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... contrast with all disscussed challenges, Figure 2 presents the different IoT data management challenges, which are represented horizontally whereas; vertical lines represents the number of data management models found in the literature. While going through the literature it was observed that there was less research on most of the data management challenges such as data aggregation, data analysis and data storage. ...
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Citations
... The conventional structure of DSS typically comprises three core components: data management, model management, and user interface [18]. The data management layer is responsible for data storage, retrieval, and processing, ensuring that relevant information is available for analysis [19]. Model management involves the application of mathematical and statistical models to interpret data and generate insights. ...
Artificial Intelligence (AI) transforms business intelligence (BI) by enhancing decision-making speed, accuracy, and depth in today's data-driven landscape. Traditional Decision Support Systems (DSS), once foundational to BI, struggle to handle modern data's complexity, scale, and diversity, often resulting in limited decision-making agility. Integrating AI into DSS has become essential to bridge this gap, enabling these systems to process vast datasets in real-time and make predictive, data-informed recommendations. This study presents an AI-powered DSS framework designed to address the limitations of conventional DSS by incorporating machine learning, natural language processing, and adaptive feedback mechanisms. Through real-world simulations and industry-specific use cases, the framework demonstrates marked improvements in decision quality, response times, and user satisfaction compared to traditional systems. Findings suggest that AI-driven DSS can substantially enhance BI processes, equipping organizations with a proactive, scalable approach to decision support. By addressing key technical and ethical challenges, this research offers valuable insights for businesses aiming to leverage AI to stay competitive in increasingly complex environments, positioning AI-powered DSS as critical to the future of BI.
... • Middle layer: This layer uses technology to consolidate and standardise IoT data while providing screening, processing, an element of security management, resource location and error resolution (Abbasi et al., 2017, Fan & Chen, 2010. All of this is made possible by the deployment of fog computing and cloud computing (Morar et al., 2021): Cloud computing is centralised data processing, filtering and modelling of data in a cloud server (Rahman & Hussain, 2019). ...
Purpose: This study aimed to develop a comprehensive framework to enable the identification of risks pertaining to data security, privacy and confidentiality when using medical Internet of Things (IoT) devices.Design/methodology/approach: A qualitative, non-empirical study was undertaken to identify data-related risks when using medical IoT devices using a systematic literature review and two governance frameworks.Findings/results: Within the medical field, risks of using IoT are concentrated around data security, privacy and confidentiality throughout the data lifecycle prevalent within each layer of the IoT architecture. A comprehensive framework was developed to identify these risks at each layer within the architecture in order to facilitate sound information technology (IT) and data governance.Practical implications: This research documents evidence of the risks posed by IoT devices within the medical field particularly pertaining to IoT data. It provides those charged with governance with a tool to identify all significant risks in this field that is compliant with Health Insurance Portability and Accountability Act and Control Objectives for Information and related Technology 2019.Originality/value: This research provides a comprehensive framework that can be used by those in charge of governance including IT specialist for risk identification during implementation for sound IT and data governance of medical IoT devices using recognised benchmarks. The use of the benchmarks ensures that all significant risks are identified, compared to previous research that identified risks in an ad hoc manner.
... This portal is accessible through various devices, such as mobile laptops, tablets, and personal computers, typically via Android applications. A possible example would be a smartphone application created to notify people on their phones every time a patient's condition is identified [15]. Many academics are quite interested in using cloud-based IoT technology for health monitoring, especially when it comes to security and intelligence [16][17][18][19]. ...
