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The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of Science Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references. [ABSTRACT FROM AUTHOR]

Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of the Internet of Things (IoT) have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in conducting high-quality data analytics within the realm of big data. To uncover big data challenges and enhance data quality, it is essential to quantitatively unveil data characteristics. Furthermore, the existing studies lack analysis of the specific time-related characteristics of physical-sensor data. Enhancing the efficiency and precision of data analytics through the big data lifecycle requires a comprehensive understanding of data characteristics to address the hidden big data challenges. To fill in the research gap, this paper proposes a systematic data characteristic framework based on a 6Vs model. The framework aims to unveil the data characteristics in terms of data volume, variety, velocity, veracity, value, and variability through a set of statistical indicators. This model improves the objectivity of data characteristic understanding by relying solely on data-driven indicators. The indicators related to time-related characteristics in physical-sensor data are also included for the analysis of temporal aspects in the physical-sensor data. Furthermore, the big data challenges are linked to each dimension of the 6Vs model to gain a quantitative understanding of the data challenges. Finally, a pipeline is developed to implement the proposed framework, and two case studies are conducted to illustrate the process of understanding the physical-sensor data characteristics and making recommendations for data preprocessing to address the big data challenges. The proposed framework is able to analyze the characteristics of all physical-sensor data, therefore, identifying potential challenges in subsequent analytics, and providing recommendations for data preprocessing. Furthermore, the characteristic indicators can be used to analyze other types of big data. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

The enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of Things (IoT) system based on a robust big data architecture. This innovative approach, combined with the power of Spark algorithms, has been proven to uncover rules representing hidden connections and patterns in the data extracted from a building in Bucharest. These uncovered patterns were essential for improving the building's energy efficiency. [ABSTRACT FROM AUTHOR]

Precision agriculture in the realm of the Internet of Things is characterized by the collection of data from multiple sensors deployed on the farm. These data present a spatial, temporal, and semantic characterization, which further complicates the performance in the management and implementation of models and repositories. In turn, the lack of standards is reflected in insufficient interoperability between management solutions and other non-native services in the framework. In this paper, an innovative system for spatio-temporal semantic data management is proposed. It includes a data query system that allows farmers and users to solve queries daily, as well as feed decision-making, monitoring, and task automation solutions. In the proposal, a solution is provided to ensure service interoperability and is validated against two European smart farming platforms, namely AFarCloud and DEMETER. For the evaluation and validation of the proposed framework, a neural network is implemented, fed through STSDaMaS for training and validation, to provide accurate forecasts for the harvest and baling of forage legume crops for livestock feeding. As a result of the evaluation for the training and execution of neural networks, high performance on complex spatio-temporal semantic queries is exposed. The paper concludes with a distributed framework for managing complex spatio-temporal semantic data by offering service interoperability through data integration to external agricultural data models. [ABSTRACT FROM AUTHOR]

In nursing homes using technologies such as IoT, big data, cloud computing, and machine learning, there is a constant risk of attacks such as Brute Force FTP, Brute Force SSH, Web Attack, Infiltration, and Botnet during data communication between individual terminals and the cloud server. Therefore, effectively identifying network communication data is essential to protect data communication security between individual terminals and the cloud server. Aiming at the data mentioned above regarding communication security issues, we propose an intelligent intrusion detection model NIDD (Network Intelligent Data Detection) model that combines deep convolution generation adversarial network (DCGAN) with Light Gradient Boosting Machine (LightGBM) and Shapley Additive exPlanations (SHAP). The NIDD model first generates new attack samples by learning the feature distribution of the existing attack sample data and effectively expands the rare attack samples. Secondly, we use the Light Gradient Boosting Machine (LightGBM) algorithm as the base classifier to train the dataset and start to build the intrusion detection model. Then use Shapley Additive exPlanations (SHAP) to analyze the contribution of the classification results, and adjust the model parameters according to the analysis results. Finally, we obtain the optimal model for the intelligent detection model of network intrusion. This paper conducts experimental tests on the NSL-KDD dataset. The experimental results show that the NIDD model built based on Light Gradient Boosting Machine can detect Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet, PROBE, R2L, and U2R attacks with an accuracy of 99.76%. Finally, we re-verified the NIDD model on the CIC-IDC-2018 dataset. The results once again proved that the NIDD model could solve the data communication security between the nursing robot and the cloud server and the data before the IoT terminal and the cloud server. Communication security provides a sufficient guarantee. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm that offers remarkable opportunities for data mining and analysis. IoT envisions a world where all smartphones, vehicles, public services facilities, and home appliances that can be connected to the internet act as data sources. Even today, a significant portion of electronic devices, including watches, emergency alarms, parking doors, and many appliances can be linked to IoT systems and remotely controlled. Big data analysis and data mining methods can be utilized to improve the performance of IoT systems and address their challenges in the area of data storage, processing, and analysis. Extensive studies on IoT with big data can make it possible to accumulate tremendous data and transform it into valuable knowledge using data mining techniques. With this background, this paper provides a systematic survey of the literature on the use of big data analytics and data mining methods in IoT. This review aims to identify the lines of research that should receive more attention in future works. To achieve this goal, the articles published between 2010 and 2021 on the subjects of IoT-based big data and IoT-based data mining (60 articles) have been reviewed. These articles fall into four general categories in terms of focus: architecture/platform, framework, applications, and security. The paper provides a summary of the methods used in IoT-based big data analysis and IoT-based data mining in these four categories to highlight the promising avenues of research for future works. [ABSTRACT FROM AUTHOR]

