Big Internet Of Things Research Articles

Bioeconomy Sustainability: Integrating Circular Economy Principles with Big Data and IoT for Sustainable Farming in Agriculture 4.0

This concept paper explores the synergy between Bioeconomy sustainability and advanced technologies, specifically the integration of circular economy principles with big data and the Internet of Things (IoT), in the context of sustainable farming within Agriculture 4.0 in Malaysia. Despite limited understanding, the study aims to unveil the potential benefits of this integration and assess the current state of technology adoption, bioeconomic practices, and sustainable farming in Malaysia. Challenges faced by Malaysian farmers, such as awareness gaps and resistance to change, are identified, and strategies, including targeted education and financial incentives, are proposed to overcome these barriers. While acknowledging potential limitations in universality due to data access constraints and the dynamic nature of technology and agriculture, the study emphasizes the importance of integrating these innovative approaches to propel Malaysian agriculture toward sustainability within the Agriculture 4.0 framework.

An Investigation on Handling IoT Big Data with RBSEE Architecture

Internet of Things (IoT) devices have resulted in an unprecedented surge in data generation, presenting both opportunities and challenges in data management and analytics. This paper investigates the feasibility and effectiveness of utilizing the RBSEE (Resource-Based Storage, Edge Computing, and Elasticity) architecture for handling IoT Big Data. Firstly, the paper elucidates the fundamental concepts of IoT and Big Data, highlighting the unique characteristics and challenges associated with managing large volumes of heterogeneous data streams generated by IoT devices. Subsequently, it introduces the RBSEE architecture, which integrates resource-based storage, edge computing, and elasticity to address the scalability, latency, and resource constraints inherent in IoT environments. The architecture's ability to distribute data storage and processing tasks across edge devices and cloud infrastructure while dynamically adapting to fluctuating workloads makes it particularly suitable for handling IoT Big Data. In any typical IoT ecosystem, real-time and instantaneous handling of request and dataset is pivotal and a fundamental property. This aims to propose energy efficient distributed system architecture for persuasive, privacy-preserved and real time handling of data generated from IoT devices.

Shipping Safety Innovation: The Role of Big Data and IoT in Reducing Risk

Shipping safety is a crucial aspect in the maritime industry which faces various risks of accidents. This research aims to examine the innovative role of Big Data and the Internet of Things (IoT) in improving shipping safety through accident prediction and prevention. The research method used is qualitative using secondary data from industry reports, journal articles and related publications. The research results show that Big Data can improve the ability to predict and prevent shipping accidents by analyzing weather data, ship movements and sea conditions to produce accurate predictive models. In addition, IoT makes a specific contribution in real-time monitoring and risk mitigation through sensors and AIS systems that provide real-time data for fast and informed decision making. However, the integration of Big Data and IoT faces major challenges such as cybersecurity, implementation costs, and the need for adequate infrastructure. Nevertheless, the prospects for developing this technology are very promising with increased security protocols, reduced costs through innovation, and development of maritime telecommunications infrastructure. In conclusion, Big Data and IoT have great potential to improve shipping safety, but require attention to existing challenges to realize their full benefits.

The role of smart technology, managerial initiatives and human factors on sustainable manufacturing: a case study of Indonesian oil and gas workers

This study aims to analyse and determine the effect of Big Data, the Internet of Things (IoT), and physical-cyber system variables on human factors in refinery industry operators and the influence of human factors and managerial initiatives on sustainable manufacturing. The method used in this study is a quantitative method using partial least square-structural equation modelling (PLS-SEM). The respondents in this study were workers of Indonesia’s upstream oil and gas sector. The results of this study indicate that Big Data, IoT, and Physical Cyber Systems (PCS) have a positive and significant effect on the human factor. In addition, there is a significant relationship between human factors and sustainable manufacturing. Furthermore, it is also found that there is a relationship between managerial initiatives and sustainable manufacturing. However, the managerial initiative cannot moderate the human factor and sustainable manufacturing.

The role of big data and IoT in logistics supply chain management of e-commerce

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. Different imputation methods were employed in the experimentation with various missing value imputation techniques, and the performances of machine learning (ML) models were compared. A hybrid model that integrates ML and sample-based statistical techniques for missing value imputation is being proposed. Furthermore, the continuation involved the dataset with the best missing value imputation, chosen based on ML model performance for subsequent feature engineering and hyperparameter tuning. K-means clustering and principal component analysis were applied in our study. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, K-fold cross-validation was implemented.

Fabrication of Sb2S3/Sb2Se3 heterostructure for potential resistive switching applications

The exponential growth of large data and the widespread adoption of the Internet of Things (IoT) have created significant challenges for traditional Von Neumann computers. These challenges include complex hardware, high energy consumption, and slow memory access time. Researchers are investigating novel materials and device architectures to address these issues by reducing energy consumption, improving performance, and enabling compact designs. A new study has successfully engineered a heterostructure that integrates Sb2Se3 and Sb2S3, resulting in improved electrical properties. This has generated significant interest in its potential applications in resistive switching. In this study, we have demonstrated the fabrication of a device based on Sb2S3/Sb2Se3 heterostructure that exhibits resistive switching behavior. The device has different resistance states that can be switched between high and low resistance levels when exposed to an external bias (−1 V to 0 V to 1 V). It also has good non-volatile memory characteristics, including low power consumption, high resistance ratio (∼102), and reliable endurance (∼103). The device enables faster data processing, reduces energy consumption, and streamlines hardware designs, contributing to computing advancements amidst modern challenges. This approach can revolutionize resistive switching devices, leading to more efficient computing solutions for big data processing and IoT technologies.