The growing use of Internet of Things (IoT) technologies in a variety of sectors, including healthcare, has opened up new possibilities for gathering and analyzing patient data. In some cases, the elderly are exposed to significant risk and even mortality as a result of the global aging problem, which has become a burden in recent years. Numerous IoT devices are being created to monitor, track, and record the actions of the elderly to reduce these hazards. This study proposed a novel, dependable, cloud-based remote system patient monitoring framework for IoT health detection. The main distinguished part of this research is that we rarely can find a framework in the literature that is based on real-time systems by considering heartbeat (BPM), blood oxygen (SpO2), and body temperature at the same time. The implementation and testing of this real-time system is classified into six distinctly separated phases for developing both the hardware and software. To verify the performance of the suggested system, data were gathered from BOT-IoT datasets. The outcome enhances patient satisfaction, secure data transmission, and healthcare outcomes by showing that the proposed framework is more efficient than other compared protocols in terms of the decision time, which is 16.3 seconds for 46 features, with 100% accuracy.
... Any IoT system deployment deals with the exchanges of enormous data between diverse platforms managed by different organisations. One of the primary areas of research in the near future will revolve around finding effective strategies to handle the vast amount of data generated by objects, exceeding that produced by the human population (Asad 2017). Improper management of users' data can have a negative effect on service delivery and negatively impact the reputation of the project as a whole. ...
... Abbasi et al. 29 presented a proposal for data management in IoT environments. The purpose of the article is conceptual, and defined layers are implemented in our work. ...
The Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. Hence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that can process, store, and promote access to large amounts of data. The data collected from sensors in typical IoT systems is stored and processed in cloud servers. However, some IoT solutions use edge devices to perform specific actions, such as processing, storage, and access, using only local infrastructure for low latency requirements. Fog computing has been used to improve IoT solutions and to transfer some of the complexity from the cloud to the edge of the network, that is, closer to devices, applications and users, working as a kind of “local and private cloud.” The cooperation of devices and applications between edge and cloud creates a need for an interplay to enable data flow among the layers of IoT systems deployed on edge and in the cloud. Thus, IoT data systems should support the data life cycle through its collection, analysis, and use. Performance efficiency is a quality factor of systems and software engineering, which measures “performance relative to the number of resources used under stated conditions.” In particular, in IoT systems that involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This work proposes a data interplay model of IoT to define and deploy the IoT data life cycle in the collection, analytics, and data use stages. This data interplay proposal aims to improve performance efficiency in IoT data life cycle operations: collection, analytics, and use among devices and applications in edge and cloud infrastructures.
... In the next few years, research will largely revolve around how to store information related to human issues with respect to goals using IoT. Now, we are in dire need of infrastructure to collect, store, and manage data by IoT (Fan and Chen 2010;Bohli et al. 2015;Abbasi et al. 2017). Organizations, mining companies, and related institutions should provide the essential conditions for developing this infrastructure. ...
... To optimally use the data of the IoT system, the determined goals should be self-descriptive, and system should have the potential for sharing its information dynamically. The availability of the IoT system seems to be essential for the compatibility of goals and continuous production in the mining industry (Abbasi et al. 2017). ...
Sustainable Development (SD) has played an important role in all industries and mining activities. The Internet of Things (IoT), interacting with industrial machines or home appliances through an interconnected network, can assist in developing real-time platforms for monitoring and operating a complex production system with minimal intervention of humans, which made it possible to benefit from the data obtained to increase production rate, Net Present Value optimization, workforce and machine safety, and decreasing pollution. Ideal automated mining can be achieved by designing an IoT based framework and suitable SD assessment. This research aims to achieve a holistic understanding of the current IoT scenario by conducting a comparative analysis of the prevailing literature regarding IoT applications in the mining and SD area. The results showed that, in the economic index of sustainability, mining activity based on IoT can increase productivity, wealth, income, and GDP and decrease operating expenses, depreciation costs, and total cost. From environmental point of view, a significant reduction in emission and pollution can be obtained. Also, the increase in workforce safety and positive impact on culture and knowledge are the components of the social index of sustainability. These activities are followed by some significant challenges such as developing infrastructures, modern machinery, adequate security for data transmission, increase business in the area, creating new jobs for individuals, especially those who have lost their jobs. It’s essential to devise new rules to protect local people, properly distribute the wealth gained from this development to improve the infrastructure of the mining area.