There is a growing demand for intensive care units, but there is a relative shortage of medical staff. Intensive care work is heavy and stressful. Optimizing the working conditions and processes of the intensive care unit is of great significance for improving the work efficiency and the level of diagnosis and treatment in the intensive care unit. The intelligent intensive care unit is a new ward management model gradually developed on the basis of modern science and technology such as communication technology, internet of things, artificial intelligence, robots, and big data. Under this model, the potential risks caused by human factors are greatly reduced, and the monitoring and treatment of patients has been significantly improved. This paper reviews the progress in related fields. [ABSTRACT FROM AUTHOR]

The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time. [ABSTRACT FROM AUTHOR]

Standard regression techniques model only the mean of the response variable. Quantile regression (QR) is more powerful in that it depicts a comprehensive relationship between the response variable and independent covariates at different quantiles. It is particularly useful for non-normally distributed data with skewness or heterogeneity, which appear routinely in many scientific fields, such as economics, finance, public health and biology. Although its theory has been well developed in the literature, its computation in big data still faces multiple challenges, especially for vertically stored big data in modern distributed environments, where communication efficiency and security are usually the primary considerations. While the popular alternating direction method of multipliers (ADMM) provides a general computational solution, its slow convergence becomes a bottleneck when communication cost dominates local computational consumption, such as Internet of Things (IoT) networks. Motivated by the residual projection technique, in this paper we propose an innovative iterative parallel framework, PIQR, that converges faster and has a more secure data transmission plan, and establish its convergence property. This framework is further extended to composite quantile regression (CQR), a modified QR technique that improves estimation efficiency at extreme quantiles. Simulation studies show that both the ADMM-based method and the PIQR enjoy favorable estimation accuracy in distributed environments. While PIQR is inferior to the ADMM-based method at local computation, it requires much fewer iterations to achieve convergence, and hence significantly improves the overall computational efficiency when communication cost is the dominating factor. Moreover, PIQR transmits only data involving the residual information between different machines, and can better prevent the leakage of important data information compared with the ADMM-based method. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), Big Data and Machine Learning (ML) may represent the foundations for implementing the concept of intelligent production, smart products, services, and predictive maintenance (PdM). The majority of the state-of-the-art ML approaches for PdM use different condition monitoring data (e.g. vibrations, currents, temperature, etc.) and run to failure data for predicting the Remaining Useful Lifetime of components. However, the annotation of the component wear is not always easily identifiable, thus leading to the open issue of obtaining quality labeled data and interpreting it. This paper aims to introduce and test a Decision Support System (DSS) for solving a PdM task by overcoming the above-mentioned challenge while focusing on a real industrial use case, which includes advanced processing and measuring machines. In particular, the proposed DSS is comprised of the following cornerstones: data collection, feature extraction, predictive model, cloud storage, and data analysis. Differently from the related literature, our novel approach is based on a feature extraction strategy and ML prediction model powered by specific topics collected on the lower and upper levels of the production system. Compared with respect to other state-of-the-art ML models, the experimental results demonstrated how our approach is the best trade-off between predictive performance (MAE: 0.089, MSE: 0.018, R 2 : 0.868 ), computation effort (average latency of 2.353 s for learning from 400 new samples), and interpretability for the prediction of processing quality. These peculiarities, together with the integration of our ML approach into the proposed cloud-based architecture, allow the optimization of the machining quality processes by directly supporting the maintainer/operator. These advantages may impact to the optimization of maintenance schedules and to get real-time warnings about operational risks by enabling manufacturers to reduce service costs by maximizing uptime and improving productivity. [ABSTRACT FROM AUTHOR]

Recently, Internet of things (IoT) devices have been widely implemented and technologically advanced in manufacturing settings to monitor, collect, exchange, analyze, and deliver data. However, this transition has increased the risk of cyber-attacks, exponentially. Subsequently, developing effective intrusion detection systems based on deep learning algorithms has proven to become a reliable intelligence tool to protect Industrial IoT devices against cyber threats. This paper presents the implementation of two different classifications and detection utilizing the long short-term memory (LSTM) architecture to address cybersecurity concerns on three benchmark industrial IoT datasets (BoT-IoT, UNSW-NB15, and TON-IoT) which take advantage of various deep learning algorithms. An overall analysis of the performance of the proposed models is provided. Augmenting the LSTM with convolutional neural network (CNN) and fully convolutional neural network (FCN) achieves state-of-the-art performance in detecting cybersecurity threats. [ABSTRACT FROM AUTHOR]

