Internet of Things and Edge Computing Journal
type: channel
description: Immerse yourself in the ever-expanding landscape of Internet of Things (IoT) and Edge Computing through our comprehensive journal. Delve into the latest advancements, emerging trends, and transformative applications that are revolutionizing how we connect and interact with the digital world. Our diverse range of articles, research papers, and case studies offer valuable insights and actionable knowledge for academics, industry professionals, and enthusiasts alike. Join us in exploring the forefront of innovation and unlocking the limitless potential of interconnected technologies. Journal Snapshot Journal Name: Internet of Things and Edge Computing Journal (IoTECJ) Editor-in-Chief: Divya D (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited h-index: 15 (By Google Scholar) i10-index: 22 (By Google Scholar) Journal Initials: IoTECJ Research Scope: Internet of Things (IoT), Edge Computing, Cloud and Fog Computing, Wireless Sensor Networks, Industrial Internet of Things (IIoT), Smart Cities and Intelligent Infrastructure, Internet of Medical Things (IoMT), Artificial Intelligence and Machine Learning for IoT, Cybersecurity and Privacy in IoT Systems, Embedded and Real-Time Computing Publication Mode: Digital (On this Website) Frequency: Semi-Annual (2 Issues a year) Launch Year: 2021 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: Worldwide Language: English Editorial Contact - editor@thesciencebrigade.com Scope: Our journal serves as a leading platform for the dissemination of cutting-edge research, insights, and advancements in the fields of Internet of Things (IoT) and Edge Computing. We aim to foster interdisciplinary collaboration and facilitate knowledge exchange among researchers, practitioners, and enthusiasts worldwide. Mission: At the forefront of innovation, our mission is to accelerate the pace of development and adoption of IoT and Edge Computing techno…
publisher: The Science Brigade Publishers
language: en
publication name: Internet of Things and Edge Computing Journal
issn: 0000-0000
copyright: License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
items: Dynamic Exposure Monitoring and Catastrophe Loss Estimation: Real-Time AI Frameworks for Integrated Insurance Risk Management
items: Differential Privacy Enforcement and Anomalous Access Detection: Real-Time AI Systems for Patient Health Data Privacy Management
items: Sequential Pattern Recognition and Entity Linkage in Payment Ecosystems: AI-Driven Computational Frameworks for Financial Transaction Behavioural Analysis
items: High-Frequency Market Microstructure Analysis and Liquidity Intelligence: AI-Based Frameworks for Real-Time Financial Market Analysis
items: In-Process Analytical Intelligence and Deviation Alert Systems: Real-Time AI-Powered Platforms for Pharmaceutical Manufacturing Quality Monitoring
items: Macroeconomic Signal Integration and Ensemble Forecasting Architectures: Machine Learning Approaches to Enhanced Financial Forecast Accuracy
items: Multi-Criteria Compound Prioritisation and Go/No-Go Signal Modelling: Machine Learning Frameworks for Enhanced Decision-Making in Drug Development
items: Multivariate Time-Series Forecasting and Biomarker Trajectory Modelling: Advanced Predictive Analytics Frameworks for Pharmaceutical Research and Development
items: Network Behavioural Analysis and Claim Pattern Recognition: AI-Based Computational Strategies for Insurance Fraud Prevention
items: Physiological Deterioration Scoring and Mortality Risk Stratification: Machine Learning Models for Early Outcome Prediction in Critical Care Settings
items: Policyholder Interaction Mining and Churn Propensity Modelling: AI-Enhanced Analytical Frameworks for Insurance Customer Insight Generation
items: Preference-Sensitive Decision Support and Care Pathway Personalisation: AI-Driven Tools for Advancing Patient-Centred Clinical Care Models
items: Regulatory Submission Intelligence and Benefit-Risk Signal Extraction: AI-Driven Frameworks for Optimising Drug Approval Pathway Navigation
items: Continuous Bioactivity Screening and Lead Optimisation in Accelerated Timelines: The Role of Real-Time AI Platforms in Modern Drug Discovery
items: Customer Intent Modelling and Contextual Product Recommendation: Deep Learning Approaches to Personalised Banking Service Delivery
items: Behavioural Cohort Analysis and Lifetime Value Stratification: AI-Driven Customer Segmentation Frameworks for Insurance Portfolio Management
items: Capacity Utilisation Modelling and Patient Flow Intelligence: AI-Based Frameworks for Healthcare Resource Allocation and Management Optimisation
items: Clinical Entity Recognition and Longitudinal Patient Summary Generation: AI-Driven Natural Language Processing Systems for Automated Medical Record Analysis
items: Clinical Feature Engineering and Outcome Prediction in Electronic Health Records: Machine Learning Approaches to Enhanced Predictive Analytics in Healthcare
items: How Cleanroom Consumables Act as Critical Infrastructure (Not an Afterthought)
items: IoT-Enhanced Energy Management Strategies for Sustainable Smart Manufacturing Practices
items: Industry Standard IoT Architectures for Secure Data Exchange in Smart Manufacturing Ecosystems
items: IoT-Driven Digital Twin Models for factories: Simulation and Real-Time tracking to Optimize Industrial Operations
items: Integrating IoT and Manufacturing process for Real-Time Predictive Maintenance in High-Throughput Production Environments
items: Advanced Machine Learning Techniques for Anomaly Detection in Edge Computing Security: A Framework for Real-Time Threat Mitigation
items: The Role of Leadership in Digital Transformation of Large Enterprises
items: The Impact of AI on Organizational Change in Digital Transformation
items: Implementing TOGAF for Large-Scale Healthcare Systems Integration
items: Evaluating FHIR's impact on Health Data Interoperability
items: Real-Time AI-Enhanced Driver Monitoring Systems

