Interconnected Data Research Articles

A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts.

Cybersecurity governance: Strengthening policy frameworks to address global cybercrime and data privacy challenges

In an increasingly interconnected world, cybercrime and data privacy challenges have escalated into critical global issues, threatening governments, organizations, and individuals alike. The proliferation of sophisticated cyberattacks, including ransomware, data breaches, and nation-state hacking, underscores the urgent need for robust cybersecurity governance. Compounding these threats are evolving regulatory landscapes and a lack of harmonized international frameworks, leaving gaps in addressing cross-border cybercrimes and ensuring data privacy. This article explores the imperative of strengthening cybersecurity policy frameworks to combat global cybercrime and safeguard sensitive data. It begins with an overview of the current cybersecurity governance landscape, highlighting gaps and inconsistencies in policy enforcement. Emphasis is placed on the integration of adaptive regulatory mechanisms, public-private partnerships, and standardized global practices to ensure a unified response to cyber threats. Key strategies discussed include the adoption of proactive risk assessment methodologies, the implementation of privacy-by-design principles, and the enhancement of international cooperation for intelligence sharing and joint cyber defense initiatives. The article also delves into case studies illustrating the effectiveness of comprehensive policy frameworks in mitigating cyber risks and fostering organizational resilience. As cyber threats continue to evolve, addressing these challenges requires a coordinated and forward-looking approach that balances innovation with security. By advancing cybersecurity governance, stakeholders can strengthen trust in digital ecosystems, safeguard privacy, and ensure the continuity of global digital operations.

Design and Development of Schema for Schemaless Databases

A schema database functions as a repository for interconnected data points, facilitating comprehension of data structures by organizing information into tables with rows and columns. These databases utilize established connections to arrange data, with attribute values linking related tuples. This integrated approach to data management and distributed processing enables schema databases to maintain models even when the working set size surpasses available RAM. However, challenges such as data quality, storage, scarcity of data science professionals, data validation, and sourcing from diverse origins persist. Notably, while schema databases excel at reviewing transactions, they often fall short in updating them effectively. To address these issues, a Chimp-based radial basis neural model (CbRBNM) is employed. Initially, the Schemaless database was considered and integrated into the Python system. Subsequently, compression functions were applied to both schema and schema-less databases to optimize relational data size by eliminating redundant files. Performance validation involved calculating compression parameters, with the proposed method achieving memory usage of 383.37[Formula: see text]Mb, a computation time of 0.455[Formula: see text]s, a training time of 167.5[Formula: see text]ms, and a compression rate of 5.60%. Extensive testing demonstrates that CbRBNM yields a favorable compression ratio and enables direct searching on compressed data, thereby enhancing query performance.

Dynamics of Soil Carbon Average Content at Different Depths: Insights from a Global Approach to Climate Change Mitigation

ABSTRACT This study provides an in-depth examination of the average values of gravimetric carbon content, measured in grams per kilogram (g/kg), and the organic Carbon (OC) content, quantified in kilograms per square meter (kg/m2), within various soil classifications and depths. Highlighting the relevance of such research, it delves into the intricacies of soil OC dynamics across diverse depth strata and offers a comparative analysis of different soil types, each with distinct carbon sequestration capacities. Utilizing the latest version of the world soil database, the research integrates three interconnected data sets: soil classification, density, and OC. In total, the study includes 51,507 soil profile layers to calculate the average gravimetric OC content in the fine-earth fraction. In parallel, the average OC content in the fine-earth fraction was determined across 6,197 soil profile layers. This comprehensive data is organized into 34 separate soil units, each dissected across four depth categories: 0–30 cm, 0–100 cm, 0–200 cm and 0–2590 cm. The findings from this analysis reveal a consistent pattern: as depth increases, both the gravimetric content and average OC content tend to decrease. However, it is noteworthy that this trend is not universal. Certain soil units demonstrate an opposing behavior, with an increase in average OC content observed at greater depths, contradicting the prevailing trend. This divergence underscores the complexity of soil OC dynamics and the inherent variability across different soil units.