Recently, the new generation of information technology as cloud computing, big data, IoT, and AI has led to profound changes in manufacturing. Many new technologies and ideas have emerged in the field of system development, manufacturing process, industrial form, and business model, such as cloud manufacturing (CM). The primary feature of CM is to realize the integration and widely sharing of manufacturing services. Therefore, optimization of manufacturing services is vital for a CM system. Based on wide investigation, this paper summarizes the research progress of CM service optimization in recent years from the aspects of optimization strategy, optimization model, and optimization algorithm. And problems and development trends such as the research of task decomposition, service discovery and optimization integration, algorithm with big data, and optimized object are proposed. [ABSTRACT FROM AUTHOR]

The explosive growth in the number of enterprises of Internet of Multimedia Things stocks reflects the economic level of an area. Internet of Multimedia Things has emerged as a new research concept in the world, in which a new data mining algorithm is proposed to study enterprise operation ability of Internet of Multimedia Things. Firstly, an image processing method based on wavelet transform is proposed. We do a big data analysis of the stock's historical data to obtain its plate leading stock average data and volatility of individual stocks data image. The number of the singular value points of the individual stocks was obtained according to their fluctuation, and we could get the singular rate from the numbers of singular values points. Stock's stability could be judged comprehensively by comparison between the trend of fluctuations in individual stocks and leading stock, singular value point, and singular rate. The results of this paper could help investors to grasp the volatility of the stock market, making predictive analysis, and getting a higher return. The experimental results show that our approach has better ability to mine the essences in Internet of Multimedia Things. Data mining also can get the development level of Internet of things enterprises. [ABSTRACT FROM AUTHOR]

Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domain-specific graphical tools. The approach follows the flow-based programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flow-based programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises early-stage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate code-abstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications. [ABSTRACT FROM AUTHOR]

In the current era of the Internet of Things (IoT), massive number of sensors are used in our daily lives. Sensors are everywhere around us. They exist in our homes, work places, streets, cars, and even ourselves. Examples include home appliances, wearable devices, and medical sensors. These sensors generate huge amount of dynamic, heterogeneous, and unstructured data that need special handling beyond the capabilities of conventional relational databases. Thus, identification of suitable data management platform to store and query this data is necessary. Despite of its popularity and efficiency in processing various types of big data, there is no single-guided study of how NoSQL data stores will behave with the Internet of Things (IoT) datasets. IoT data have its own characteristics that make it special. IoT data come from various sensors, with a wide range of formats, high velocity, and require high throughput processing with low latency. NoSQL data stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data store performs the best from the two scalability aspects (scale-up and scale-out) in a distributed and parallel processing environment. This paper benchmarks the commonly used NoSQL data stores (MongoDB, Cassandra, and HBase), and compares their performance with real industrial IoT dataset. In addition, we focus on comparing the throughput, latency, and run time of the evaluated NoSQL data stores. [ABSTRACT FROM AUTHOR]

The Internet of Thing (IoT) provides a wide range of services for human convenience by linking the connectivity from different platform devices. The IoT service holds an unlimited potentials, because the knowledge extracted from the big data can be applied to a huge variety service areas such as smart homes, smart buildings and transportations as well. There are lots of IoT service platforms developed by several companies. Except some platforms, most of them have been only focused on the connectivity. As a result, they have some limits for supporting an intelligent service. For the reason, we design the intelligent IoT common service platform architecture and implement the basic operations in this paper. We also develop the IoT Broker for providing the connectivity as well. We hereby verify the usefulness of the proposed service platform by implementing the IoT service on the platform. [ABSTRACT FROM AUTHOR]

The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS). [ABSTRACT FROM AUTHOR]

Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are mainly characterized by volume, velocity, variety and veracity issues. Big data-related issues strongly challenge traditional data management and analysis systems. The concept of data lake was introduced to address them. A data lake is a large, raw data repository that stores and manages all company data bearing any format. However, the data lake concept remains ambiguous or fuzzy for many researchers and practitioners, who often confuse it with the Hadoop technology. Thus, we provide in this paper a comprehensive state of the art of the different approaches to data lake design. We particularly focus on data lake architectures and metadata management, which are key issues in successful data lakes. We also discuss the pros and cons of data lakes and their design alternatives. [ABSTRACT FROM AUTHOR]

In the present era, secure data storage for any Internet of Things (IoT) platform is plagued by poor performance of secure read and write operations, which limits the use of data storage security on any IoT platform. Therefore, in this paper, a data storage security method based on double secret key encryption and Hadoop suitable for any IoT platform is proposed. First, the Hadoop deep learning architecture and implementation process are analyzed, and the process of client Kerberos identity authentication in the Hadoop framework is discussed. From this, the current shortcomings of data storage security based on the Hadoop framework are analyzed. The elements of data storage security are also determined. Furthermore, a novel double secret key encryption method for data storage security and to improve the security of stored data itself is introduced. Simultaneously, hash computing is used to improve the read and write performance of data after secure storage. Experimental results clearly show that our proposed method can effectively improve read and write performance of data, and that the performance of data security operations is improved from current standard implementations. [ABSTRACT FROM AUTHOR]