Dynamic Exposure Monitoring and Catastrophe Loss Estimation: Real-Time AI Frameworks for Integrated Insurance Risk Management
type: item
description: Insurance risk management is significant for the industry to sustain profitability in the long run. Traditional assessment of risk is not effective due to changes in rules and regulations, changing scenarios, delayed outputs, and underwriting evidence. Technology is redefining traditional processes and strategies.
subject: Articles
  topic: section
subject: dynamic exposure monitoring
  topic: keywords
subject: catastrophe loss estimation
  topic: keywords
subject: real-time ai frameworks
  topic: keywords
subject: integrated insurance risk management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Nigel Atkinson
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 20
ending page: 28

Differential Privacy Enforcement and Anomalous Access Detection: Real-Time AI Systems for Patient Health Data Privacy Management
type: item
description: Patient data privacy is an important issue within the medical field, as it deals with the highly sensitive topic of individual health. Making these records available to people without proper authorization can result in serious detrimental effects for the patient, including stigmatization, discrimination in employment and healthcare provision, as well as social isolation.
subject: Articles
  topic: section
subject: differential privacy enforcement
  topic: keywords
subject: anomalous access detection
  topic: keywords
subject: real-time ai systems
  topic: keywords
subject: patient health data privacy management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Carlos Hernández
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 11
ending page: 19

Sequential Pattern Recognition and Entity Linkage in Payment Ecosystems: AI-Driven Computational Frameworks for Financial Transaction Behavioural Analysis
type: item
description: The banking system has been overwhelmed by data since its origins. Historical transaction records cover millions of items from varied categories and dimensions. From deposits to loans, clients’ transactions have to run through a myriad of controls aiming to ensure compliance with regulations and risk assessment purposes.
subject: Articles
  topic: section
subject: sequential pattern recognition
  topic: keywords
subject: entity linkage
  topic: keywords
subject: payment ecosystems
  topic: keywords
subject: computational frameworks
  topic: keywords
subject: financial transaction behavioural analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Agata Grabowska
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 31
ending page: 40

High-Frequency Market Microstructure Analysis and Liquidity Intelligence: AI-Based Frameworks for Real-Time Financial Market Analysis
type: item
description: AI-based solutions are being researched for an increasing range of human activities. In finance, we benefit from the latest progress in technology, which improves our ability to analyze markets and opens new competitive advantages. At the center of this work is the trade-off between the speed and analytical quality of market research.
subject: Articles
  topic: section
subject: high-frequency market microstructure analysis
  topic: keywords
subject: liquidity intelligence
  topic: keywords
subject: frameworks
  topic: keywords
subject: real-time financial market analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Feng Li
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 29
ending page: 39

In-Process Analytical Intelligence and Deviation Alert Systems: Real-Time AI-Powered Platforms for Pharmaceutical Manufacturing Quality Monitoring
type: item
description: The impact of drug quality on the safety and effectiveness of pharmaceuticals is universally recognized. This creates significant pressure to implement more effective and efficient quality control methodologies within the pharmaceutical industry. Manufacturing processes used to produce pharmaceuticals are becoming increasingly complex and pose many challenges when it comes to assuring product quality. Such complexity demands a new approach to process monitoring, one that can work in real-time.
subject: Articles
  topic: section
subject: in-process analytical intelligence
  topic: keywords
subject: deviation alert systems
  topic: keywords
subject: real-time ai-powered platforms
  topic: keywords
subject: pharmaceutical manufacturing quality monitoring
  topic: keywords
subject: machine learning
  topic: keywords
creator: Jorge Murillo
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 40
ending page: 48

Macroeconomic Signal Integration and Ensemble Forecasting Architectures: Machine Learning Approaches to Enhanced Financial Forecast Accuracy
type: item
description: The complexity of modern financial markets requires precise predictions guided by an accurate consensus of their most likely direction. In the throes of constantly changing macroeconomic environments, the value of recent past financial performance is being constantly outstripped by forward investment expectations.
subject: Articles
  topic: section
subject: macroeconomic signal integration
  topic: keywords
subject: ensemble forecasting architectures
  topic: keywords
subject: machine learning approaches to enhanced financial forecast accuracy
  topic: keywords
creator: Jérémy Fix
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 8

