Internet Of Things Big Data Research Articles

Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations' data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication - read file, second-level authentication - download file, and third-level authentication - download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time.

Blockchain-Enabled Decentralized Trust Management and Secure Usage Control of IoT Big Data

With the fast development of Internet-of-Things (IoT) technologies, IoT big data and its applications are getting more and more useful. However, traditional IoT data management is fragile and vulnerable. Once the gathered data are untrusted or the stored data are tampered with deliberately from the internal users or attacked by an external hacker, then the tampered data have a serious problem to be utilized. To solve the problems of trust and security of IoT big data management, in this article, we propose a permissioned blockchain-based decentralized trust management and secure usage control scheme of IoT big data (called BlockBDM), upon which all the data operations and management, such as data gathering, invoking, transfer, storage, and usage, are processed over the blockchain smart contract. To encourage the IoT client to supply high-quality content, in our scheme, we design public-blockchain-based tokens reward mechanism for the high-quality data supply contribution. All the data processing and usage procedure can be recorded in a cryptography-signed and Merkle tree-based transaction(s) and block(s) with high-level security in a global and distributed ledger with tamper resistance. For data utilization and consumption, we propose secure usage control for digital rights management and token-based data consumption approach of high-value data from being violated or spread without any limitation. We implemented the BlockBDM scheme based on public and permissioned blockchain for IoT big data management. Finally, a large amount of evaluation manifests that the proposed BlockBDM scheme is feasible, secure, and scalable for decentralized trust management of IoT big data.

Differentially Private Tensor Train Decomposition in Edge-Cloud Computing for SDN-Based Internet of Things

With the advent of the 5G era, the Internet of Things (IoT) will flourish in the future. Millions of IoT devices will be connected by 5G networks, which will bring great challenges to network management. Software-defined network (SDN) is a novel solution for managing a large number of IoT devices over the network. In order to solve the problems of secure data analysis in SDN-based IoT, a differentially private tensor computing model (DPTCM) is proposed in this article. Our approach utilizes tensor to model and analyze the SDN-based IoT big data, and an algorithm named differentially private tensor train decomposition (DPTTD) is proposed to achieve secure computing in SDN-based IoT. The algorithm can make full use of the flexible computing power of edge-cloud computing so as to realize collaborative computing between edges, cloud, and the third party. By separating the calculation process of the private data and nonprivate data, the algorithm implements the localized calculation and preservation of the original data which can protect the data security from the source. Meanwhile, we use the differential privacy technology to protect the privacy of data transmitted to the cloud. Finally, we prove that the algorithm satisfies $\varepsilon $ -differential privacy. In the experiments, we verify our model on two real-world data sets. The experimental results show that differential privacy has a little side effect on prediction results, and our model has good performance in data prediction.

The path to the Hotel of Things: Internet of Things and Big Data converging in hospitality

研究目的本论文开发了酒店管理中的物联网（IoT）的使用, 并且检验了其与大数据的关系。根据其理论和实际考虑, 这方面的科技缺乏应用理论基础和未来研究方向。研究设计/方法/途径本论文采用理论讲述的方式, 通过例子来阐述IoT的应用和大数据对于酒店管理的影响。本论文还拓展了IoT、大数据、以及酒店管理在文献范畴、价值属性、以及供应商考量中的认识。本论文借鉴了信息系统和商业管理中的理论模型以支撑提出的理论概念。研究结果本论文综合审视了IoT在酒店管理中的应用, 提出了一个输入-处理-输出的模型, 指出了其与大数据的关联。其相关价值维度通过IoT-大数据三重影响强度模型来显示。研究理论限制/意义本论文提出了对IoT使用的未来展望, 拓展了著名的SMAC模型（social, mobile, analytics, cloud）, 加入了IoT成分。研究实际意义本论文对IoT和大数据在酒店IT架构、商业模型、安保和标准化等方面的使用, 强调了其未来应用研究的方向。研究原创性/价值本论文通过酒店管理中的IoT应用综述, 以及其与大数据分析的关联, 展示了IoT、大数据、酒店覆盖等概念如何融合, 这方面的研究还未真正被开发。本论文对于酒店领域中的IoT和大数据应用以及相关领域的未来研究, 有着奠基作用。

