BIG DATA ANALYTICS BASED ON THE INTERNET OF THINGS AND CLOUD COMPUTING INTEGRATED FOR EVOLVING ENVIRONMENTAL DATA MANAGEMENT

Internet of Things (IoT) is nowadays increasingly becoming a worldwide network of interconnected devices uniquely addressable, via a standard communication protocol. Such devices generate a massive volume of heterogeneous data, which lead a system towards a major computational challenges, such as aggregation, storing, and processing. Also, a major problem arises when there is a need to extract useful information from this massive volume of data. Therefore, to address these needs, this paper proposes an architecture to analyze big data in the IoT. The basic concept involves the partitioning of dynamic data, i.e., big data with the complex magnitude is divided into subsets. These subsets are based on the theoretical model of data fusion, which works in the Hadoop processing server to enhance the computational efficiency. The proposed architecture is tested by analyzing healthcare data sets, mainly comprises of activities including walking, running, ECG. The feasibility and efficiency of the proposed architecture are implemented on Hadoop single node setup on UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed architecture efficiently analyze the massive volume of data with a maximum throughput.

The rapid urbanization and technological advancement have catalyzed the emergence of smart cities as a transformative paradigm for sustainable urban development. This paper presents a comprehensive framework for modeling smart cities through the systematic integration of Internet of Things (IoT), big data, and analytics technologies. We propose a multi-layered architectural model that addresses the technical, operational, and governance challenges inherent in smart city implementations. The research examines how IoT sensors and devices generate massive volumes of heterogeneous data, which are subsequently processed through big data platforms to extract actionable insights via advanced analytics techniques. Our framework encompasses data acquisition, storage, processing, and visualization layers, while incorporating machine learning algorithms and real-time analytics for intelligent decision-making. Through case studies of various smart city domains including transportation, energy management, public safety, and healthcare, we demonstrate the practical applicability of our integrated model. The paper also addresses critical challenges such as data privacy, security, interoperability, and scalability that must be overcome for successful smart city deployment. Our findings reveal that effective integration of these three technological pillars enables cities to optimize resource allocation, enhance service delivery, improve quality of life for citizens, and achieve sustainability goals. The proposed model provides urban planners, policymakers, and technology implementers with a structured approach to design and deploy smart city solutions that are both technologically robust and contextually relevant.

Cloud computing paradigm builds on the foundations of distributed computing, grid computing, virtualisation, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, and functionality. Therefore, Cloud-based platforms have become fundamental to collaborative and data management systems. However, the new Cloud environment creates major collaborative and data management challenges such as efficient and reliable remote storage, online data query processing, data integrity, outsourcing computation, and secure virtualisation, among others. Such issues need to be carefully studied and solved to enable the wide deployment and adoption of Cloud computing in businesses, industry, etc. The purpose of this special issue is to publish recent advances in Cloud-based collaborative systems and Cloud storage. The special issue comprises five high-quality papers, which are arranged as follows. De Maio et al in the first paper1 “Social Media Marketing through Time-Aware Collaborative Filtering,” define a time-aware collaborative filtering for estimating users' interest along the time in Twitter. Their approach uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. The authors present experimental results comparing these techniques and performing model training in different time windows. The second paper2 “SERNOTATE: An Automated Approach for Business Service Description Annotation for efficient Service Retrieval and Composition” by Chotipant et al proposes an automated approach, namely, SERNOTATE, for business service description annotation for efficient service retrieval and composition. To that end, the authors employ new semantic-based linking approaches, namely, Extended Case-based Reasoning, Vector-based and Classification-based, that automatically annotate business services to relevant service concepts. The experimental results test and validate the applicability of the proposed approaches to the automatic annotation of business service descriptions to service concepts on a real-world dataset. In the third paper,3 “Sensor Data Management in the Cloud: Data Storage, Data Ingestion, and Data Retrieval,” the authors address an increasing need to capture, store, and analyse the dynamic semistructured data from sensors. A similar growth of semistructured data in the modern Web has led to the creation of NoSQL data stores for scalability, availability, and performance, whereas large-scale data processing frameworks for parallel analysis. The authors study how sensor data management can benefit from MongoDB with Apache Spark technologies, specifically for ingesting high-velocity sensor data and parallel retrieval of high volume data. The performance of MongoDB sharding and no-sharding databases with Apache Spark are evaluated to identify the right software environment for sensor data management. Park et al in the fourth paper,4 “Study on the SDN-IP based Solution of Well-known Bottleneck Problems in Private Sector of National R&E Network for Big Data Transfer,” deal with issues arising in Software Defined Networking (SDN). The key feature of SDN networks is the provisioning of dynamic service architecture. Therefore, it is more appropriate for the big data science computing trends, for handling today's big data science, which requires massive parallel processing and a constant demand for additional capacity and on demand connectivity. The authors bring the experience from KREONET (Korea Research Environment Open Network) and Korea National R&E Network, which adopt SDN as a platform service for big data scientific application. The big data application for KREONET SDN service comprises high-energy physics, astrophysics, biology/genetics, meteorology, artificial satellite data, etc. The last paper5 “Improved Outsourced Private Set Intersection Protocol Based on Polynomial Interpolation” by Yang et al investigates issues in private set intersection (PSI) protocols, which enable two parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. The authors present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption as well as an efficient and secure outsourcing computation algorithm for RSA crypto-system. Based on this algorithm, the authors transform a variant of delegated private set intersection protocol into an improved outsourced one, which enables the clients to perform only simple modular multiplication for computing during the execution of the protocol. Compared with the state of the art, the proposed protocol has a great advantage in efficiency. The editor of this special issue would like to thank all authors for their contributions and the reviewers for their constructive and useful feedback to authors. I would like to thank Prof Geoffrey Fox, the Editor-in-Chief of Concurrency and Computation: Practice and Experience, for the opportunity to edit this special issue and his support.

