility. However, the study also identifies persistent issues such as device interoperability, cybersecurity risks, and regulatory constraints. This paper concludes that biomedical engineering will continue to play a critical role in shaping the future of healthcare. The integration of AI, nanotechnology, and personalized medicine is expected to further accelerate innovation. Addressing existing barriers through multidisciplinary collaboration will be essential in maximizing the benefits of biomedical technologies and ensuring equitable access to advanced healthcare solutions globally.

Purpose Ever since Mark Weiser coined the term “ubiquitous computing” (ubicomp) in 1988, there has been a general interest in proposing various solutions that would support his vision. However, attacks targeting devices and services of a ubicomp environment have demonstrated not only different privacy issues, but also a risk of endangering user’s life (e.g. by modifying medical sensor readings). Thus, the aim of this paper is to provide a comprehensive overview of security challenges of ubicomp environments and the corresponding countermeasures proposed over the past decade. Design/methodology/approach The results of this paper are based on a literature review method originally used in evidence-based medicine called systematic literature review (SLR), which identifies, filters, classifies and summarizes the findings. Findings Starting from the bibliometric results that clearly show an increasing interest in the topic of ubicomp security worldwide, the findings reveal specific types of attacks and vulnerabilities that have motivated the research over the past decade. This review describes most commonly proposed countermeasures – context-aware access control and authentication mechanisms, cryptographic protocols that account for device’s resource constraints, privacy-preserving mechanisms, and trust mechanisms for wireless ad hoc and sensor networks. Originality/value To the best of our knowledge, this is the first SLR on security challenges in ubicomp. The findings should serve as a reference to an extensive list of scientific contributions, as well as a guiding point for the researchers’ novel to the security research in ubicomp.

<p><strong>BACKGROUND:</strong> Healthcare data are very sensitive records that should not be made available to unauthorized people in order for protecting patient's information security. However, in progressed technologies as cloud computing which are vulnerable to cyber gaps that pose an adverse impact on the security and privacy of patients’ electronic health records and in these situations, security challenges of the wireless networks need to be carefully understood and considered. Recently, security concerns in cloud computing environment are a matter of challenge with rising importance.</p><p><strong>OBJECTIVE:</strong> In this study a systematic review to investigate the security challenges in cloud computing was carried out. We focused mainly on healthcare cloud computing security with an organized review of 210 full text articles published between 2000 and 2015.</p><p><strong>METHOD:</strong> A systematic literature review was conducted including PubMed, Science direct, Embase, ProQuest, Web of science, Cochrane, Emerald, and Scopus databases.</p><p><strong>FINDINGS:</strong> Using the strategies described, 666 references retrieved (for research question one 365, research question two 201, and research question three 100 references).</p><p><strong>IMPROVEMENTS:</strong> Review of articles showed that for ensuring healthcare data security, it is important to provide authentication, authorization and access control within cloud's virtualized network. Issues such as identity management and access control, Internet-based access, authentication and authorization and cybercriminals are major concerns in healthcare cloud computing. To manage these issues<strong> </strong>many involved events such as Hybrid Execution Model, VCC-SSF, sHype Hypervisor Security Architecture, Identity Management, and Resource Isolation approaches<em> </em>have to be defined for using cloud computing threat management processes.</p>

As the semiconductor industry approaches the physical limits of silicon-based technologies, the quest for alternative materials and innovative technologies has become paramount to continuing Moore's Law and addressing the growing demands for computing power and energy efficiency. This paper explores the frontier of semiconductor innovation, highlighting the emergence of novel materials and technologies poised to transcend the limitations of traditional silicon. The paper examines the properties and potential of materials such as graphene, transition metal di-chalcogenides (TMDCs), and topological insulators, alongside advancements in device architectures like quantum dots and spintronic. The methodology combines a systematic review of recent literature with comparative analysis of material properties, device performance, and scalability challenges. The integration of these emerging materials with current semiconductor manufacturing ecosystems is also discussed. Findings indicate that these materials offer significant advantages over silicon, including higher electron mobility, flexibility, transparency, and superior thermal properties, which could revolutionize the semiconductor industry by enabling faster, smaller, and more energy-efficient devices. These materials are showcasing their potential to enhance computational capabilities, sensor sensitivity, and connectivity for a wide range of applications, from wearable electronics to quantum computing. However, challenges related to synthesis, large-scale production, and integration into current systems persist. Addressing these hurdles requires interdisciplinary research and collaboration between academia, industry, and regulatory bodies to develop standardized methods for material synthesis, characterization, and device fabrication. The implications of findings extend beyond the semiconductor industry, offering insights into the future of electronics, telecommunications, and computing. As we stand on the brink of a new era in semiconductor technology, this paper calls for increased investment in research and development, policy support, and international cooperation to harness the full potential of these emerging materials and technologies, ensuring a sustainable and innovative future for the global semiconductor market.