An Adaptive Erasure Code for JointCloud Storage of Internet of Things Big Data

JointCloud is a cross-cloud cooperation architecture for integrated Internet service customization. The customized cross-cloud storage service based on this architecture is called JointCloud storage. Storing the Internet of Things (IoT) big data in erasure-coded JointCloud storage systems ensures that data can be accessed when several cloud services interrupt. However, because existing erasure codes cannot adapt the generator matrix and data placement scheme to different network environments and encoding parameters, they usually incur a large network resource consumption (NRC) for repairing data in JointCloud storage systems. As a result, the availability of IoT applications running on JointCloud storage systems is impaired. In this article, to minimize the NRC of repairing data, we propose an adaptive erasure code for JointCloud storage of IoT big data called ACIoT. Specifically, we first propose the concept of average weighted locality (AWL) of a stripe of erasure-coded data, which is proportional to the average NRC of repairing this stripe in JointCloud storage systems. Then, we propose an active parallel trial-and-error algorithm to calculate the optimal generator matrix and data placement scheme to achieve the lowest AWL, under different network environments and encoding parameters. By encoding and placing each stripe of data with the optimal generator matrix and data placement scheme, ACIoT can achieve the minimum NRC. The experiments show that, compared with several state-of-the-art erasure codes, ACIoT reduces the NRC by 26.4%–44.7%.

IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

Guest Editorial Special Issue on Enabling a Smart City: Internet of Things Meets AI

Future cities are to be not only an intelligent and green living environment but also provide human-centered public services at a lower cost. The Internet of Things (IoT) and artificial intelligence (AI) are two cornerstone technologies enabling the smart city concept, which are fusing into an organic whole in recent years. Some particular joint points where IoT meets AI are intelligent IoT devices, smart sensing boosted by AI, and IoT big data mining with AI. Such “IoT meets AI” trend is already casting significant impact to enable a smart city. Some examples are: smartphones are able to learn the touching pattern of users; home Wi-Fi router can intelligently detect and locate an intruder; vehicles locations and movement information can be exploited for traffic control; video cameras installed in the street can perform face recognition locally.

Application of Big Data Analysis and Internet of Things to the Intelligent Active Robotic Prosthesis for Transfemoral Amputees

With the advent and rising usage of Internet of Things (IoT) eco-systems, there is a consequent, parallel rise in opportunities where technology can find its place to improve a number of human conditions. However, this is nothing new - we have been perfecting the usage of tools to aid our daily living throughout history. The true evolution lies in the interaction between us and the tools we create. Tools are now smart devices, yielding an opportunity where human-device interaction is giving us the very knowledge on how to improve that particular synthesis. From improving our fitness to detecting bradycardia and response of traumatic brain injury patient, we have come to a point where we are able to gain actionable insight into a lot of aspects of our health and condition. This creates a certain autonomy in understanding the unique make-up of every single person, in addition to yielding information that can be used by health practitioners to help in diagnosis, determination of medical approach and right recovery and follow-up methods. All of this supported by two major factors: IoT platforms and Big Data Analysis (BDA). This paper takes a deep dive into exemplary set-up of IoT platform and BDA framework necessary to support the improvement of human condition. Our SmartLeg prosthetic device integrates advanced prosthetic and robotic technology with the state-of-the-art machine learning algorithms capable of adapting the working of the prosthesis to the optimal gait and power consumption patterns, which provide means to customize the device to a particular user.

Rate and Energy Efficiency Improvements for 5G-Based IoT With Simultaneous Transfer

Internet of Things (IoT) is facing the shortage of spectrum resources due to the rapid growth of IoT terminals and big data services. Fifth generation (5G) network owns sufficient spectrum resources and supplies large data volume business, which can help to expand the communication resources of the IoT by combing IoT with 5G network. In this paper, a 5G-based IoT is designed to transfer both 5G and IoT information simultaneously. Two simultaneous transfer models including time switching model and power splitting model are proposed to carry out 5G and IoT communications using different time slots and power streams, respectively. For these two models, we have formulated joint optimization problem of allocation factors and node powers to maximize the 5G transmission rate while the IoT transmission rate and the total power are constrained. An alternative optimization problem is also proposed to maximize the IoT transmission rate while guaranteeing the minimal 5G transmission rate. A joint optimization algorithm based on the Lagrange dual optimization is proposed to obtain the solution to the optimization problems. An energy efficiency model is proposed to minimize the consumed total power of the IoT while keeping the minimal 5G and IoT transmission rates. Simulation results are given to evaluate the performance of our proposed models from diverse perspectives.