Multi-Criteria Compound Prioritisation and Go/No-Go Signal Modelling: Machine Learning Frameworks for Enhanced Decision-Making in Drug Development
type: item
description: Drug development is a complex field with important decision-making processes that directly lead to the success or failure of pharmaceuticals. There is increasing interest in employing artificial intelligence (AI) to assist in these decision-making processes. AI technologies generally claim to improve the efficiency and accuracy of these processes. AI is seen to be able to scroll through vast datasets, find patterns and associations that are missed by humans.
subject: Articles
  topic: section
subject: multi-criteria compound prioritisation
  topic: keywords
subject: go/no-go signal modelling
  topic: keywords
subject: machine learning frameworks
  topic: keywords
subject: enhanced decision-making
  topic: keywords
creator: Małgorzata Michalewicz
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 9
ending page: 16

Multivariate Time-Series Forecasting and Biomarker Trajectory Modelling: Advanced Predictive Analytics Frameworks for Pharmaceutical Research and Development
type: item
description: The application of advanced predictive analytics in pharmaceuticals, driven by artificial intelligence (AI), is revolutionizing how drug efficacy and safety are forecasted. In recent years, AI technologies—such as machine learning (ML), neural networks, and deep learning—have increasingly integrated into pharmaceutical research and development (R&D), enabling more precise predictions of drug performance across diverse patient populations.
subject: Articles
  topic: section
subject: multivariate time-series forecasting
  topic: keywords
subject: biomarker trajectory modelling
  topic: keywords
subject: advanced predictive analytics frameworks
  topic: keywords
subject: pharmaceutical research
  topic: keywords
subject: machine learning
  topic: keywords
creator: Andrés Páez-Gaviria
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 17
ending page: 30

Network Behavioural Analysis and Claim Pattern Recognition: AI-Based Computational Strategies for Insurance Fraud Prevention
type: item
description: Take the increasing number of claims made by the use of social media; paying benefits due to non-verifiable claims and the numbers used by organized crime to commit insurance fraud. All the processes described above represent surprise factors related to the underwriting result of an insurance company. Hence the importance that the insurance business gives to fraud prevention and detection strategies.
subject: Articles
  topic: section
subject: network behavioural analysis
  topic: keywords
subject: claim pattern recognition
  topic: keywords
subject: computational strategies
  topic: keywords
subject: insurance fraud prevention
  topic: keywords
subject: machine learning
  topic: keywords
creator: Aliaksandr Pinchuk
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 31
ending page: 39

Physiological Deterioration Scoring and Mortality Risk Stratification: Machine Learning Models for Early Outcome Prediction in Critical Care Settings
type: item
description: In recent years, there has been a growing body of work on applications of machine learning models in both the critical care unit and emergency department. It is well established that AI can provide insights beyond traditional mortality prediction tools, such as illness severity stratification, estimation of the impact of acute changes, early warning of sepsis and delirium, and end-of-life care strain, as well as novel ways of understanding the determinants of length of stay in an ICU setting.
subject: Articles
  topic: section
subject: physiological deterioration scoring
  topic: keywords
subject: mortality risk stratification
  topic: keywords
subject: machine learning models
  topic: keywords
subject: early outcome prediction
  topic: keywords
creator: David Kim
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-12-31
publication date: 2025-12-31
starting page: 40
ending page: 54

Policyholder Interaction Mining and Churn Propensity Modelling: AI-Enhanced Analytical Frameworks for Insurance Customer Insight Generation
type: item
description: The increasing access to customer-level data across channels, products, and services is a unique and advantageous position for the insurance industry to be in. However, few analytics directors believe this information reveals a lot about a customer. As a result, many insurers are working to create an understanding of customer preferences and behavior, not just the 'what' but the 'why' behind their decisions, in real time. There is greater complexity in customer relationships.
subject: Articles
  topic: section
subject: policyholder interaction mining
  topic: keywords
subject: churn propensity modelling
  topic: keywords
subject: ai-enhanced analytical frameworks
  topic: keywords
subject: insurance customer insight generation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anibal Traça
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 13

Preference-Sensitive Decision Support and Care Pathway Personalisation: AI-Driven Tools for Advancing Patient-Centred Clinical Care Models
type: item
description: Patient-centered care has become an important focal point in healthcare. Patients and families are now seen as essential members of an interprofessional care team collaborating toward recovery and health maintenance. People have highlighted the need for care that takes their preferences, values, and culture into account. As such, the healthcare profession has shifted from a one-size-fits-all model to one that attempts to take individual differences into account.
subject: Articles
  topic: section
subject: preference-sensitive decision support
  topic: keywords
subject: care pathway personalisation
  topic: keywords
subject: tools
  topic: keywords
subject: advancing patient-centred clinical care models
  topic: keywords
subject: machine learning
  topic: keywords
creator: Mariana Molina
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 14
ending page: 21