This systematic review has demonstrated that Artificial Intelligence (AI) plays a transformative role in Smart Building Management Systems (SBMS), enhancing energy efficiency, predictive maintenance, and sustainable automation. By analyzing 472 high-quality studies, this research has identified that AI-driven HVAC optimization, lighting control, solar energy forecasting, and demand-side energy management significantly reduce energy consumption, with reported efficiency improvements ranging between 20-50%. The review also highlights that reinforcement learning (RL) and deep learning (DL) models outperform traditional rule-based systems by dynamically adjusting building operations based on real-time sensor data, occupancy patterns, and environmental conditions. AI-powered fault detection and predictive maintenance further improve building operations by reducing unexpected system failures, lowering maintenance costs by up to 35%, and extending equipment lifespan. Moreover, the study underscores the growing potential of hybrid AI models integrating IoT, blockchain, and cloud computing in enabling real-time energy monitoring, decentralized energy trading, and secure automation. Despite these advancements, the review also reveals critical research gaps, particularly the lack of large-scale empirical validation, challenges in AI scalability, and the need for interdisciplinary collaboration to enhance AI’s effectiveness in sustainable building design. While theoretical and simulation-based studies provide strong evidence of AI’s benefits, real-world pilot projects, regulatory frameworks, and cross-sector collaborations are essential for AI-driven smart building technologies to achieve widespread adoption. Addressing these challenges through industry-academia partnerships, policy support, and further longitudinal research will be key to ensuring that AI-powered SBMS can drive long-term sustainability, operational efficiency, and energy resilience in modern smart infrastructure.

Swarm Intelligence inspired Intrusion Detection Systems — A systematic literature review

The rapid expansion of Edge Computing (EC) and Internet of Things devices has introduced significant cybersecurity challenges, necessitating advanced and privacy-preserving attack detection strategies. Traditional cyber-attack detection methods and centralized machine learning solutions face critical limitations in addressing privacy concerns, resource constraints, and the evolving nature of cyber threats in edge environments. Federated Learning (FL) offers a transformative solution by enabling distributed model training across edge devices while preserving data privacy. This systematic literature review investigates FL for cyber-attack detection in EC environments using the PRISMA methodology, analyzing 131 primary studies from 2020–2025 across five major databases. Our contributions include: (1) a comprehensive PRISMA-compliant framework encompassing seven thematic areas with detailed comparative analysis, (2) an in-depth gap analysis with actionable recommendations for privacy-performance trade-offs, scalability, and standardization challenges, and (3) a forward-looking research agenda addressing generative models, collaborative defense, 6G-enabled intelligence, and zero-trust architectures. Unlike existing surveys, this work provides the most comprehensive scope with bibliometric analysis, multi-perspective evaluation, and practical deployment guidelines, serving as a foundational reference for advancing federated cyber-attack detection in edge computing environments.

Industry 4.0 represents the fourth industrial revolution that will leverage the industrial Internet of Things (IIoT) to introduce adaptive and autonomous systems that can self-heal and self-learn. IIoT aims to promote businesses and industries by realizing intelligent industrialization. However, the constantly surging data volumes that are generated by IIoT environments present security issues like data integrity and system scalability. Blockchain is a promising candidate to address these problems, which offers distributed system design principles. Though blockchain-based IIoT frameworks may have the potential to support the demands and services of next-generation industrial systems, their integration is still constrained by significant challenges in scalability and security. Therefore, blockchain in its original structure with traditional proof-of-work consensus is not suitable for IIoT. To address this, we propose a blockchain architecture that uses a dynamic proof-of-work consensus with a block checkpoint mechanism. The dynamic consensus functions with different mining difficulty levels allow the architecture to efficiently scale with increase in communication traffic of IIoT devices, whereas the checkpoint defines an alternative mechanism to generate the next block hash in the blockchain. To study the scalability and feasibility of the architecture, thorough performance and security analyses are presented, which attest that it can scale and offer enhanced security fidelity with a minimal increase in block mining time.

