Internet Of Things Big Data Research Articles

Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

A Survey on IoT Big Data Analytic Systems: Current and Future

The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices, such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT big data</i> . The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop- and Spark-based batch processing systems for spatiotemporal and trajectory data. We also review fog- and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability. We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.

Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology

The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform

The use of edge computing-based internet of things big data in the design of power intelligent management and control platform

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

The current exponential advancements in the Internet of Things (IoT) technologies pave a vast intelligent computing platform by integrating smart objects with sensing, processing and communication capabilities. The core element of IoT is the complex big data generated from different interconnected sources at real-time, presenting divergent processing and analysis challenges. Best practices in software engineering have been continuously addressed in IoT technologies to handle such big data efficiently at different domains. Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in IoT. This paper provides a systematic literature review for the current state-of-the-art of IoT systems in different domains. The study investigates the current techniques and technologies that serve IoT systems from the big data analytics and software engineering perspectives, revealing a matrix for the specific IoT data features and their encountered challenges and gaps for each domain. The review deduces a proposed domain-independent software architecture for big IoT data analytics, maintaining various IoT data processing challenges, including data scalability, timeliness, heterogeneity, inconsistency, confidentiality and correlations. Finally, the main research gaps are emphasized for future considerations.

A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

ADCC: An effective adaptive duty cycle control scheme for real time big data in Green IoT

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme.

Special Issue on Green Internet of Things: Challenges and Future Opportunities—Part II

Internet of Things (IoT) is a key enabler for many modernized applications from marine monitoring to outer space exploration. However, the complicated operations (such as device interconnection, data transmission, and service optimization) will consume substantial energy in contrast with limited energy storage of IoT devices. To improve architectural sustainability and ultimately reduce systemic cost, the greenness design of IoT has become ever prominent. Particularly, with the continuous penetration of advanced information and communications (ICT) technologies (such as VR/AR, UAVs, and automobiles), our smart world is being surrounded by big IoT data that extremely craves for energy-efficient caching, computing, networking and securing.

Fault Diagnosis of Power IoT System Based on Improved Q-KPCA-RF Using Message Data

As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through Q learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).

Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications.

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.

Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0

When studying the vehicle routing problem, especially for on-time arrivals, the determination of travel time plays a decisive role in the optimization of logistics companies. Traffic Internet of Things (IoT) connects ubiquitous devices and collects data from various channels like traffic cameras, vehicle detectors, GPS, sensors, etc. that can be used to analyze real-time traffic status and eventually increase the efficiency of logistics management for Logistics 4.0. However, big IoT data contain joint features that interact non-linearly and complicatedly, thus increasing the stochastic nature and difficulty of determining travel time on real-time basis. This research proposes a novel method (named the gradient boosting partitioned regression tree model) to forecast travel time based on big data collected from the industrial IoT infrastructure. The proposed method separates the global regression tree model based on the gradient boosting decision tree into several partitions to capture the time-varying features simultaneously – that is, to subdivide the non-linearity into fragments and to characterize the feature interactions in a manageable way with recursive partitions. We illustrate several analytical properties with manageable advantages in terms of big data analytics of the proposed method and apply it to real traffic IoT data. Findings of this research show that the proposed method performs successfully at enhancing the predictive accuracy of travel time after empirically comparing it with other computational methods.

Assessing the Complexity of Intelligent Parks’ Internet of Things Big Data System

Today, intelligence in all walks of life is developing at an unexpectedly fast speed. The complexity of the Internet of Things (IoT) big data system of intelligent parks is analyzed to unify the information transmission of various industries, such as smart transportation, smart library, and smart medicine, thereby diminishing information islands. The traditional IoT systems are analyzed; on this basis, a relay node is added to the transmission path of the data information, and an intelligent park IoT big data system is constructed based on relay cooperation with a total of three hops. Finally, the IoT big data system is simulated and tested to verify its complexity. Results of energy efficiency analysis suggest that when the power dividing factor is 0.5, 0.1, and 0.9, the energy efficiency of the IoT big data system first increases and then decreases as α0 increases, where the maximum value appears when α0 is about 7 J. Results of outage probability analysis demonstrate that the system’s simulation result is basically the same as that of the theoretical result. Under the same environment, the more hop paths the system has, the more the number of relays is; moreover, the larger the fading index m, the better the system performance, and the lower the outage possibility. Results of transmission accuracy analysis reveal that the IoT big data system can provide a result that is the closest to the actual result when the successful data transmission probability is 100%, and the parameter λ values are between 0.01 and 0.05; in the meantime, the delay of successful data transmission is reduced gradually. In summary, the wireless relay cooperation transmission technology can reduce the outage probability and data transmission delay probability of the IoT big data system in the intelligent park by adding the multihop path, thereby improving the system performance. The above results can provide an experimental basis for exploring the complexity of IoT systems in intelligent parks.