Big Data (BD) has turned into a significant research field owing to the dawn of vast quantity of data generated as of various sources like Internet of things (IoT), social media, and also multimedia applications. BD has played an imperative part in numerous decision-makings as well as forecasting domains for instance health care, recommendation systems, web display advertisement, transportation, clinicians, business analysis, and fraud detection along with tourism marketing. The domain of health care attained its influence by the effect of BD since the data sources concerned in the healthcare organizations are famous for their volume, heterogeneous complexity, and high dynamism. Though the function of BD analytical techniques, platforms, and tools are realized among various domains, their effect on healthcare organization for possible healthcare applications shows propitious research directions. This paper concentrates on the analysis of multiple diseases using modified adaptive neuro-fuzzy inference system (M-ANFIS). Initially, the healthcare BD undergoes pre-processing. In the pre-processing step, data format identification and integration of the healthcare BD dataset is done. Now, features are extracted from the preprocessed dataset and the count of the closed frequent item set (CFI) is found. Then, the entropy of the CFI count is determined. Finally, analyses of the multiple diseases are executed with the aid of M-ANFIS. In M-ANFIS, k-medoid clustering is used to cluster the CFI entropy of healthcare BD. The proposed method's performance is assessed by comparing it with the other existent techniques. [ABSTRACT FROM AUTHOR]

A huge amount of sensing data are generated by a large number of pervasive IoT devices. In order to find meaningful information from the big data, it is essential to perform pre-processing, in which many outlier data points need to be removed, because they deteriorate as time passes. Although pre-processing is essential in the big data field, there has been a significant lack of research works with case studies. In this paper, big data pre-processing methods are investigated and proposed. To evaluate the pre-processing methods for accurate analysis, we used a collection of digital tachograph (DTG) data. We obtained DTG sensing data of 6198 driving vehicles over a year. We studied five kinds of pre-processing methods: filtering ranges, excluding meaningless values, comparing filters from variables, applying statistical techniques, and finding driving patterns. In addition, we developed a MapReduce program using a Hadoop ecosystem and deployed big data to perform the pre-processing analysis. Through the pre-processing steps, we confirmed that the proportion of DTG sensing data points including any errors was up to 27.09%. Compared to the traditional brute-force way to detect, ours had 71.1% additional detection effect. In addition, we confirmed that outlier data points, which are difficult to detect through simple range error pre-processing, could be well detected. [ABSTRACT FROM AUTHOR]

In the era of big data, scientific research is entering a key stage of scientific development under the guidance of a new paradigm, 'e-Science', and the core characteristics of which are collaboration and sharing. In the past decade, e-Science has rapidly developed around the world. There are now e-Science strategic plans, projects and extensive research activities on the national and international scales that encompass particle physics, astronomy, earth science, ecology, marine science, medicine, life sciences and other disciplines. However, there is no uniform and clear understanding of the essence, characteristics, infrastructure and application of e-Geoscience. This paper first discusses and analyzes the development of e-Science in a global context and then explores its development in China. Next, the development of e-Geoscience is discussed, particularly regarding the details of its design and implementation in China, including a conceptual model, a mode of application, a logical hierarchy, and functional and technical systems. Finally, the paper introduces a typical application, called the Northeast Asia Joint Scientific Exploration and Cooperative Research Platform (NAJSECRP), which is operating in research institutions in China, Russia and Mongolia. This platform can not only provide geodata and bibliographies and promote resource sharing but also provides a collaborative research platform for scientific exploration. In practice, this platform has been shown to save costs and improve the efficiency of transnational, interdisciplinary scientific exploration and cooperative research. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing amount of data being produced and stored, which is known as Big Data. The social networks, internet of things, scientific experiments and commercial services play a significant role in generating a vast amount of data. Three main factors are important in Big Data; Volume, Velocity and Variety. One needs to consider all three factors when designing a platform to support Big Data. The Large Hadron Collider (LHC) particle accelerator at CERN consists of a number of data-intensive experiments, which are estimated to produce a volume of about 30 PB of data, annually. The velocity of these data that are propagated will be extremely fast. Traditional methods of collecting, storing and analysing data have become insufficient in managing the rapidly growing volume of data. Therefore, it is essential to have an efficient strategy to capture these data as they are produced. In this paper, a number of models are explored to understand what should be the best approach for collecting and storing Big Data for analytics. An evaluation of the performance of full execution cycles of these approaches on the monitoring of the Worldwide LHC Computing Grid (WLCG) infrastructure for collecting, storing and analysing data is presented. Moreover, the models discussed are applied to a community driven software solution, Apache Flume, to show how they can be integrated, seamlessly. [ABSTRACT FROM AUTHOR]