In-Mapper combiner based MapReduce algorithm for processing of big climate data

Big data refers to a massive volume of data collected from heterogeneous data sources including data collected from Internet of Things (IoT) devices. Big data analytics is playing a crucial role in extracting patterns that would benefit efficient and effective decision making. Processing this massive volume of data poses several critical issues such as scalability, security and privacy. To preserve data privacy, numerous privacy-preserving data mining and publishing techniques exist. Data anonymization utilizing data mining techniques for preserving an individual’s privacy is a promising approach to prevent the data against identity disclosure. In this paper, a Parallel Clustering based Anonymization Algorithm (PCAA) is proposed, and the results prove that the algorithm is scalable and also achieves a better tradeoff between privacy and utility. The MapReduce framework is used to parallelize the anonymization process for handling a huge volume of data. The algorithm performs well in terms of classification accuracy, F-measure, and Kullback–Leibler divergence metrics. Moreover, the big data generated from heterogeneous data sources are efficiently protected to meet the ever-growing requirements of the application.

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

As we delve into the Internet of Things (IoT), we are witnessing the intensive interaction and heterogeneous communication among different devices over the Internet. Consequently, these devices generate a massive volume of Big Data. The potential of these data has been analyzed by the complex network theory, describing a specialized branch, known as 'Human Dynamics.' The potential of these data has been analyzed by the complex network theory, describing a specialized branch, known as 'Human Dynamics.' In this extension, the goal is to describe human behavior in the social area at real-time. These objectives are starting to be practicable through the quantity of data provided by smartphones, social network, and smart cities. These make the environment more intelligent and offer an intelligent space to sense our activities or actions, and the evolution of the ecosystem. To address the aforementioned needs, this paper presents the concept of 'defining human behavior' using Big Data in SIoT by proposing system architecture that processes and analyzes big data in real-time. The proposed architecture consists of three operational domains, i.e., object, SIoT server, application domain. Data from object domain is aggregated at SIoT server domain, where the data is efficiently store and process and intelligently respond to the outer stimuli. The proposed system architecture focuses on the analysis the ecosystem provided by Smart Cities, wearable devices (e.g., body area network) and Big Data to determine the human behaviors as well as human dynamics. Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS coreTMi5 machine with 3.2 GHz processor and 4 GB memory.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

Recently, two terms, namely Big Data and Internet of Things (IoT) have gained popularity individually. However, their interconnections are not fully explored and understood. It is expected that the fusion of Big Data and IoT would create many complex systems for smart cities. While the data from IoT lies in Big Data, the scale of operations are completely different in terms of providing the required real-time analytics for such smart systems. Even though NoSQL databases and other next generation solutions could be deployed to achieve real-time responses, the major security challenges need to be understood as mission critical and sensitive data intertwines Big Data and IoT. In this paper, we identify the security challenges shared by the closely-knit Big Data and IoT in three main risk areas: i) NoSQL security vulnerabilities, ii) mobile IoT (M-IoT) security and privacy constraints and iii) encryption key security threats. We perform a comparative study of security vulnerabilities of NoSQL databases and identify the security and privacy constraints of M-IoT networks. Encryption key attacks for resource constrained IoT devices are also illustrated mathematically. Overall, this paper explores new research directions in these prime areas of security and privacy that would result in solution opportunities for a meaningful fusion of Big Data and IoT for a smart environment.