Data Modeling Essentials: Techniques, Best Practices, and Future Trends

Data modeling, in essence, is based on the process of data structuring and organization to effectively handle, analyze, and make informed decisions across organizations. This paper reviews the major concepts and methods significant for modern data architecture in "Data Modeling Essentials: Techniques, Best Practices, and Future Trends.". We start by considering the base techniques, namely Entity-Relationship and Dimensional modeling, and look into their applications in operational and analytical data systems. The paper also covers more advanced topics that include Data Vault modeling for agile data warehousing and NoSQL modeling to accommodate unstructured and semi-structured data, increasingly important in today's big data era. The paper also gives an overview of best practices in developing scalable, flexible data models: stakeholder communication, documentation, and the use of automation tools in accelerating model development and consistency. Data quality, adherence to regulations, and alignment of models with business objectives are among the practices identified as crucial in today's fast-moving data environments. New trends are taking precedence, and data modeling is continuously evolving. We will review the rise of AI- assisted data modeling, graph database structures for highly interconnected data, and model-driven development to accelerate system deployments. This paper examines actionable insights for data professionals, IT teams, and business leaders on how to design robust, agile models that improve the quality of data, enhance decision-making, and foster innovation in an ever-changing data ecosystem. Keywords Data Modeling, Entity-Relationship (ER) Modeling, Dimensional Modeling, Data Quality, Scalable Data Models, Model Driven Development (MDD), Data Management, Structured & Unstructured Data, Future Trends in Data Modeling.

Artificial intelligence and big data-driven evaluation research and practices: A systematic literature review

The widespread adoption of digitalization and artificial intelligence, alongside the abundance of big data, has significantly transformed societies. Recently, there has been an increasing interest in leveraging big data and artificial intelligence to capture and analyze social transformative change in evaluation. However, there is no consensus on the ethical and appropriate use of these tools in evaluation. This article used a systematic literature review to provide an overview of using big data and artificial intelligence for evaluation purposes, identifying challenges faced. Unresolved issues encompass ethical, methodological, and ownership concerns. The study suggests ways to address these challenges and advocates for united efforts to mix big data and artificial intelligence with traditional approaches. To achieve this, it emphasizes the necessity of leveraging interconnected data platforms, mitigating ethical risks, and enhancing evaluators’ competencies in computer and data science, which is essential for the integration of big data and artificial intelligence in the evaluation field.

FTPComplEx: A flexible time perspective approach to temporal knowledge graph completion

The dynamic nature of interconnected data evolving over time poses significant challenges for graph representation and reasoning, particularly as temporal knowledge graphs scale in size and complexity. Existing models like TPComplEx (Time Perspective Complex Embedding) leverage tensor decomposition techniques to capture temporal dynamics, but their static weighting approach often lacks the flexibility needed to adapt to the nuanced evolution of relationships and entities. This rigidity can lead to missed temporal dependencies and loss of valuable insights, especially in large-scale graphs comprising millions or even billions of factual entries. To overcome these limitations, we propose FTPComplEx (Flexible Time Perspective Complex Embedding), a novel embedding model that introduces adjustable weights to dynamically modulate the influence of temporal information. This flexibility enables FTPComplEx to more accurately capture the intricate interactions between entities, relations, and time, providing a more robust understanding of temporal dynamics within knowledge graphs. Our extensive evaluations on benchmark datasets, including YAGO15k, ICEWS, and GDELT, demonstrate that FTPComplEx achieves state-of-the-art results, outperforming TPComplEx and other existing models. Notably, on the YAGO15k dataset, FTPComplEx achieves a 9.04% improvement in Mean Reciprocal Rank (MRR) and an 11.35% increase in Hits@1, demonstrating its effectiveness in managing complex temporal relationships. Further analysis shows that FTPComplEx maintains strong performance even with lower-rank embeddings, significantly reducing computational costs while maintaining accuracy.

External information enhancing topic model based on graph neural network

In the digital age, social media platforms have seen a surge in user-generated content, particularly short-form we-media content. Traditional topic modeling methods often struggle to effectively analyze such content due to their limited generalization ability and interpretability. To address this issue, we propose the Co-occurrence Graph Topic Model (COGTM), a novel approach designed to enhance topic modeling in the context of long-short text co-occurrence scenarios. COGTM leverages the inherent interconnectedness between short and associated long-texts, as well as semantically similar words, within the text corpus. By incorporating these associations into the modeling process, COGTM aims to capture richer semantic information and improve the interpretability of the learned topics. Empirical analysis demonstrates that COGTM outperforms baseline models in various text classification and clustering tasks. By effectively capturing the latent associations between different types of text elements, COGTM offers a promising approach to topic modeling in scenarios involving diverse and interconnected textual data.