Guest Editorial Special Issue on Advanced Computational Technologies in Mobile Edge Computing for the Internet of Things

Nowadays, for different purposes and contexts, we interact with a lot of different smart devices in our daily lives. Most of these devices are connected to the Internet and are therefore commonly referred to as the Internet of Things (IoT). Mobile edge computing (MEC) has recently evolved as an emerging technique by moving the computing and storage resources from the cloud to the edge of the network. The MEC supports IoT devices to improve their efficiency and scalability; helps to reduce latency delay for real-time applications, bandwidth bottlenecks, and energy consumption; and delivers contextual information processing. MEC offers many features and capabilities, such as access to a multitude of network interface (from 4G and 5G to Wi-Fi), support for device mobility, device context, geo-location awareness, and geographical distribution. Such attributes can support the real-time processing requirements of the Internet of Everything application, such as patient care, disaster management and detection (e.g., earthquakes), and flood monitoring. However, to fully exploit the potential of MEC in the IoT applications, many challenges need to be addressed, such as issues related to IoT Big Data, effective management of data storage and computing, privacy and security concerns, and innovative and emerging communication paradigm (e.g., 5G), require new architectures, applications, and methods.

IoT Big Data Security and Privacy Versus Innovation

In this paper, we address the conflict in the collection, use, and management of Big Data at the intersection of security and privacy requirements and the demand of innovative uses of the data. This problem is exaggerated in the context of the Internet of Things (IoT). We propose a three-part decomposition of the design space, in order to clarify requirements and constraints. To reach this final analysis, we begin by clarifying the challenges in the design space: 1) there is a little agreement on what is meant by IoT, and in particular the security and privacy implications of different definitions; 2) we then consider the requirement and constraints on the Big Data that result from various IoT system designs; and 3) in parallel, we examine the intricacies of the demand for innovation from both the legal and economic perspectives. In this context, we then can decompose the set of drivers and objectives for security/privacy of data as well as innovation into: 1) the regulatory and social policy context; 2) economic and business context; and 3) technology and design context. By identifying these distinct objectives for the design of IoT Big Data management, we propose that more effective design and control is possible at the intersection of these forces, through an iterative process of review and redesign.

A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues

The increasing population in the world forces humans to improve farm yields using advanced technologies. The Internet of Things (IoT) is one promising technique to achieve precision agriculture, which is expected to greatly increase yields. However, the large-scale application of IoT systems in agriculture is facing challenges such as huge investment in agriculture IoT systems and non-tech-savvy farmers. To identify these challenges, we summarize the applications of IoT techniques in agriculture in four categories: controlled environment planting, open-field planting, livestock breeding, and aquaculture and aquaponics. The focus on implementing agriculture IoT systems is suggested to be expanded from the growth cycle to the agri-products life cycle. Meanwhile, the energy concern should be considered in the implementation of agriculture IoT systems. The construction of green IoT systems in the whole life cycle of agri-products will have great impact on farmers' interest in IoT techniques. With the life cycle framework, emerging finance, operation, and management (FOM) issues in the implementation of green IoT systems in agriculture are observed, such as IoT finance, supply chain and big data financing, network nodes recharging and repairing, and IoT data management. These FOM issues call for innovative farm production modes and new types of agribusiness enterprises.

HybridIoT: Integration of Hierarchical Multiple Access and Computation Offloading for IoT-Based Smart Cities