Regulatory Submission Intelligence and Benefit-Risk Signal Extraction: AI-Driven Frameworks for Optimising Drug Approval Pathway Navigation
type: item
description: Current approaches to drug approval processes have been the subject of criticism and are in need of innovation due to the prevalence of inefficiencies that have resulted in the requirement of years and billions of dollars to bring new treatments to the market. Clinical approval processes involve a cascade of largely sequential examination steps with high significance and expediency, in which the decision at each step ultimately leads to the final approval of the drug.
subject: Articles
  topic: section
subject: regulatory submission intelligence
  topic: keywords
subject: benefit-risk signal extraction
  topic: keywords
subject: frameworks
  topic: keywords
subject: optimising drug approval pathway navigation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Anibal Traça
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
date: 2026-06-30
publication date: 2026-06-30
starting page: 22
ending page: 30

Continuous Bioactivity Screening and Lead Optimisation in Accelerated Timelines: The Role of Real-Time AI Platforms in Modern Drug Discovery
type: item
description: The pharmaceutical design lifecycle has begun to integrate artificial intelligence (AI) in order to aid the complex process of drug discovery. Historically speaking, choosing the appropriate drug constitutes the critical aspect of developing an appropriate pharmaceutical. As diseases have become more complex, the matter of choosing an appropriate therapeutic agent has become even more challenging.
subject: Articles
  topic: section
subject: continuous bioactivity screening
  topic: keywords
subject: lead optimisation
  topic: keywords
subject: accelerated timelines
  topic: keywords
subject: the role
  topic: keywords
subject: real-time ai platforms
  topic: keywords
subject: machine learning
  topic: keywords
creator: Michel Beauregard
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
date: 2022-12-31
publication date: 2022-12-31
starting page: 37
ending page: 50

Customer Intent Modelling and Contextual Product Recommendation: Deep Learning Approaches to Personalised Banking Service Delivery
type: item
description: The banking and financial services sector in India has been growing at a rapid pace, posing a challenge to both regulatory authorities and service providers. As banks face the challenge of attracting and retaining customers due to intensified competition, technology is proving to be a crucial advantage in developing personalized banking services based on customer information.
subject: Articles
  topic: section
subject: customer intent modelling
  topic: keywords
subject: contextual product recommendation
  topic: keywords
subject: deep learning approaches to personalised banking service delivery
  topic: keywords
subject: machine learning
  topic: keywords
creator: Dzmitry Tsetseruk
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 10

Behavioural Cohort Analysis and Lifetime Value Stratification: AI-Driven Customer Segmentation Frameworks for Insurance Portfolio Management
type: item
description: It is increasingly important for insurance companies to adapt to the changing nature of consumers, who have diverse needs and demand coverage tailored to their particular lifestyle or specific event. Moreover, insurers operating in that sector of the market are facing intense competition that erodes their profit margins. To help tackle these challenges, customer segmentation has attracted growing scholarly and managerial interest.
subject: Articles
  topic: section
subject: behavioural cohort analysis
  topic: keywords
subject: lifetime value stratification
  topic: keywords
subject: customer segmentation frameworks
  topic: keywords
subject: insurance portfolio management
  topic: keywords
subject: machine learning
  topic: keywords
creator: Andrés Páez-Gaviria
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-06-30
publication date: 2021-06-30
starting page: 64
ending page: 73

Capacity Utilisation Modelling and Patient Flow Intelligence: AI-Based Frameworks for Healthcare Resource Allocation and Management Optimisation
type: item
description: The planning and allocation of staff, equipment, and facilities in hospitals have grown increasingly complex due to a growing demand for specialized staff and resources, as well as the need for hospitals to accommodate specific patient needs and requirements. With access to few ideal databases and restricted samples due to the lack of a mandated ratio, these calculations are not always grounded in empirical proof. As a consequence, this key metric of a well-run hospital is often subjective.
subject: Articles
  topic: section
subject: capacity utilisation modelling
  topic: keywords
subject: patient flow intelligence
  topic: keywords
subject: frameworks
  topic: keywords
subject: healthcare resource allocation
  topic: keywords
subject: machine learning
  topic: keywords
creator: Aisha Hassan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 29
ending page: 37

Clinical Entity Recognition and Longitudinal Patient Summary Generation: AI-Driven Natural Language Processing Systems for Automated Medical Record Analysis
type: item
description: Artificial intelligence (AI) is increasingly playing a transformative role in the healthcare sector. In addition to many other possibilities, one of the first considerations is its opportunity to boost diagnostic accuracy and quicker, more informed decisions by care providers, allowing for significant advancements in resource use and patient care. In terms of hard data, the efficiency and resources of healthcare forecasting are significantly threatened.
subject: Articles
  topic: section
subject: clinical entity recognition
  topic: keywords
subject: longitudinal patient summary generation
  topic: keywords
subject: natural language processing systems
  topic: keywords
subject: automated medical record analysis
  topic: keywords
subject: machine learning
  topic: keywords
creator: Stefan Wagner
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 38
ending page: 48