Modern Data Storage Technologies in the Context of Industry 4.0

Existing and currently widely used data storage technologies are considered. It is noted that the growth in the volume of digital information resources at an astronomical pace has led to the rapid development of technologies for their creation, storage, and access to them. In the context of Industry 4.0, the use of digital technologies to create intelligent and interconnected production and data processing processes is of particular importance. Blockchain technology and cloud technologies have become integral components of modern digital transformation. Taking into account the current state of data storage and blockchain technologies, network capabilities, enterprise needs and other factors, the possibilities of selecting and using storage tools and technologies, principles of joint implementation and application of different technologies, as well as modern virtualization approaches and blockchain technology for solving distributed data storage issues have been explored.

Mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Enhancing the security in IoT and IIoT networks: An intrusion detection scheme leveraging deep transfer learning

The Internet of Things (IoT) networks, which are defined by their interconnected devices and data streams are an expanding attack surface for cyber adversaries. Industrial Internet of Things (IIoT) is a subset of IoT and has significant importance in-terms of security. Robust intrusion detection systems (IDS) are essential for protecting these critical infrastructures. Our research suggests a novel approach to the detection of anomalies in IoT and IIoT networks that leverages the capabilities of deep transfer learning. Our methodology begins with the EdgeIIoT dataset, which serves as the basis for our data analysis. We convert the data into an appropriate image format to enable Convolutional Neural Network (CNN)-based processing. The hyper-parameters of individual machine learning models are subsequently optimized using a Random Search algorithm. This optimization phase optimizes the performance of each model by modifying the hyper-parameters that are unique to the learning algorithms. The performance of each model is meticulously assessed subsequent to hyper-parameter optimization. The top-performing models are subsequently, strategically selected and combined using the ensemble technique. The IDS scheme’s overall detection accuracy and generalizability are improved by the integration of strengths from multiple models. The proposed scheme demonstrates significant effectiveness in identifying a broad spectrum of attacks, encompassing a total of 14 distinct attack types. This comprehensive detection capability contributes to a more secure and resilient IoT ecosystem. Furthermore, application of quantization to our best models reduces resource utilization significantly without compromising accuracy.

A Statistical Analysis of Knowledge Graph and its Applications

In today's fast-paced world driven by big data and artificial intelligence, organizing and representing vast amounts of knowledge is crucial. Knowledge graphs are dynamic structures serve as repositories for real-world knowledge, presented in the form of interconnected graph data. With their ability to encapsulate complex information, knowledge graphs have swiftly captured the attention of both academia and industry alike. In this paper, we dive deep into understanding knowledge graphs. In first Section, we start by looking at their origins and evolution. In second section describes knowledge graphs, compares them with other graph forms, and explores how academic societies have incorporated them. In conclusion, we discuss the various applications of knowledge graphs in various domains and highlight the software tools that are facilitating their advancement. By providing a systematic overview of knowledge graphs, we aim to illuminate new avenues for research and foster advancements in this burgeoning field.

Ethical challenges of the implementation of artificial intelligence in nursing care

The advancement of digitalization and technology has facilitated the utilization of artificial intelligence (AI) to enhance the delivery of healthcare and nursing services. AI refers to systems capable of exhibiting intelligent behavior, allowing them to analyze their surroundings and execute a range of tasks independently to accomplish predetermined objectives [1]. AI has been utilized extensively in the field of nursing for an extended period. Despite not being commonly recognized as AI, there has been a significant surge of interest in its application within the healthcare sector. This heightened interest has sparked new discussions regarding the intersection of AI and nursing, prompting further exploration and analysis of their relationship [2]. Over the past few years, there has been a growing focus on research highlighting the potential for the advancement of AI in the field of nursing care. AI can enhance and automate planning and scheduling processes, thereby increasing the effectiveness of human approaches. This includes tasks like generating nursing staff schedules and making care-related scheduling decisions, as well as providing support to nurses in remotely conducted tasks during direct patient interactions [3]. Nurses can enhance their ability to deliver comprehensive care by utilizing AI to consolidate various forms of interconnected data, including environmental, genomic, health, social, and demographic information [4].

