Request-based, secured and energy-efficient (RBSEE) architecture for handling IoT big data

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

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

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

SummaryAs an emerging technology, blockchain (BC) has been playing a promising role in today's software‐defined networking (SDN)‐enabled Internet of Things (IoT) applications. Because of the salient feature of the network function virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud‐driven ecosystem. When cloud‐enabled systems encounter immense security and operational challenges caused mainly by third‐party dependency, large‐scale data communication, and maintenance, BC offers effective and robust data transfer solutions without incorporating intermediaries over the distributed network. With the increased SDN‐BC convergence in the IoT domain, the underlying challenges and perspectives deserve proper attention methodically and structurally. From the motivation of addressing such issues, this study provides necessary insights to combine those for successful plug‐and‐play. Therefore, the study includes purposefully investigating current state‐of‐the‐art to extract the research trends, future directions, and perspectives in this domain. This study provides a comprehensive survey of IoT, SDN, NFV, and BC‐enabled emerging technologies. More importantly, the authors intelligently integrated the four different technologies—IoT, SDN, BC, and NFV based on characteristics, scopes, challenges, taxonomies, and tables in numerous areas. Initially, the authors introduce the SDN‐IoT ecosystem in brief and address the features and applications. We took a close look at the SDN's overall taxonomy based on security, environment, scopes, and challenges. We also briefly describe the integration of SDN‐IoT with the NFV ecosystems. Moreover, we review the prospect of BC technology from security perspectives, its extent, challenges of practical implementation, and the possible integration of IoT regarding smart applications. Finally, this study highlights several future directions based on these technologies.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT

Millions of devices equipped with sensors are connected together to communicate with each other in order to collect and exchange data. The phenomenon of daily life objects that are interconnected through a worldwide network is known as the Internet of Things (IoT) or Internet of Objects. These sensors from a large number of devices or objects simultaneously and continuingly generate a huge amount of data, often referred to as Big Data. Handling this vast volume, and different varieties, of data imposes significant challenges when time, resources, and processing capabilities are constrained. Hence, Big Data analytics become even more challenging for data collected via the IoT. In this chapter, we discuss the challenges pertaining to Big Data in IoT; these challenges are associated with data management, data processing, unstructured data analytics, data visualization, interoperability, data semantics, scalability, data fusion, data integration, data quality, and data discovery. We present these challenges along with relevant solutions.

Chapter Nine - Envisioning big data in IoT with augmented and virtual reality: challenges,opportunities, and potential solutions

The sharing economy and new business models have emerged as a result of the economy’s rapid growth in recent years. The sharing economy and innovative business models have enabled people to transact with one another, saving both time and money. The growth has been aided by the introduction of IoT big data. This study will look at how to use big data from the Internet of Things to examine the growth of the sharing economy and new business models. This study proposes a BP neural network algorithm based on the big data of the Internet of Things (IoT), which can not only effectively classify a large amount of data in the sharing economy but also predict the development direction of the sharing economy and new business models. The experimental results of this study show that the economic benefit of shared office in 2017 is 11 billion yuan, and the economic benefit of shared office in 2018 is 20.6 billion yuan, an increase of 87.3% in 2018 compared with 2017. The economic benefit of knowledge and skills in 2017 was 138.2 billion yuan, and the economic benefit of shared office in 2018 was 235.3 billion yuan, an increase of 70.3% in 2018 compared with 2017. It can be seen that the economic benefits brought by the sharing economy to the country are very high, and there is also a lot of room for growth. Therefore, based on the background of IoT big data, it is essential to conduct research on the development of sharing economy and new business models.

Internet of Things (IoT) is an evolution of the current Internet into a ubiquitous network of interconnected objects which collects inputs through the sensing mechanisms and takes actions based on those inputs. Fuelled by the falling costs of the sensors, streams of raw data is transmitted to the IoT network continuously which is voluminous in the scale of Big Data. This puts an immense value on the frameworks, tools and techniques which can convert this raw data into usable knowledge.

With the continuous expansion and deepening of international exchanges and cooperation, language differences have become the biggest obstacle to exchanges and cooperation. At present, English translation software is the translation between natural languages made by humans on the basis of computers. As an important technical means to break through language barriers, machine translation is a software that uses computers to process spoken or written language. This paper aims to study the innovative system of English translation software, using the Internet of Things and big data algorithms to analyze some loopholes and deficiencies in the innovative system, and provide some data research for the system update. This paper proposes to analyze the innovative system of translation software based on the big data of the Internet of Things (IoT). Among them, the big data of the Internet of Things chooses the dynamic frame time slot ALOHA algorithm and the lower limit estimation algorithm. Its application in system analysis is very innovative. The system consists of a translation engine and a machine dictionary, which solves the basic problem of insufficient translation database of current translations, and plays a role in helping the English translation software innovation system to improve the accuracy of translations. The experimental results of this paper show that the translation system’s translation software achieves 61.3% of the A-target fluent translations, and 22.7% of the B-targets can translate the original text. It can be seen that English translation software has important research significance.

In this article, the authors have discussed about the connection between Internet of Things and growth of big data. They have also discussed short reference on the evolution, features, lifecycle, and implementation of Big Data from IoT over the cloud. Internet of Things represents a platform or environment that consists of enormous number of sensors and mediators interconnecting heterogeneous physical devices over the internet. IoT applications are available in many real-world areas such as smart city, smart workplace, smart home, smart transportation and various other ubiquitous computing areas. Using IoT applications generates tremendous amount of data for storage and management in the internet. With the time and research evolution integration of the IoT platforms and cloud comes in the market and IoT platforms data storage and management started shifting to the cloud from the internet connected physical systems for many real-world application areas. Meanwhile when this data becomes huge termed as Big Data. Handling of Big Data over the cloud develops many new areas of research and attention.

This article is a deepening of our article that was published in 2017 in the journal. We took some elements but we also developed other concepts to make a consequent synthesis on the IoT. We present, 1) Connected Object (OC), 2) Internet of Things: Definition, 3) Steps and Technologies in the IoT Ecosystem, 4) IoT to the Internet of Everything (IoE), 5) IoT and Big Data, 6) Cloud computing applied to Big Data and IoT, 8) Data Science and IoT, 9) Issues and challenges of IoT, 10) Opportunities and threats of the IoT ecosystem, 11) Security of the IoT, 12) Blockchain and IoT, and 13) in conclusion, we summarize the perspectives of Ido.

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

IoT ecosystems generates vast amount of data from interconnected devices, sensors, and networks. This vast amount of data poses significant challenges for traditional way of analysis. Deep learning comes into picture, with its ability to model complex pattern and unstructured data, addressing these challenges and elevating the IoT systems to new levels of intelligence. The integration of deep learning with Internet of Things (IoT) ecosystems provides an advancement in creating the intelligent network of connected devices. Key deep learning architectures like convolutional neural network (CNN) and Recurrent Neural Network (RNN) excel at processing sensor data for visual inspection and environmental monitoring. Looking forward, advancement in improvised learning, explainable AI, and next generation networks like 5G/6G will further enhance the capabilities of intelligent IoT ecosystems. This chapter explains how deep learning architecture facilitates the applications of IoT.