Clinical Feature Engineering and Outcome Prediction in Electronic Health Records: Machine Learning Approaches to Enhanced Predictive Analytics in Healthcare
type: item
description: The article is devoted to the application of models of machine learning and artificial intelligence for data analysis of medical information and the definition of the state of the human body. Many questions that are directly or indirectly connected with healthcare deserve special attention. These include the problems of diagnostics of diseases and anomalies, methods for predicting the course of complex diseases, as well as tasks aimed at determining optimal treatment strategies.
subject: Articles
  topic: section
subject: clinical feature engineering
  topic: keywords
subject: outcome prediction
  topic: keywords
subject: electronic health records
  topic: keywords
subject: machine learning approaches to enhanced predictive analytics
  topic: keywords
subject: healthcare
  topic: keywords
creator: Olga Petrova
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
date: 2021-12-31
publication date: 2021-12-31
starting page: 49
ending page: 58

How Cleanroom Consumables Act as Critical Infrastructure (Not an Afterthought)
type: item
description: Modern buildings depend on invisible systems. Power distribution, structured cabling, control panels, and data pathways operate quietly behind walls and enclosures, expected to perform flawlessly for years. When failures occur, investigations typically focus on hardware specifications, installation quality, or environmental conditions like heat and humidity. What is often overlooked is a quieter risk factor: micro-contamination introduced during installation and maintenance. Swabs, wipes, gloves, and cleaning tools are usually categorized as expendable supplies; items to be sourced cheaply and replaced often. In reality, these consumables play a direct role in system reliability. In sensitive electrical, digital, and low-voltage environments, they function as a form of protective infrastructure, influencing performance, uptime, and lifecycle cost. The Hidden Layer of Modern Infrastructure As buildings become smarter and more connected, tolerances continue to shrink. Fiber connectors, edge computing hardware, and compact electrical enclosures are far less forgiving than legacy systems. A small amount of lint, oil, or residue imperceptible to the naked eye can introduce signal loss, thermal inefficiencies, or intermittent faults that are difficult to trace. Unlike catastrophic failures, contamination-related issues tend to degrade systems slowly. They appear as nuisance outages, unexplained performance drops, or recurring service calls. Because the symptoms are subtle, the root cause is often misdiagnosed as a component defect rather than a process issue. Where Micro-Contamination Causes Real Failures In commercial and industrial facilities, contamination risk is highest in areas that combine density, sensitivity, and human interaction: Fiber optic connectors and adapters, where microscopic debris can disrupt light transmission Electrical panels and control cabinets, where dust and residues contribute to heat buildup and corrosion Low-voltage and AV terminations, whic…
subject: Blogs
  topic: section
creator: Ayush Jain
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-11
publication date: 2026-02-11

IoT-Enhanced Energy Management Strategies for Sustainable Smart Manufacturing Practices
type: item
description: The integration of Internet of Things (IoT) technologies into smart manufacturing processes has emerged as a transformative approach to addressing energy efficiency challenges while simultaneously advancing sustainability objectives. This paper critically investigates IoT-enhanced energy management strategies in smart manufacturing, focusing on their potential to optimize energy consumption and achieve sustainability goals within industrial operations. The proliferation of IoT-enabled devices, sensors, and communication systems allows for real-time data acquisition, advanced analytics, and predictive decision-making, thereby enhancing energy efficiency across manufacturing ecosystems. These technologies facilitate granular monitoring of energy usage, enabling the identification of inefficiencies, predictive maintenance, and the adaptive optimization of energy-intensive processes. The research begins with an in-depth analysis of IoT architecture tailored to energy management in industrial settings, emphasizing the interplay between physical systems, cyber-physical integration, and communication protocols such as MQTT, CoAP, and OPC UA. By leveraging big data analytics, edge computing, and artificial intelligence (AI) algorithms, manufacturers can dynamically allocate energy resources and reduce wastage. The paper highlights advanced frameworks such as digital twins and IoT-based energy monitoring systems, which provide real-time visualization of energy consumption patterns and facilitate informed, data-driven decision-making at every stage of production. A key focus of the research is the role of IoT in predictive and prescriptive analytics. Predictive models, fueled by machine learning algorithms, forecast energy demands and preempt disruptions, while prescriptive analytics recommend optimal operational adjustments. Case studies are presented to illustrate successful implementations of IoT-enhanced energy management in industries such as automotive, electronics, and…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: energy management
  topic: keywords
subject: smart manufacturing
  topic: keywords
subject: sustainability
  topic: keywords
creator: Sunthar Subramanian
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-01-03
publication date: 2024-01-03
starting page: 115
ending page: 152