Data storage, especially big data storage, is a research hot spot in Internet of Things (IoT) system today. In traditional data storage methods, the fault-tolerant algorithm for data copies is adjusted with whole data file, which causes huge redundancy because there are less utilization and more cost of data storage when only a part of data blocks in the file are accessed. Therefore, an optimized cluster storage method for big data in IoT is proposed in this paper. First, weights of data blocks in each historical accessing period are calculated by temporal locality of data access, and the access frequencies of the data block in next period are predicted by the weights. Second, the hot spot of a data block is determined with a threshold which is calculated by previous data access. Meantime, in order to improve the data access efficiency and resource utilization, as well as to reduce the copy storage costs, copy of data block is dynamically adjusted and stored in different groups with high-performance and low-load nodes for data balance. Finally, experimental results show that the storage cost of proposed method is 70% less than that of traditional methods, which means that the proposed method effectively improves the data access speed, reduces storage space, and balances the storage load. [ABSTRACT FROM AUTHOR]

Cities, such as industry or the universities, are immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things (IoT), Big Data and Artificial Intelligence and its consequences: hyperconnectivity, datafication and algorithmization. A process of transformation towards what has come to be called as Smart Cities. The aim of this paper is to show the impacts and consequences of digital connectivity, algorithmization and the datafication of urban digital society to outline possible ways of resolving the underlying moral conflicts. [ABSTRACT FROM AUTHOR]

Software Defined Networks (SDN) has been attracting researchers, scientist, and technology experts from both academia and industry to enhance the current ICT stakes and networking paradigm. The beauty of SDN is the division of Control and Data planes and make it easy for the engineers to modify the networking protocols without visiting onsite devices. Similarly, smart cities concept has been coined recently, where a plethora of smart devices will be connected and providing tons of services to the citizens, officials, and governmental departments. The Internet of Things (IoT) plays a vital role in guaranteeing such services. Few efforts have been made to merge SDN and IoT with the sole purpose of efficient Data retrieval and achieve remotely configurable networks. In this paper, we explicitly define the Internet of Software Defined Things architecture and bring it to Smart Cities as a use-case. Our 3-tier architecture consists of Data Collection, Data Management, and Application levels that are further connected via two intermediate levels working on SDN principles. Followed by the potentials of SDN and IoT for Smart Cities, we evaluated our proposed architecture using Spark and GraphX with Hadoop Ecosystem and the results shows that efficient transfer of Data over SDN for real-time processing is achieved. [ABSTRACT FROM AUTHOR]

Based on the analysis of 5G and Internet of Things technology, this paper proposes the reference architecture of smart factory and its application path for traditional manufacturing enterprises in China, in which the intelligent manufacturing workshop is the core component of smart factory. The Internet of Things technology combined the advanced technologies (Industrial Big Data, WSN, RFID, Cloud Computing Platform) and provides hardware network foundation and technical theory for designing the real-time tracking and monitoring system of intelligent workshop products. The developed system has the advantages of low cost, rapid deployment, and convenient expansion, which traditional manufacturing enterprises realize intelligent management based on IoT application platform. [ABSTRACT FROM AUTHOR]

There is increased interest in deploying big data technology in the healthcare industry to manage massive collections of heterogeneous health datasets such as electronic health records and sensor data, which are increasing in volume and variety due to the commoditization of digital devices such as mobile phones and wireless sensors. The modern healthcare system requires an overhaul of traditional healthcare software/hardware paradigms, which are ill-equipped to cope with the volume and diversity of the modern health data and must be augmented with new "big data" computing and analysis capabilities. For researchers, there is an opportunity in healthcare data analytics to study this vast amount of data, find patterns and trends within data and provide a solution for improving healthcare, thereby reducing costs, democratizing health access, and saving valuable human lives. In this paper, we present a comprehensive survey of different big data analytics integrated healthcare systems and describe the various applicable healthcare data analytics algorithms, techniques, and tools that may be deployed in wireless, cloud, Internet of Things settings. Finally, the contribution is given in formation of a convergence point of all these platforms in form of SmartHealth that could result in contributing to unified standard learning healthcare system for future. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) is an internet among things through advanced communication without human's operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods. [ABSTRACT FROM AUTHOR]

Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics Big Data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and Big Data analytics in industry field. [ABSTRACT FROM AUTHOR]

Due to the prevalence of IoV technology, big data has increasingly been promoted as a revolutionary development in a variety of applications. Indeed, the received big data from IoV is valuable particularly for those involved in analyzing driver's behaviors. For instance, in the fleet management domain, fleet administrators are interested in fine-grained information about fleet usage, which is influenced by different driver usage patterns. In the vehicle insurance market, usage-based insurance or pay-as-you-drive schemes aim to adapt the insurance premium to individual driver behavior or even to provide various value-added services to policy holders. These applications can be expected to improve and to make safer the driving style of various individuals. Nowadays, big data analysis is becoming indispensable for automatic discovering of intelligence that is involved in the frequently occurring patterns and hidden rules. It is essential and necessary to study how to utilize these large-scale data. Regarding driving behaviors analysis, this paper presents a preliminary study based on feature selection and statistical approach. Feature selection is one of the important and frequently used techniques in data preprocessing for big data mining. Feature selection, as a dimensionality reduction technique, focuses on choosing a small subset of the significant features from the original data by removing irrelevant or redundant features. According to selection process, the most significant feature is vehicle speed for the collected vehicular data. Afterward, the statistical approach calculates skewness and dispersion in speed distribution as the statistical features for driving behaviors analysis. Finally, the established classification rules not only provide data-driven services and big data analytics but also offer training data samples for supervised machine learning algorithms. To validate the feasibility of the proposed method, over 150 drivers and more than 200,000 trips are verified in the simulation. As expected, experimental results are well matched with our observations. [ABSTRACT FROM AUTHOR]