IoT data analytics in dynamic environments: From an automated machine learning perspective

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

One of the main aspects of this study was the use of Big Data tools to analyze and effectively use the data generated by Internet of Things (IoT) devices. Big data allows you to store and analyze a huge amount of diverse data, as well as to obtain valuable information from it. With the help of Big Data tools, it is possible to process this data efficiently using parallel algorithms and analysis methods in real or quasi-real time. The purpose of the article is to analyze the latest relevant research in the field of Big Data and the Internet of Things, as well as to reveal the features of their interdependence. To achieve this goal, the method of systematic review of scientific literature was used, which allowed to systematically trace and analyze significant research and publications in this area. The methodology of this research is based on a retrospective analysis of literature sources and scientific publications related to the use of Big Data and the Internet of Things in various fields. To collect data, a systematic search of electronic databases such as IEEE Xplore, ScienceDirect, and searches on web resources such as Google Scholar were used. The analysis identified key concepts, methods, and practical applications of combining Big Data and the Internet of Things. The latest developments in the Internet of Things architecture and Big Data models were analyzed. The practical significance of the study is that it contributes to a deeper understanding of the latest research in the areas of Big Data and the Internet of Things, the interaction between them and the identification of new opportunities for using this combination in various industries. The results of this research can be used to develop new technologies and improve existing systems for collecting, processing, and analyzing data. In addition, these findings can be used as a basis for further research in this area aimed at improving and implementing new solutions. This study helps to deepen the understanding of the potential of combining Big Data and the Internet of Things and to point out ways for further development in this area

Among the most revolutionary technologies are big data analytics, artificial intelligence (AI) and robotics, machine learning (ML), cybersecurity, blockchain technology, and cloud computing. The project was focused on how to create a Big Data Analytics Architecture Framework to increase the production capability and global trade for Sub-Saharan Africa’s oilseeds and textile industries (SSA). The infrastructure, e-commerce, and disruptive technologies in the oilseeds and textile industries, as well as global e-commerce, all demand large investments. The pragmatic paradigm served as the foundation for the research approach. This study employed a review of the literature, document analysis, and focus groups. For the oilseeds and textile sectors in SSA, a Big Data analytics architectural framework was created. The Hadoop platform was created as a framework for big data analytics. The open-source Hadoop platform offers the analytical tools and computing capacity needed to handle such massive data volumes. It supports E-commerce and is based on the Hadoop platform, which offers the analytical tools and computing power needed to handle such massive data volumes. The low rate of return on investments made in breeding, seed production, processing, and marketing limits the competitiveness of the oil crop or legume seed markets.

The increasing world population and rapid industrial development is driving the need for sustainable management solutions and preservation of the natural resources and the ecosystem. The Internet of Things (IoT) and big data (BD) analytics are expected to play key role toward sustainability in areas such as water, agriculture, energy, transportation and smart city. In this chapter, the focus is sustainability in water. To address the issues of sustainability of clean accessible water for human use and other living things, a water ecosystem is presented. The water ecosystem consists of five major elements which are water source, treatment, reservoir, consumption and wastage. There are several issues faced in sustainable water supply such as decreasing fresh water resources, loss of revenue due to water loss, complexity in managing the increasing demands. Existing water-management models are not efficient in addressing these issues. In addition, due to seasonal variations, changes in environmental laws, varying plant-operating conditions and other factors, there is a need for effective and efficient monitoring. The application of IoT and BD analytics technology is promising and can provide sustainable management solution in water. The practical deployment of IoT and BD analytics consists of four major components which are IoT devices, communication technology, internet and BD. The emerging low power wide area (LPWA) communication technology is expected to enhance massive connectivity required for water monitoring, data acquisition and promote sustainability in water. Hence, in this chapter, the features of the water ecosystem, the system architecture of IoT and BD and the application in water sustainability, challenges such as cyber security, policy and regulations, accuracy of the data and technology interoperability are discussed.

The new generation of information and communication technologies represented by the Internet of Things, big data, and cloud computing are developing rapidly. Through continuous integration with other emerging technologies, the Internet of Things technology accelerates its penetration into the fields of smart medicine, new energy, and materials. In this article, we will explore the airport peripheral environment and civil airport fire emergency management. In airport emergency management, bird strike and fire management are the most frequent problems. Bird strikes most often occur in aircraft take-off, taxi, and landing areas. Therefore, the study of airport environmental characteristics is particularly important. In order to improve the emergency support capability of the civil airport fire department and the level of airport operation support, it is necessary to clarify the factors that affect the emergency support capability of the airport fire department. This article is based on the research of the airport surrounding environment of Big Data Internet of Things and applies it to the research of civil airport fire emergency management, which improves the emergency ability of airport firefighters.