Industry Standard IoT Architectures for Secure Data Exchange in Smart Manufacturing Ecosystems
type: item
description: The proliferation of the Internet of Things (IoT) in smart manufacturing ecosystems has catalyzed transformative advancements in industrial operations, enhancing productivity, automation, and data-driven decision-making. However, the interconnected nature of IoT networks introduces significant cybersecurity challenges, data integrity concerns, and scalability issues. This research explores a standardized IoT architecture tailored to the unique demands of smart manufacturing ecosystems, emphasizing secure, reliable, and efficient data exchange mechanisms. The study systematically addresses key aspects of IoT implementation in Industry 4.0, focusing on cybersecurity protocols, data integrity assurance, and interoperability across diverse stakeholders in the supply chain. A comprehensive review of existing IoT architectures, industry standards, and best practices underpins the proposed framework, which integrates advanced cryptographic techniques, secure communication protocols, and decentralized data management strategies. Particular emphasis is placed on leveraging emerging technologies such as blockchain, edge computing, and artificial intelligence to enhance the security and efficiency of data exchange in heterogeneous manufacturing environments. The framework incorporates a multilayered approach that aligns with industrial cybersecurity standards such as IEC 62443, NIST CSF, and ISO/IEC 27001, ensuring compliance and adaptability to dynamic operational requirements. The paper delineates the architecture’s core components, including sensor-level data acquisition, secure communication channels, and cloud-based analytics, highlighting their interdependencies and contributions to a robust IoT ecosystem. Additionally, it discusses techniques for mitigating risks associated with unauthorized access, data tampering, and supply chain vulnerabilities. Case studies from real-world implementations demonstrate the framework's efficacy in improving operational resilience and s…
subject: Articles
  topic: section
subject: IoT architecture
  topic: keywords
subject: smart manufacturing
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: cybersecurity
  topic: keywords
creator: Sunthar Subramanian
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-06-28
publication date: 2024-06-28
starting page: 157
ending page: 188

IoT-Driven Digital Twin Models for factories: Simulation and Real-Time tracking to Optimize Industrial Operations
type: item
description: The advent of the Internet of Things (IoT) and its integration into manufacturing has catalyzed significant advancements in the development of digital twin models for smart factories. Digital twins, functioning as virtual representations of physical manufacturing systems, enable the seamless interplay between simulation and real-time tracking, offering transformative potential for industrial operations. This study delves into the underlying principles, architecture, and practical implementations of IoT-driven digital twin models, underscoring their role in optimizing manufacturing processes through predictive analytics and dynamic performance monitoring. IoT-driven digital twin models rely on robust frameworks comprising interconnected sensors, edge computing devices, and cloud-based platforms to facilitate bidirectional data flow. Real-time data acquisition and processing enable the digital twin to reflect the physical system's state with high fidelity, fostering comprehensive visibility into factory operations. This capability empowers manufacturers to simulate various scenarios, perform root cause analyses, and identify potential inefficiencies or equipment failures before they occur. The study elucidates the technical requirements for developing such systems, including data integration pipelines, model synchronization, and system scalability, with an emphasis on mitigating latency and ensuring interoperability across diverse industrial ecosystems. The paper presents case studies highlighting successful applications of IoT-driven digital twins in predictive maintenance, energy optimization, and supply chain management. These implementations illustrate the models' ability to preemptively address disruptions, thereby reducing operational downtime and enhancing resource utilization. Predictive analytics, enabled through machine learning algorithms embedded within the digital twin framework, provide actionable insights for informed decision-making, augmenting factory…
subject: Articles
  topic: section
subject: IoT-driven digital twins
  topic: keywords
subject: smart factories
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Sunthar Subramanian
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-01-13
publication date: 2023-01-13
starting page: 101
ending page: 136

Integrating IoT and Manufacturing process for Real-Time Predictive Maintenance in High-Throughput Production Environments
type: item
description: The advent of the Internet of Things (IoT) has significantly influenced the paradigm of modern manufacturing by facilitating seamless connectivity, data acquisition, and real-time analytics in high-throughput production environments. This paper delves into the integration of IoT-enabled sensors and advanced analytics platforms to enable real-time predictive maintenance (PdM) within large-scale manufacturing systems. Predictive maintenance, a critical component of Industry 4.0, leverages continuous data monitoring and machine learning (ML) models to predict potential equipment failures, mitigate unplanned downtimes, and optimize operational efficiency. Unlike conventional reactive or preventive maintenance strategies, PdM minimizes operational disruptions by precisely identifying anomalies in machine behavior and scheduling maintenance interventions based on condition-driven insights. The integration of IoT into manufacturing ecosystems introduces a robust mechanism for acquiring real-time machine data, such as vibration, temperature, pressure, and other critical performance parameters. IoT-enabled devices, when networked with edge computing nodes and centralized cloud platforms, facilitate a bi-directional flow of data, fostering robust predictive analytics pipelines. By processing this sensor data through advanced ML algorithms, manufacturing setups can identify degradation patterns, forecast failures, and proactively manage asset lifecycles. This study investigates the architecture of IoT-based predictive maintenance systems, encompassing components such as sensor networks, data acquisition modules, edge and cloud computing infrastructures, and AI-driven decision-making frameworks. Incorporating IoT for PdM within high-throughput manufacturing facilities presents unique challenges, including the scalability of IoT networks, interoperability among heterogeneous devices, real-time data processing constraints, and the deployment of reliable predictive algorithms. Mor…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: predictive maintenance
  topic: keywords
creator: Sunthar Subramanian
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-07-15
publication date: 2022-07-15
starting page: 1
ending page: 36