... Conventionally managing a network, involves the use of a set of management protocols that facilitate the sharing of data between users and networks of all kinds [111]. Due to the wide range of networked systems found on the Internet today, controlled network modules can have diverse characteristics in terms of storage, processing capacities, and energy usage [112]. IoT network management should be able to provide functionalities, among other capabilities, such as to monitor network status, detect faults, configure operating parameters, collect network performance information, and manage its operation [113]. ...
This paper is an SLR about SDN-based IoT management frameworks.
... Conventionally managing a network, involves the use of a set of management protocols that facilitate the sharing of data between users and networks of all kinds [113]. Due to the wide range of networked systems found on the Internet today, controlled network modules can have diverse characteristics in terms of storage, processing capacities, and energy usage [114]. IoT network management should be able to provide functionalities, among other capabilities, such as to monitor network status, detect faults, configure operating parameters, collect network performance information, and manage its operation [115]. ...
Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.
... The IoT requires methods for data processing and analysis. The IoT data management system is indispensable [2][3][4] to achieve effective performance. Systems in IoT networks cannot have a large amount of storage or processing power. ...
The Internet of Things (IoT) is expected to connect devices with unique identifiers over a network to create an equilibrium system with high speeds and volumes of data while presenting an interoperability challenge. The IoT data management system is indispensable for attaining effective and efficient performance because IoT sensors generate and collect large amounts of data used to express large data sets. IoT data management has been analyzed from various perspectives in numerous studies. In this study, a Systematic Literature Review (SLR) method was used to investigate the various topics and key areas that have recently emerged in IoT data management. This study aims to classify and evaluate studies published between 2015 and 2021 in IoT data management. Therefore, the classification of studies includes five categories, data processing, data smartness application, data collection, data security, and data storage. Then, studies in each field are compared based on the proposed classification. Each study investigates novel findings, simulation/implementation, data set, application domain, experimental results, advantages, and disadvantages. In addition, the criteria for evaluating selected articles for each domain of IoT data management are examined. Big data accounts for the highest percentage of data processing fields in IoT data management, at 34%. In addition, fast data processing, distributed data, artificial intelligence data with 22%, and data uncertainty analysis account for 11% of the data processing field. Finally, studies highlight the challenges of IoT data management and its future directions.
... Data management is a top concern in organizations and having high-quality corporate information data is more important than ever to fulfill the company's strategic goals. The storage model, query languages, managing linked data, distribution methods, and schema evolution of traditional and contemporary databases for geographic Big Data management are assessed [3]. The storage model, query languages, managing linked data, distribution methods, and schema evolution of traditional and contemporary databases for geographic Big Data management are assessed. ...
... The capacity to handle variable-length text string data and arrange it in tree-structured files, the availability of this data in a multi-user environment and the presence of a high-level language for programming and system development are all criteria for the creation of a data management system. [3] Despite its relevance, the field of data governance is largely understudied. Corporations confront new and more complicated difficulties due to the rise of Cloud computing and its growing acceptance by businesses, public organizations, and governments and the potential benefits of embracing the technology. ...
... Storage, pre-processing, processing, and security are all viable approaches for managing large data. Furthermore, the important features of these approaches are evaluated by creating a taxonomy to find issues and solutions [3]. The adoption of good data governance, according to forward-thinking businesses, is the only approach to tackle the data problem [4]. ...
This paper presents a framework for modeling and implementation of collaborative data management. The framework ensures information sharing and parallel process development. Building data infrastructure that you can trust necessitates adhering to a sound data management methodology. Reliable data comes from data infrastructures built on solid management concepts. Data analysis on the performance of these systems for integration and the motivation of implementing a data management framework. To acquire and preserve essential information such as conceptual metadata, the underlying culture that makes such a plan effective involves a partnership between business and IT, responsibility among data owners and stewards, and cooperation and collaboration throughout the data management process in an organization. This paper identifies the most appropriate data strategic approach and examines the current data management solutions.