Improved Random Forest Algorithm Performance For Big Data

In this paper, the effectiveness of using random forest algorithm in the big data is studied. The reason for choosing this algorithm is because of its effective results in many previous studies, so it was chosen. The random forest algorithm was applied to the big data Internet of Things (IoT) dataset, with size 150,000 instances. After applying the algorithm, it gave an Accuracy score of 99.976%, and this indicates the effectiveness of the random forest algorithm in the big data. This result made the researcher search for one of the ways that increases the value of Accuracy, knowing that it is an excellent result. And use a filter to remove frequent values, and this helps reduce data volume and keep related data. After applying the filter with the random forest algorithm, the Accuracy value appeared at 99.998%, which indicates that it improved the random forest algorithm’s performance in the big data.

Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology

Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology

Adaptive Cognitive Management and Knowledge Discovery Framework Based on Internet of Things Big Data

In the future of large-scale industrial automation applications in the Internet of Things big data management and knowledge discovery, the importance of the industrial Internet is increasing day by day. The Internet of Things (IoT), computing intelligence, machine type communication, big data and sensor technology and other diversification. Technology can be combined to improve the efficiency of data management and knowledge discovery for large-scale automated applications. To this end, we need to propose a cognitive-oriented IoT big data framework (COIB-framework) and its implementation architecture, IoT big data hierarchical architecture, data organization and knowledge exploration subsystems to achieve effective data management and knowledge discovery suitable for large-scale industrial automation applications. In this work, we need to propose a cognitive-oriented IoT big data framework (COIB-framework), as well as implementation architecture, IoT big data layered architecture, data organization and knowledge mining subsystems, to meet the needs of large-scale industrial automation applications. Discussion and analysis show that the proposed framework and architecture are based on the intelligent industrial application of IoT big data provides a reasonable solution.

AIOT-Arch: Furthering Artificial Intelligence in Big Data IoT Applications

Applications for the Internet of Things (IoT) have evolved in excessive numbers, producing a vast amount of data needed for intelligent processing. The varying IoT infrastructures such as cloud and IoT application layer protocol limitations in the transmission/receiving of messages become the barriers in the implementation of intelligent IoT apps. In this paper, we review the importance of Big data, cloud computing and fog computing in IoT and the challenges of using machine learning in IoT. Finally, we discuss the general statistics of using artificial intelligence in IoT applications.

Reliable Federated Learning Systems Based on Intelligent Resource Sharing Scheme for Big Data Internet of Things

Federated learning (FL) is the up-to-date approach for privacy constraints Internet of Things (IoT) applications in next-generation mobile network (NGMN), 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation (5G), and 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation (6G), respectively. Due to 5G/6G is based on new radio (NR) technology, the multiple-input and multiple-output (MIMO) of radio services for heterogeneous IoT devices have been performed. The autonomous resource allocation and the intelligent quality of service class identity (IQCI) in mobile networks based on FL systems are obligated to meet the requirements of privacy constraints of IoT applications. In massive FL communications, the heterogeneous local devices propagate their local models and parameters over 5G/6G networks to the aggregation servers in edge cloud areas. Therefore, the assurance of network reliability is compulsory to facilitate end-to-end (E2E) reliability of FL communications and provide the satisfaction of model decisions. This paper proposed an intelligent lightweight scheme based on the reference software-defined networking (SDN) architecture to handle the massive FL communications between clients and aggregators to meet the mentioned perspectives. The handling method adjusts the model parameters and batches size of the individual client to reflect the apparent network conditions classified by the k-nearest neighbor (KNN) algorithm. The proposed system showed notable experimented metrics, including the E2E FL communication latency, throughput, system reliability, and model accuracy.

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

An innovative financial risk early warning model based on the Internet of Things (IoT) big data technology is proposed to maintain the long-term stable development of Internet finance. The Internet credit finance is introduced, a financial risk identification method based on the big data technology is proposed, and a risk assessment method for Internet finance based on back propagation neural network (BPNN) is put forwarded in this study. The actual Internet financial data is selected for verification and analysis. The results reveal that the identification and prediction model of Internet credit finance risk based on big data technology can realize risk analysis of online credit in the Internet credit finance, and can give corresponding credit ratings to new customers on credit platforms. The training error is the lowest when the number of hidden layer nodes is 14; and the training error is the smallest when the learning rate is 0.06; Based on the BPNN, it can accurately assess the financial risks of the Internet credit platform, and the accuracy of its risk level prediction has reached 100%. This study can provide a theoretical basis for the application of IoT big data technology and neural network models in the financial field, and also give an important reference for the risk management of Internet finance.