Advanced Machine Learning Techniques for Anomaly Detection in Edge Computing Security: A Framework for Real-Time Threat Mitigation
type: item
description: The rapid proliferation of edge computing, coupled with the expansion of IoT devices, 5G infrastructure, and decentralized computing systems, has significantly transformed the landscape of cybersecurity. Edge computing environments, which bring computation closer to the data source, have introduced new challenges related to security and anomaly detection. As traditional security paradigms struggle to address the unique characteristics of edge-based systems, the integration of advanced machine learning (ML) techniques for real-time threat mitigation has become crucial. This paper investigates the potential of advanced ML methods, including unsupervised clustering, autoencoders, and graph-based models, for anomaly detection in edge computing security. These techniques offer robust solutions for identifying subtle and sophisticated threats in dynamic, resource-constrained environments, where real-time response is essential. Edge computing networks, particularly those in IoT and 5G ecosystems, face distinctive security threats that necessitate novel approaches for intrusion detection and prevention. Traditional security measures, which often rely on centralized models, are ill-suited to address the distributed nature of edge computing and its inherent limitations, such as bandwidth constraints, computational power limitations, and high-volume data streams. Anomaly detection, which involves identifying patterns that deviate from expected behavior, is a pivotal component of security frameworks in edge environments. This research focuses on the development of a comprehensive framework that leverages advanced ML models for anomaly detection, designed to operate within the specific constraints and operational characteristics of edge computing systems. The first part of the paper explores unsupervised clustering techniques, which do not require labeled data and are well-suited to dynamic environments where labeled data is scarce or non-existent. Techniques such as K-means, DB…
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: machine learning
  topic: keywords
subject: unsupervised clustering
  topic: keywords
creator: Sandeep Kampa
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-11-08
publication date: 2024-11-08
starting page: 81
ending page: 120

The Role of Leadership in Digital Transformation of Large Enterprises
type: item
description: Digital transformation has become a pivotal element in the evolution of large enterprises, reshaping traditional business models, operational processes, and customer interactions through the integration of advanced digital technologies. The role of leadership in orchestrating such a transformation is paramount, as the complexity of digital initiatives requires a combination of strategic vision, organizational agility, and a deep understanding of technology's potential to disrupt and enhance business outcomes. This paper explores the multifaceted role that leadership plays in the successful execution of digital transformation within large organizations, with a focus on how leaders can navigate the challenges, uncertainties, and opportunities inherent in this process. In large enterprises, digital transformation is not merely a technological shift; it encompasses a comprehensive rethinking of how value is created and delivered. Leaders in these organizations must not only foster a culture that is receptive to innovation but also possess the ability to bridge the gap between technological advancements and business imperatives. This paper argues that leadership plays a crucial role in ensuring that digital transformation aligns with the organization's overarching strategy, allowing the enterprise to remain competitive in an increasingly digitized economy. By focusing on leadership's strategic and operational influence, this research highlights the importance of visionary leadership in shaping the trajectory of digital transformation, particularly in large, complex enterprises where the stakes are higher, and the resistance to change is often more entrenched. The paper further delves into the challenges that leaders face in steering digital transformation initiatives. These challenges are not solely technical but also include managing organizational resistance, aligning diverse stakeholders, fostering cross-functional collaboration, and ensuring that digital initiatives …
subject: Articles
  topic: section
subject: digital transformation
  topic: keywords
subject: leadership
  topic: keywords
creator: Sai Ganesh Reddy
creator: Vipin Saini
creator: Tanzeem Ahmad
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-10-18
publication date: 2023-10-18
starting page: 1
ending page: 38

The Impact of AI on Organizational Change in Digital Transformation
type: item
description: Artificial intelligence (AI) has become a fundamental driver in the ongoing wave of digital transformation, prompting profound organizational change across multiple sectors. This research paper examines how AI influences organizational structures, workflows, decision-making processes, and cultural dynamics within organizations pursuing digital transformation initiatives. The study begins by delineating the role of AI in enabling efficient and data-driven operations, demonstrating its capacity to enhance productivity, optimize processes, and increase flexibility within corporate structures. AI systems facilitate real-time data analysis, predictive modeling, and autonomous decision-making, which collectively redefine traditional organizational practices and necessitate structural and strategic adaptations. Organizations integrating AI technologies into their digital transformation strategies frequently undergo changes that extend beyond the implementation of new technologies; they experience shifts in organizational culture, workforce composition, and skill requirements, further influenced by the need to align with ethical standards, data governance policies, and regulatory frameworks. A significant aspect of this research is its focus on how AI reconfigures decision-making paradigms. Traditional, hierarchical decision-making models are increasingly being replaced or complemented by more decentralized, data-driven approaches that leverage AI’s analytical capabilities. Through machine learning and data analytics, AI provides insights that empower decision-makers at all organizational levels, enabling faster and more accurate responses to dynamic market conditions and customer demands. This shift not only accelerates decision cycles but also redistributes authority across different levels of an organization, contributing to a more agile and adaptable organizational model. Moreover, the study explores how AI-driven transformation affects workforce dynamics, specifically …
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: organizational change
  topic: keywords
creator: Ajay Aakula
creator: Vipin Saini
creator: Taneem Ahmad
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-05
publication date: 2024-03-05
starting page: 75
ending page: 115