Pricing Cyber Insurance: A Geospatial Statistical Approach

ABSTRACTCyberspace is a dynamic ecosystem consisting of interconnected data, devices, and individuals, with multiple network layers comprising identifiable nodes. Location‐based information can significantly improve cyber resilience decision‐making and facilitate the development of innovative cyber risk pricing tools. This article is based on a methodology that uses company geospatial data to accurately estimate the number of expected losses arising from cyberattacks. Our approach aims to build and compare statistical spatial models that allow pricing cyber policies more effectively than traditional non‐spatial methods by incorporating all available data. By accounting for spatial dependence, we can assess the risk of data breaches and contribute to the design of more efficient cyber risk policies for the insurance market.

ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.

Induction of Synaptic Plasticity in Flexible Organic Synaptic Transistors with Cross‐Linked Polymer Dielectric

AbstractControlled cross‐linking of polymer dielectric poly (4‐vinylphenol) (PVP) is demonstrated as an effective tool in enhancing the performance of flexible organic synaptic transistors (OSTs). Investigation of variation of concentration of the cross‐linking agent methylated poly (melamine‐co‐formaldehyde) (PMCF) in PVP in bilayer combination with high‐k hafnium oxide (HfO2) as gate dielectric in devices shows that the lower concentration of cross‐linking agent results in better memory performance. OSTs with 26% PMCF concentration in PVP (by mass) exhibit excellent memory performance with memory window > 4 V for VGS sweep of ±5 V, static retention of ≈104 s, dynamic retention for 500 cycles, and ≈125 continuous program/erase cycles. Pulse paired facilitation with relaxation time constants of 370 and 4670 ms respectively for slow and rapid phases with regulating modulation amplitude of ≈1 resemble a biological synapse. Through excitatory post synaptic current characteristics, spike timing dependant plasticity and spike voltage dependant plasticity are clearly observed, with low energy consumption per spike on the order of 10 pJ. Further, by leveraging the intricate interconnected data transfer and computation phenomenon, “AND” logic is effectively implemented using these OSTs. These exciting results may open up new directions toward the development of hardware for neuromorphic computing.

Bidirectional passive optical interconnected data center

AbstractA novel bidirectional passive optical interconnection is proposed for data centers (DCs). The interconnection is based on a coupler of arrayed waveguide grating. The nodes distribute on both sides and simultaneously, bidirectional transmission is allowed. Compared with the traditional passive optical interconnected DCs, the scale is doubled, and the throughput is increased. The crosstalk in simultaneously bidirectional transmission is tested and the results show it is very little. The transmission between the nodes on the same side is relayed by one node on the other side. Some relay methods are proposed, and the relay efficiency is evaluated.

Cybersecurity policy framework requirements for the establishment of highly interoperable and interconnected health data spaces.

This paper examines cybersecurity policy framework requirements for establishing highly interoperable and interconnected health data spaces, with a focus on the European Health Data Space (EHDS) and its corresponding joint action Toward European Health Data Space (TEHDAS). It explores the challenges of ensuring data security within an increasingly digital and collaborative healthcare environment, emphasizing the need for robust policy management to protect sensitive health information across diverse healthcare systems and supply chains. Through an analysis of use cases and held expert workshops, the study identifies key requirements for enhancing cybersecurity measures, fostering cross-border data exchange, and ensuring compliance with regulatory standards. It illustrates the practical implications of cybersecurity policies in a real-world scenario, demonstrating how they can be applied to enhance data security and policy effectiveness.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

IoT Approach for Ensuring Safety in smart Cities for Energy Saving

The security of city dwellers is of the utmost importance due to the increasing number of smart cities and the speed with which they are being developed. As a whole, smart city safety can be improved through the use of the Internet of Things (IoT), which is the focus of this article. Several safety concerns, such as public safety, emergency management, and disaster preparedness, can be monitored and addressed with the help of the Internet of Things (IoT) framework's network of interconnected devices, sensors, and data analytics. In order to improve situational awareness, reaction times, and overall safety results, this research dives into important Internet of Things (IoT) applications such smart sensors, real-time analytics, and surveillance systems. In addition to looking at potential energy savings, the study analyses the pros and cons of using an Internet of Things (IoT) safety infrastructure in smart cities.