Big data and IoT adoption in shaping organizational citizenship behavior: The role of innovation organizational predictor in the chemical manufacturing industry

This research aims to investigate the relationships between Big Data and Internet of Things (IoT) adoption and employee behavior in the chemical manufacturing industry, specifically focusing on the mediating role of organizational innovation. The research methodology employs a quantitative approach that involves employee surveys, statistical analysis, and mediation testing. The primary findings reveal that Big Data adoption significantly enhances Organizational Innovation, contributing positively to Organizational Citizenship Behavior (OCB) among employees. Conversely, IoT adoption has a significant positive impact on Organizational Innovation but does not directly influence OCB. The relationship between IoT adoption and OCB is mediated by Organizational Innovation, highlighting the pivotal role of innovation as an intermediary in influencing employee behavior. The practical implications of this research suggest that organizations in the chemical manufacturing industry should strategically integrate Big Data and IoT technologies to foster innovation and elevate OCB. Leadership support and employee training are crucial. Study limitations include industry specificity, self-reported data, and static analysis. Future research should diversify samples and use longitudinal methods. Recommendations: embrace tech with innovation focus, train leaders, and deepen understanding of tech, innovation, and behavior.

Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system.

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10-20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5-15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10-12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8-11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance.

INNOVATION IN DATA STORAGE TECHNOLOGIES: FROM CLOUD COMPUTING TO EDGE COMPUTING

In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage.
 The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations.
 The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field.
 Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors

Purpose The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data. Design/methodology/approach For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model. Findings Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM2.5 concentration level than bagging and boosting ensemble models. Research limitations/implications A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast. Practical implications The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system Originality/value This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Logistics Security in the Era of Big Data, Cloud Computing and IoT

Abstract In the backdrop of the Fourth Industrial Revolution and the use of new technologies like the Internet of Things (IoT), 5G, big data and cloud computing, this research paper intends to analyze the many cybersecurity concerns encountered by the logistics industry. This paper will offer insights into the main cybersecurity concerns affecting the logistics industry and explain viable ways for reducing these risks through a thorough study of recent research. The Internet of Things (IoT) has made it possible to use smart devices that can gather and analyze massive volumes of data in real-time, giving logistical operations valuable information. But as IoT devices develop, the attack surface also grows, leaving logistical systems open to digital attacks. With the introduction of 5G networks, the connection is now quicker and more dependable, enabling real-time communication between IoT devices and making it easier to implement cutting-edge logistical systems. Real-time data analysis is made possible through the scalable and secure infrastructure provided by cloud computing, which also improves the security of logistics systems. Security measures for logistics systems will grow more and more dependent on cybersecurity, IoT, 5G, big data, and cloud computing as logistics organizations continue to adopt digital technologies.

EBF: an enhanced Bloom Filter for intrusion detection in IoT

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.

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.

Digital maturity of business: Technological gap and limitations of digital transformation

Digital maturity of business is the object of the study. The subject of research is the process of systemic digital transformation of business. Research methods include indicative method and the method of system analysis. The premise of the study is to identify the unsystematic digitalization of business in industry as one of the significant problems of achieving digital maturity in the Strategy for Digital Transformation of Manufacturing Industries in the Russian Federation. The purpose of the study is to identify the reasons for the unsystematic processes of business digitalization to achieve “digital maturity”. To achieve this goal, technological trends in the global systemic digital business transformation were analyzed and monitoring of the compliance of digital business transformation processes in Russia with the identified characteristics of global systemic business digitalization was carried out. The study showed that in the Russian business sector there is a technological gap with the global processes of digital business transformation. As a result of the study, it was found that the main reasons for the unsystematic processes of digitalization of Russian business are the low quality of business digitalization with high digital activity and low technological capabilities for implementing Industry 4.0 technologies due to the limited use of the Internet mobile network in business, business cybersecurity tools, Internet communications and cloud services. The increment of knowledge consists in determining the technological principle of digital business transformation at the global level – this is a mobile device operating on the principles of machine-to-machine interaction M2M with big data IoT (Internet of things) based on 5G speeds and Wi-Fi 6 communication standards, as well as identifying a system of indicators for monitoring the technological basis for managing the “digital maturity” of a business.

The Relationship Between Big Data and IoT

The evolution of Internet of Things (IoT) makes the life of human being very easier. IoT makes it easy to control any device in a bit. Several IoT applications like smart homes, manufacturing, transportation, and consumer goods like wearables, smartphones are available. IoT is actually the network of devices that contain sensors, electronics, software’s, actuators, and connectivity which allows these things to connect, interact and exchange data. IoT made easy way to control the devices, collect the information from such devices, disseminate such information and transfer this information to analyze the same and to predict or prescribe the solution for the problems might exist. The large quantity of data is collected from IoT devices through the sensors where the Big Data came into picture. The paper reviews the relationship between IoT and Big Data.