Implementing TOGAF for Large-Scale Healthcare Systems Integration
type: item
description: This paper explores the application of The Open Group Architecture Framework (TOGAF) in the integration of large-scale healthcare systems, focusing on its potential to optimize system interoperability, data management, and organizational alignment. Healthcare organizations face immense challenges when integrating complex and diverse systems that need to function cohesively across various departments, platforms, and regions. The introduction of advanced technologies such as electronic health records (EHRs), telemedicine, and health information exchanges (HIEs) has necessitated a framework capable of managing this complexity while maintaining regulatory compliance and data privacy standards. TOGAF, a widely accepted enterprise architecture framework, offers a structured approach for addressing these challenges by providing a comprehensive methodology for designing, planning, implementing, and governing enterprise information architectures. Through its iterative Architecture Development Method (ADM), TOGAF facilitates the identification of business requirements, stakeholder needs, and technological constraints, all of which are crucial for healthcare organizations aiming to streamline large-scale systems integration. The research paper delves into how TOGAF’s architectural principles, such as modularity, scalability, and service orientation, can be adapted to the healthcare domain. It highlights how the framework supports interoperability among disparate systems by promoting standardization and enabling seamless communication across healthcare networks. Furthermore, the paper examines TOGAF’s ability to address the unique data governance challenges inherent to healthcare, including the secure handling of sensitive patient information and adherence to regulatory frameworks such as HIPAA and GDPR. These regulations impose stringent requirements for data privacy, and TOGAF’s governance structure provides a robust mechanism for ensuring compliance while facilitating the se…
subject: Articles
  topic: section
subject: TOGAF
  topic: keywords
subject: enterprise architecture
  topic: keywords
creator: Dheeraj Kumar Dukhiram Pal
creator: Jenie London
creator: Ajay Aakula
creator: Subrahmanyasarma Chitta
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-05-08
publication date: 2022-05-08
starting page: 55
ending page: 102

Evaluating FHIR's impact on Health Data Interoperability
type: item
description: The Fast Healthcare Interoperability Resources (FHIR) standard, developed by Health Level Seven International (HL7), represents a significant advancement in healthcare information exchange by promoting health data interoperability across diverse systems and applications. This paper aims to provide a comprehensive evaluation of FHIR’s impact on enhancing data interoperability within healthcare, a critical factor for improving care coordination, patient outcomes, and healthcare system efficiency. The increasing complexity of healthcare data management, driven by the proliferation of electronic health records (EHRs), wearable health devices, and patient-generated data, has underscored the need for effective data sharing mechanisms across disparate healthcare systems. FHIR was introduced as a standard designed to address these challenges by offering a flexible, modular framework for exchanging healthcare data in a structured and standardized manner, leveraging widely accepted web technologies such as RESTful APIs, XML, and JSON. This study delves into FHIR’s capabilities, focusing on its technical framework, including its resources, extensions, and profiles that enable the seamless exchange of healthcare information. In this regard, the research will evaluate the extent to which FHIR has achieved interoperability goals, comparing it with previous standards such as HL7 version 2, HL7 version 3, and Clinical Document Architecture (CDA). The analysis will cover several dimensions, including the technical complexity of implementation, adaptability across various healthcare settings, and its influence on data accessibility and consistency. A key focus of this paper will be on the role of FHIR in bridging the gap between diverse healthcare IT infrastructures, ranging from EHR systems to cloud-based health applications, ensuring that healthcare providers can exchange data effortlessly. The paper will also critically assess the adoption and implementation challenges associated …
subject: Articles
  topic: section
subject: FHIR
  topic: keywords
subject: electronic health records
  topic: keywords
creator: Vipin Saini
creator: Sai Ganesh Reddy
creator: Dheeraj Kumar
creator: Tanzeem Ahmad
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-03-10
publication date: 2021-03-10
starting page: 28
ending page: 63

Real-Time AI-Enhanced Driver Monitoring Systems
type: item
description: Driver distraction and fatigue have become major concerns for road transport authorities worldwide. Fatigued and distracted drivers' reaction times are delayed, they have decreased sensory perceptions, and their decision-making abilities are affected. They have an increased risk of being involved in a road accident, which can cause property damage and personal injury as well as fatalities. From 2019 to 2020, there was an increase in deaths on Australian roads of 3.8%, and fatalities involving heavy vehicles increased by 4.5%. A proposed increase in heavy vehicle accidents in New South Wales may not be a surprise given the percentage of all commuter travel that heavy vehicles represent. It is thus most important to design and implement an accurate driver monitoring system that can detect inattentive, distracted, and fatigued drivers as quickly and correctly as possible. While significant expertise currently resides within specialist personnel, the research now seeks to provide those analysts with an AI-enhanced driver monitoring system, presenting a live real-time video summarizing any instances of the driver being inattentive, fatigued, or tired.
subject: Articles
  topic: section
creator: Dr. Hans Müller
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-11-12
publication date: 2024-11-12
starting page: 62
ending page: 79