The Internet of Things (IoT) is an emerging technology that proffers to connect massive smart devices together and to the Internet. On the basis of IoT, a smart city is endowed with real-time monitoring, ubiquitous sensing, universal connectivity, and intelligent information processing and control. An IoT-based smart city can offer various smart services to citizens and administrators, thus improving the utilization of public resources regarding transportation, healthcare, environment, entertainment, and energy. The integration of transmitting, computing, and caching is having a profound impact on the development of flexible and efficient IoT in smart cities. However, with the introduction of ultra dense networking (UDN) and mobile edge computing (MEC), we have to carefully consider a joint problem across the physical layer and MAC layer to enable the efficient transmission, computation, and caching of big IoT data generated by massive IoT devices distributed in a city. In doing so, efficient multiple access and computation offloading should be addressed in the physical layer and MAC layer, respectively. In this article, we propose a scalable and sustainable IoT framework that integrates UDN-based hierarchical multiple access and computation offloading between MEC and cloud to support the smart city vision. The proposed integrated framework can substantially reduce the end-to-end delay and energy consumption of computing data from massive IoT devices. Numerical comparison results are presented to show the efficiency of the proposed framework. In addition, we discuss a number of open research issues in implementing the proposed framework. Introduction

An optimized cluster storage method for real-time big data in Internet of Things

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.

SIoTFog: Byzantine-resilient IoT fog networking

The current boom in the Internet of Things (IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called “SIoTFog,” to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

A Novel Framework and Enhanced QoS Big Data Protocol for Smart City Applications.

Various heterogeneous devices or objects will be integrated for transparent and seamless communication under the umbrella of Internet of things (IoT). This would facilitate the open access of data for the growth of various digital services. Building a general framework of IoT is a complex task because of the heterogeneity in devices, technologies, platforms and services operating in the same system. In this paper, we mainly focus on the framework for Big Data analytics in Smart City applications, which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication to improve the quality of life of citizens. A novel approach is proposed in this paper to enhance energy conservation and reduce the delay in Big Data gathering at tiny sensor nodes used in IoT framework. To implement the Smart City scenario in terms of Big Data in IoT, an efficient (optimized in quality of service) wireless sensor network (WSN) is required where communication of nodes is energy efficient. Thus, a new protocol, QoS-IoT(quality of service enabled IoT), is proposed on the top layer of the proposed architecture (the five-layer architecture consists of technology, data source, data management, application and utility programs) which is validated over the traditional protocols.

IoT big data analytics for smart homes with fog and cloud computing

Internet of Things (IoT) analytics is an essential mean to derive knowledge and support applications for smart homes. Connected appliances and devices inside the smart home produce a significant amount of data about consumers and how they go about their daily activities. IoT analytics can aid in personalizing applications that benefit both homeowners and the ever growing industries that need to tap into consumers profiles. This article presents a new platform that enables innovative analytics on IoT captured data from smart homes. We propose the use of fog nodes and cloud system to allow data-driven services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. We discuss in this paper the requirements and the design components of the system. To validate the platform and present meaningful results, we present a case study using a dataset acquired from real smart home in Vancouver, Canada. The results of the experiments show clearly the benefit and practicality of the proposed platform.

IoT Based Decision Making System to Improve Veracity of Big Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.

Identifying Industry 4.0 IoT enablers by integrated PCA-ISM-DEMATEL approach

PurposeThe purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.Design/methodology/approachIoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).FindingsThe integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.Practical implicationsThe findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.Originality/valueThe integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

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

The technological advancements in the field of computing are giving rise to the generation of gigantic volumes of data which are beyond the handling capabilities of the conventionally available tools, techniques and systems. These types of data are known as big data. Moreover with the emergence of Internet of Things (IoT), these types of data have increased in multiple folds in 7Vs (volume, variety, veracity, value, variability, velocity and visualisation). There are several techniques prevalent in today’s time for handling these types of huge data. Hadoop is one such open source framework which has emerged as a de facto technology for handling such huge datasets. In an IoT ecosystem, real-time handling of requests is an imperative requirement; however, Hadoop has certain limitations while handling these types of requests. In this article, we present an energy-efficient architecture for effective, secured and real-time handling of IoT big data. The proposed approach adopts atrain distributed system (ADS) to construct the core architecture. This study uses software-defined networking (SDN) framework for energy-efficient and optimal routing of data and requests from source to destination, and vice versa. Furthermore, to ensure secured handling of IoT big data, the proposed approach uses ‘Twofish’ cryptographic technique for encrypting the information captured by the sensors. Finally, the concept of ‘request-type’ identifying unit has been proposed. Instead of handling all the requests in an identical way, the proposed approach works by characterising the requests on the basis of certain criteria and parameters, which are identified here.