State-of-the-art publish/subscribe systems are efficient when the subscriptions are relatively static—for instance, the set of followers in Twitter—or can fit in memory. However, now-a-days, many big data and IoT based applications follow a highly dynamic query paradigm, where both continuous queries and data entries are in the millions and can arrive and expire rapidly. In this paper we propose and compare several publish/subscribe storage architectures, based on the popular NoSQL log-structured merge tree (LSM) storage paradigm, to support high-throughput and highly dynamic publish/subscribe systems. Our framework naturally supports subscriptions on both historic and future streaming data, and generates instant notifications. We also extend our framework to efficiently support self-joining subscriptions, where streaming pub/sub records join with past pub/sub entries. Further, we show how hierarchical attributes, such as concept ontologies, can be efficiently supported; for example, a publication's topic is "politics" whereas a subscription's topic is "US politics." We implemented and experimentally evaluated our methods on the popular LSM-based LevelDB system, using real datasets, for simple match and self-joining subscriptions on both flat and hierarchical attributes. Our results show that our approaches achieve significantly higher throughput compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

This work makes a case for the integration of the increasingly popular and largely separate topics of Industry 4.0 and the circular economy (CE). The paper extends the state-of-the-art literature by proposing a pioneering roadmap to enhance the application of CE principles in organisations by means of Industry 4.0 approaches. Advanced and digital manufacturing technologies are able to unlock the circularity of resources within supply chains; however, the connection between CE and Industry 4.0 has not so far been explored. This article therefore contributes to the literature by unveiling how different Industry 4.0 technologies could underpin CE strategies, and to organisations by addressing those technologies as a basis for sustainable operations management decision-making. The main results of this work are: (a) a discussion on the mutually beneficial relationship between Industry 4.0 and the CE; (b) an in-depth understanding of the potential contributions of smart production technologies to the ReSOLVE model of CE business models; (c) a research agenda for future studies on the integration between Industry 4.0 and CE principles based on the most relevant management theories. [ABSTRACT FROM AUTHOR]

With the revolution of the Internet technology, smart-sensing applications and the Internet of Things (IoT) are coupled in critical missions. Wireless sensor networks (WSNs), for example, present the main enabling technology in IoT architectures and extend the spectrum of their smart applications. However, this technology has limited resources and suffers from several vulnerabilities and security issues. Since the wireless networks used by this technology are deployed in open areas, several challenges are faced by the service provider in terms of privacy and the quality of service. Encryption can be a good solution to preserve confidentiality and privacy, but it raises serious problems concerning time latency and performance. In this paper, we propose agile framework that enables authentication, confidentiality and integrity while collecting the sensed data by using elliptic curve cryptography. [ABSTRACT FROM AUTHOR]

With advancements in popular modern wearable devices, such as Apple Watch and Fitbit, it is now possible to harness these technologies for continuous monitoring and recording of heart rate data, which can then be used for medical research and ultimately e-health applications. In this paper, we report the development of a new mobile smartphone application (app) that enables heart rate profiles to be extracted and analysed from continuous heart rate monitoring time series. The new iOS app, called Hactive, extracts heart rate data from Apple's smartwatches to construct heart rate profiles. A key innovation is Hactive's ability to detect and analyse exercise-associated heart rate changes from continuous heart rate data, which enables heart rate profiles to be constructed based on free-living conditions. We believe this tool advances the use of wearable technology to collect physiologically relevant big data for healthcare and medical research. The source code of Hactive is available via an MIT open source licence at https://github.com/VCCRI/hactive. [ABSTRACT FROM AUTHOR]

In this technological era, every person, authorities, entrepreneurs, businesses, and many things around us are connected to the internet, forming Internet of thing (IoT). This generates a massive amount of diverse data with very high-speed, termed as big data. However, this data is very useful that can be used as an asset for the businesses, organizations, and authorities to predict future in various aspects. However, efficiently processing Big Data while making real-time decisions is a quite challenging task. Some of the tools like Hadoop are used for Big Datasets processing. On the other hand, these tools could not perform well in the case of real-time high-speed stream processing. Therefore, in this paper, we proposed an efficient and real-time Big Data stream processing approach while mapping Hadoop MapReduce equivalent mechanism on graphics processing units (GPUs). We integrated a parallel and distributed environment of Hadoop ecosystem and a real-time streaming processing tool, i.e., Spark with GPU to make the system more powerful in order to handle the overwhelming amount of high-speed streaming. We designed a MapReduce equivalent algorithm for GPUs for a statistical parameter calculation by dividing overall Big Data files into fixed-size blocks. Finally, the system is evaluated while considering the efficiency aspect (processing time and throughput) using (1) large-size city traffic video data captured by static as well as moving vehicles’ cameras while identifying vehicles and (2) large text-based files, like twitter data files, structural data, etc. Results show that the proposed system working with Spark on top and GPUs under the parallel and distributed environment of Hadoop ecosystem is more efficient and real-time as compared to existing standalone CPU-based MapReduce implementation. [ABSTRACT FROM AUTHOR]

IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract “sense” from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a (fast) evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts. [ABSTRACT FROM AUTHOR]

Advanced sensing and internet of things bring the big data, which provides an unprecedented opportunity for data-driven knowledge discovery. However, it is common that a large number of variables (or predictors, features) are involved in the big data. Complex interdependence structures among variables pose significant challenges on the traditional framework of predictive modeling. This paper presents a new methodology of self-organizing network to characterize the interrelationships among variables and cluster them into homogeneous subgroups for predictive modeling. Specifically, we develop a new approach, namely nonlinear coupling analysis to measure variable-to-variable interdependence structures. Further, each variable is represented as a node in the complex network. Nonlinear-coupling forces move these nodes to derive a self-organizing topology of the network. As such, variables are clustered into sub-network communities. Results of simulation experiments demonstrate that the proposed method not only outperforms traditional variable clustering algorithms such as hierarchical clustering and oblique principal component analysis, but also effectively identifies interdependent structures among variables and further improves the performance of predictive modeling. Additionally, real-world case study shows that the proposed method yields an average sensitivity of 96.80% and an average specificity of 92.62% in the identification of myocardial infarctions using sparse parameters of vectorcardiogram representation models. The proposed new idea of self-organizing network is generally applicable for predictive modeling in many disciplines that involve a large number of highly-redundant variables. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate a novel cloud big data system, s2Cloud, to enabled both smart and secure transmission and management of patient big data from Internet of Things (IoT), especially in the new common of long covid. More specifically, s2Cloud allows doctors to manage patient records effectively through the interactive web server. It further enables real-time, long-term, and continuous streaming and management of biomechanical dynamics from IoT phones/wearables. The big data can be visualized in both real-time and historical modes. Besides, s2Cloud achieves big data security through secure sign up and log in, as well as data transmission protection. It also allows doctor-patient interactions through instantaneous chat. s2Cloud has been evaluated on the real-world application, in which human IoT biomechanical dynamics are streamed, managed, stored, and visualized to demonstrate the effectiveness. Overall, the proposed system, empowered by smart and secure design innovations, has demonstrated the feasibility and potential for IoT big data-driven precision health. [ABSTRACT FROM AUTHOR]

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline. [ABSTRACT FROM AUTHOR]

The widespread growth of Big Data and the evolution of Internet of Things (IoT) technologies enable cities to obtain valuable intelligence from a large amount of real-time produced data. In a Smart City, various IoT devices generate streams of data continuously which need to be analyzed within a short period of time; using some Big Data technique. Distributed stream processing frameworks (DSPFs) have the capacity to handle real-time data processing for Smart Cities. In this paper, we examine the applicability of employing distributed stream processing frameworks at the data processing layer of Smart City and appraising the current state of their adoption and maturity among the IoT applications. Our experiments focus on evaluating the performance of three DSPFs, namely Apache Storm, Apache Spark Streaming, and Apache Flink. According to our obtained results, choosing a proper framework at the data analytics layer of a Smart City requires enough knowledge about the characteristics of target applications. Finally, we conclude each of the frameworks studied here have their advantages and disadvantages. Our experiments show Storm and Flink have very similar performance, and Spark Streaming, has much higher latency, while it provides higher throughput. [ABSTRACT FROM AUTHOR]

Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules. [ABSTRACT FROM AUTHOR]

Intrusion Detection is essential to identify malicious incidents and continuously alert many users of the Internet of Things (IoT). The constant monitoring of events generated from many devices connected to the IoT and the extensive analysis of every event based on predefined security policies consumes enormous resources. Accordingly, performance enhancement is a crucial concern of Intrusion Detection in IoT and other massive Big Data Applications to ensure a secure environment. Like many Big Data Applications, the Intrusion Detection system of the IoT needs to employ the fast membership filter, Bloom Filter, to quickly identify possible attacks. Bloom Filter is an admiringly fast and space-efficient data structure that quickly handles elements of extensive datasets in small memory space. However, the trade-off between the query performance, the number of hash functions, memory space, and false positive probability remains an issue of Bloom Filter. Thus, this article presents an enhanced Bloom Filter (eBF) that remarkably improves memory efficiency and introduces new techniques to accelerate the filtering of malicious URLs. We experimentally show the efficacy of eBF using a real Intrusion Detection dataset. The experimental result shows that the proposed filter is remarkably memory efficient, faster, and more accurate than the state-of-the-art filters. eBF requires 15.6x, 13x, and 8x less memory compared with Standard Bloom Filter, Cuckoo filter, and robustBF, respectively. Therefore, this new system significantly enhances the performance of Intrusion Detection of IoT that concurrently monitors several billion events crosschecking with the defined security policies. [ABSTRACT FROM AUTHOR]

Background: We are living in an age where data is everywhere and grows up in a very speedy way. Thanks to sensors, mobile phones and social networks, we can gather a hug amount of information to understand human behavior as well as his individual life. In healthcare system, big data analytics and machine learning algorithms prove their effectiveness and efficiency in saving lives and predicting new diseases. This triggered the idea of taking advantages of those tools and algorithms to create systems that involve both doctors and patients in the treatment of disease, predict outcomes and use real-time risk factors from sensors and mobile phones. Methods: We distinguish three types of data: data from sensors, data from mobile phones and data registered or updated by the patient in a mobile app we created. We take advantages from IoT systems such as Raspberry Pi to collect and process data coming from sensors. All data collected is sent to a NoSql Server to be then analyzed and processed in Databricks Spark. K-means centroid clustering algorithms is used to build the predictive model, create partitions and make predictions. To validate results in term of efficiency and effectiveness, we used clustering validations techniques: Random K, Silhouette and Elbow methods. Results: The main contribution of our work is the implementation of a new system that has the capability to be applied in several prediction disease researches using Big Data Analytics and IoT. Also, comparing to other studies in literature that use only medical or maternal risk factors from echography; our work had the advantage to use real-time risk factors (maternal and medical) gathered from sensors, react in advance and track diseases. As a case study, we create an e-monitoring real-time miscarriage prediction system to save baby's lives and help pregnant women. In fact, doctors receive the results of clustering and track theirs patient through our mobile app to react in term of miscarriage to avoid non-suitable outcomes. While pregnant women receive only advices based on their behaviors. The system uses 15 real-time risk factors and our dataset contains more than 1,000,000 JSON files. Elbow method affirm three as the optimal number of clusters and we reach 0.99 as a value of Silhouette method, which is a good sign that clusters are well separated and matched. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT. [ABSTRACT FROM AUTHOR]

When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all parties. Second, we adopt multi-head attention (MHA)-based gated recurrent unit (GRU) to do inbound logistics task prediction. Finally, numerical results justify that multi-head attention-based GRU model has better fitting efficiency and prediction accuracy than its counterparts. [ABSTRACT FROM AUTHOR]

Smart cities would depend on digital technologies that have the potential ahead where Blockchains would perform significantly. Blockchains has become popular because it offers adequate security than other solutions for a wide range of purpose. Blockchains can support the growth of smart cities due to their robust features like transparent, reliable, authenticate, and decentralized features. At present, big cities are utilizing sophisticated technologies with knowledge platforms to evaluate the quality of life of their residents. Society participation and dedication grow when humans are even more committed to responding with and being adaptable at some stage in their metropolis authorities and responding in an established way to personal problems. Big data, IoT, and Blockchain would accelerate and extend its government services, capacity, and even reduces costs and provides exciting opportunities like transportation services, exchanging services, healthcare services, etc. This study shows the reviews and importance of Blockchain in futuristic smart cities. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is rapidly developed and widely deployed in recent years, which makes the sensory data generated by IoT systems explode. The huge amount of sensory data generated by some IoT systems has already exceeded the storage, transmission, and computation capacities of IoT systems. However, the valuable sensory data which is highly related to a query in an IoT system is relatively small. The sensory data which is highly related to a query Q forms the relative kernel dataset of Q. Therefore, retrieving sensory data in the relative kernel dataset of a query instead of the raw sensory data will reduce the heavy burdens of an IoT system in terms of transmission and computation and then reduce the energy consumption of the IoT system. In this paper, we investigate the problem of retrieving relative kernel dataset from big sensory data for continuous queries in IoT systems. Two algorithms, relative kernel dataset retrieving algorithm and piecewise linear fitting-based relative kernel dataset retrieving algorithm, are proposed to retrieve the relative kernel dataset for continuous queries. Beside, algorithms for estimating the answers of continuous queries based on their relative kernel datasets are also proposed. Extensive simulation results are provided to verify the effectiveness and energy efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Several studies have used machine learning algorithms to develop intrusion systems (IDS), which differentiate anomalous behaviours from the normal activities of network systems. Due to the ease of automated data collection and subsequently an increased size of collected data on network traffic and activities, the complexity of intrusion analysis is increasing exponentially. A particular issue, due to statistical and computation limitations, a single classifier may not perform well for large scale data as existent in modern IDS contexts. Ensemble methods have been explored in literature in such big data contexts. Although more complicated and requiring additional computation, literature has a note that ensemble methods can result in better accuracy than single classifiers in different large scale data classification contexts, and it is interesting to explore how ensemble approaches can perform in IDS. In this research, we introduce a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15). We further enhance incorporate feature selection techniques to select the best relevant features with the proposed SET. A comprehensive performance analysis shows that our proposed model can better identify the normal and anomaly traffic in network than other existing IDS models. This implies the potentials of our proposed system for cybersecurity in Internet of Things (IoT) and large scale networks. [ABSTRACT FROM AUTHOR]