Today's Internet suffers from ever-increasing challenges in scalability, mobility, and security, which calls for deep innovations on network protocols and infrastructures. However, the distributed controlling mechanism, especially the bundle of control plane and the data plane within network devices, sharply restricts such evolutions. In response, the software-defined networking (SDN), an emerging networking paradigm, proposes to decouple the control and data planes, producing logically centralized controllers, simple yet efficient forwarding devices, and potential abilities in functionalities programming. This chapter presents a short yet comprehensive overview of SDN components and the OpenFlow protocol on basis of both classic and latest literatures. The topics range from fundamental building blocks, layered architectures, novel controlling mechanisms, and design principles and efforts of OpenFlow switches.

The Internet of Things (IoT) has emerged as one of the most transformative paradigms in modern computing, enabling billions of connected devices to sense, process, and transmit data. This growth poses challenges in scalability, security, and real-time data management. Cloud platforms, particularly Amazon Web Services (AWS), have become central enablers of large-scale IoT deployments due to their comprehensive service ecosystem. This paper presents a review of AWS IoT, focusing on its architecture, applications, and open research directions. We analyze core components such as AWS IoT Core, Lambda, Greengrass, and data services, highlighting their roles in enabling cloud-edge integration and secure device management. A survey of academic literature and industrial case studies demonstrates AWS IoT's practical adoption across healthcare, agriculture, and smart city domains. Comparative analysis with Microsoft Azure IoT and Google Cloud IoT positions AWS as a scalable and secure platform, while identifying limitations in latency, cost, and provisioning complexity. Finally, we outline future research opportunities in federated learning, energy-efficient edge inference, protocol interoperability, and blockchain-based provisioning. This review provides a consolidated reference for researchers and practitioners aiming to leverage AWS IoT for next-generation intelligent systems.

The rapid evolution of cyber threats necessitates innovative approaches to enhance global cybersecurity collaboration. Federated Learning (FL) has emerged as a decentralized machine learning paradigm that enables distributed threat intelligence sharing while maintaining data privacy and security. This chapter explores the application of FL for large-scale cybersecurity networks, addressing critical challenges in scalability, security, and communication efficiency. The focus is on optimizing secure aggregation techniques to enable efficient and privacy-preserving model updates across heterogeneous and resource-constrained environments. Key solutions such as hierarchical aggregation, sparse model updates, and blockchain-based enhancements are discussed to mitigate the computational and communication overheads inherent in federated systems. the chapter investigates the integration of advanced cryptographic methods, including homomorphic encryption and differential privacy, to strengthen the security of federated networks against adversarial attacks. By leveraging FL’s potential, organizations can share threat intelligence across global networks without compromising sensitive data, significantly improving real-time cyber threat detection and response. The chapter concludes by identifying future research directions for overcoming existing challenges and further optimizing federated models in cybersecurity.

The integration of artificial intelligence (AI) with wearable bioelectronics is revolutionizing digital healthcare by enabling proactive, personalized, and data-driven medical solutions. These advanced devices, equipped with multimodal sensors and AI-powered analytics, facilitate real-time monitoring of physiological and biochemical parameters-such as cardiac activity, glucose levels, and biomarkers-allowing for early disease detection, chronic condition management, and precision therapeutics. By shifting healthcare from reactive to preventive paradigms, AI-driven wearables address critical challenges, including rising chronic disease burdens, aging populations, and healthcare accessibility gaps. However, their widespread adoption faces technical, ethical, and regulatory hurdles, such as data interoperability, privacy concerns, algorithmic bias, and the need for robust clinical validation. This review comprehensively examines the current state of AI-enhanced wearable bioelectronics, covering (1) foundational technologies in sensor design, AI algorithms, and energy-efficient hardware; (2) applications in continuous health monitoring, diagnostics, and personalized interventions; (3) key challenges in scalability, security, and regulatory compliance; and (4) future directions involving 5G, the IoT, and global standardization efforts. We highlight how these technologies could democratize healthcare through remote patient monitoring and resource optimization while emphasizing the imperative of interdisciplinary collaboration to ensure equitable, secure, and clinically impactful deployment. By synthesizing advancements and critical gaps, this review aims to guide researchers, clinicians, and policymakers toward responsible innovation in the next generation of digital healthcare.

As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes—including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers—based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5–24.3%, achieves a task success rate of 84.2–88.5%, improves average resource utilization to 88.9–92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs.