Internet Of Things Data Processing Research Articles

Investigation on big data evaluation and visualization of internet of things based on edge computing

With the rapid development and popularization of Internet of Things (IoT) technology, a large number of IoT devices have generated massive amounts of data. In order to efficiently process and analyze this data and extract valuable information from it, the analysis and visualization of Big Data (BD) of the IoT based on Edge Computing (EC) has become a hot topic in current research. How to efficiently process and analyze these data has become a current research hot topic. Based on EC technology, this paper used a solution for BD analysis and visualization of the IoT. This paper used Sobel operator and edge extraction algorithm to analyze the BD analysis and visualization research of the IoT based on EC. Firstly, useful information in the image is extracted through the Sobel operator to improve image quality, in order to better understand and utilize image data; Then, edge extraction algorithms are used to quickly and accurately extract the edge information of the image for subsequent data processing and analysis. This scheme used EC nodes to preliminary process and analyze the data, reduced the burden of the central server, and improved the response speed and real-time performance. At the same time, this article also designed a visualization platform to display the analysis results in the form of charts, making it easy for users to intuitively understand the data situation. The experimental results showed that the score of data and information visualization results based on EC was between 89 and 95. The research results showed that the IoT BD analysis and visualization research method based on EC could effectively improve the efficiency and accuracy of IoT data processing and achieve more intuitive data display through experiments. BD analysis and visualization technology can rely on IoT devices to extract useful information and knowledge, and help users better achieve monitoring, control, and decision support for IoT devices.

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

Distributed transmission and optimization of relay-assisted space-air-ground IoT systems

This paper investigates the integration of relay-assisted Internet of Things (IoT) systems, focusing on the use of multiple relays to enhance the system performance. The central metric of interest in this study is system outage probability, evaluated in terms of latency. Our research provides a comprehensive analysis of system outage probability, considering different relay selection criteria to optimize the system’s transmission performance. Three relay selection strategies are employed to enhance the system transmission performance. Specifically, the first strategy, optimal relay selection, aims to identify the relay that minimizes the latency and maximizes the data transmission reliability. The second approach, partial relay selection, focuses on selecting a subset of relays strategically to balance the system resources and achieve the latency reduction. The third strategy, random relay selection, explores the potential of opportunistic relay selection without prior knowledge. Through a rigorous investigation, our paper evaluates the impact of these relay selection criteria on the performance of relay-assisted edge computing systems. By assessing the system outage probability in relation to latency, we provide valuable insights into the trade-offs and advantages associated with each selection strategy. Our findings contribute to the design and optimization of reliable and efficient edge computing systems, with implications for various applications, including the IoT and intelligent data processing.

Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT

The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for training models in a distributed manner while preserving data privacy on client devices. We contribute SAFI , a semi-asynchronous FL approach based on clustering to achieve a novel in-cluster synchronous and out-cluster asynchronous FL training mode. Specifically, we propose a three-tier architecture to enable IoT data processing on edge devices and design a clustering selection module to effectively group heterogeneous edge devices based on their processing capacities. The performance of SAFI has been extensively evaluated through experiments conducted on a real-world testbed. As the heterogeneity of edge devices increases, SAFI surpasses the baselines in terms of the convergence time, achieving a speedup of approximately × 3 when the heterogeneity ratio is 7:1. Moreover, SAFI demonstrates favorable performance in non-independent and identically distributed settings and requires lower communication cost compared to FedAsync. Notably, SAFI is the first Java-implemented FL approach and holds significant promise to serve as an efficient FL algorithm in IoT environments.

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.

Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage.

In the Internet of Things (IoT) era, the surge in Machine-Type Devices (MTDs) has introduced Massive IoT (MIoT), opening new horizons in the world of connected devices. However, such proliferation presents challenges, especially in storing and analyzing massive, heterogeneous data streams in real time. In order to manage Massive IoT data streams, we utilize analytical database software such as Apache Druid version 28.0.0 that excels in real-time data processing. Our approach relies on a publish/subscribe mechanism, where device-generated data are relayed to a dedicated broker, effectively functioning as a separate server. This broker enables any application to subscribe to the dataset, promoting a dynamic and responsive data ecosystem. At the core of our data transmission infrastructure lies Apache Kafka version 3.6.1, renowned for its exceptional data flow management performance. Kafka efficiently bridges the gap between MIoT sensors and brokers, enabling parallel clusters of brokers that lead to more scalability. In our pursuit of uninterrupted connectivity, we incorporate a fail-safe mechanism with two Software-Defined Radios (SDR) called Nutaq PicoLTE Release 1.5 within our model. This strategic redundancy enhances data transmission availability, safeguarding against connectivity disruptions. Furthermore, to enhance the data repository security, we utilize blockchain technology, specifically Hyperledger Fabric, known for its high-performance attributes, ensuring data integrity, immutability, and security. Our latency results demonstrate that our platform effectively reduces latency for 100,000 devices, qualifying as an MIoT, to less than 25 milliseconds. Furthermore, our findings on blockchain performance underscore our model as a secure platform, achieving over 800 Transactions Per Second in a dataset comprising 14,000 transactions, thereby demonstrating its high efficiency.

Application of internet of things data processing based on machine learning in community sports detection

Due to the rapid development of the IoT ecosystem, a large amount of IoT data has been generated. However, in the current development process of the Internet of Things, how to ensure data security is a key issue. Traditional IoT security protection is relatively basic. With the increase of data size, these facilities need to be further improved in terms of computing, information security, storage information, and stability. However, traditional security and privacy protection methods are often insufficient for large topology structures. Blockchain is based on distributed networks, characterized by monotonous and decentralized data. Blockchain also provides a new approach to encrypting IoT security information. This article introduces blockchain technology into the Internet of Things, which can prevent reliance on centralized servers and effectively protect internet data and information security. Then this article also analyzed the motion recognition and detection of community sports, as well as the widespread application of computer vision and machine learning technologies in various fields such as computer mining and data security. Motion detection technology is very important in the field of computer vision and has applications in various real-world scenarios. This article studies new ideas for information encryption in the computer Internet of Things, and also analyzes machine learning and motion detection systems and applies them to community sports, improving the development of community sports.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Tamper-proof access control for IoT clouds using enclaves

Internet of Things (IoT) devices rely on cloud computing for processing user-sensitive data, like health recordings and geolocalization. In this case, security primitives like cryptography and certificate-based authentication does not prevent the cloud provider from acting against the privacy policy. This paper presents a framework for clouds to execute arbitrarily complex processing tasks over IoT data while maintaining the access control policies over the client’s control. We rely on a memory enclave to enforce that the cloud follows personal and customizable access policies and analyzed the security properties of our scheme. The performance evaluation reveals that these robust security improvements come with a latency overhead of just 0.1 ms, confirming the system’s viability. The system leverages multi-threaded processing inside an enclave to process thousands of client messages per second, achieving high scalability. This work also contributes with a microbenchmark that identifies how much each step of an enclave application influences the performance and evaluates the enclave viability for performing realistic IoT data processing.

A Review on Fog Computing: Research Challenges and Future Directions

The traditional centralized cloud computing paradigm confronts several problems, including a lack of capacity, high latency, and network failure, due to the rapid expansion of IoT (Internet of Things) applications. Fog Computing brings cloud computing and IoT devices closer to addressing these issues. Instead of transferring IoT data to the cloud, the Fog allows local processing and storage of IoT data in IoT devices. Fog Computing offers services with a quicker response time and higher quality than the cloud. Therefore, Fog Computing may be considered the greatest option for enabling the IoT to provide efficient and secure services to many IoT users. In this paper, we define the term "Fog Computing," examine its architecture and list its features. We also talk about other related work and emphasize it.

Blockchain Application Analysis Based on IoT Data Flow

In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given.

KFIML: Kubernetes-Based Fog Computing IoT Platform for Online Machine Learning

The massive onsite data produced by the Internet of Things (IoT) can bring valuable information and immense potentials, thus empowering a new wave of emerging applications. However, with the rapid increase of onsite IoT data streams, it has become extremely challenging to develop a scalable computing platform and provide a comprehensive workflow for processing IoT data streams with lower latency and more intelligence. To this end, we present a Kubernetes-based scalable fog computing platform (KFIML), integrating big data streaming processing with machine learning (ML)-based applications. We also provide a comprehensive IoT data processing workflow, including data access and transfer, big data processing, online ML, long-term storage, and monitoring. The platform is feasibly validated on a clustered testbed, which comprises a master node, IoT broker servers, worker nodes, and a local database server. By leveraging the lightweight orchestration system, namely Kubernetes, we can readily scale and manage containerized software frameworks on our testbed. The big data processing layer utilizes the advanced data flow frameworks such as Apache Flink, to support both streaming processing and statistical analysis with low latency. In addition, the specified long short-term memory (LSTM)-based ML pipelines are employed on the online ML layer, to enable the real-time predictive analysis of IoT data streams. The experiments on a real-world smart grid use case demonstrate that the container-based KFIML platform can be well-scaled with Kubernetes to efficiently perform big data processing increased onsite IoT data streams with lower latency and conduct ML-based applications.

An Adaptive Modeling and Performance Evaluation Framework for Edge-Enabled Green IoT Systems

The enormous growth in Internet of Things (IoT) has caused large-scale transformation in data acquisition and communication mechanism for conventional IoT systems. The continuously increasing requirements for delay-tolerant delivery of services in IoT applications has led to the emergence of more scalable and energy-efficient computing platforms like edge computing. However, the massive growth in volume of data being offloaded from low-powered IoT devices to the edge has imposed challenges on edge servers in terms of traffic bottlenecks, latency, and wastage of energy. In this view, a Local Data Reduction (LDR) framework is proposed which addresses the latency issues and cost constraints to facilitate energy-efficient processing of IoT data. We exploit the Markovian birth-death process to model edge-based IoT systems and derive performance metrics for the proposed LDR model. We also provide explicit analytical solution for the total expected cost function incurred pertaining to the LDR and without LDR (WLDR) models. Through extensive numerical illustrations we validate our findings and observe that the proposed LDR model outperforms the WLDR model. Hence, the LDR model operates well to meet the Quality of Service (QoS) requirements for real-time IoT systems by favouring green computing paradigms.

Efficient pipelined flow classification for intelligent data processing in IoT

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things (IoT). In recent years, researchers have tried to develop hardware-based solutions for the classification of Internet packets. Due to higher throughput and shorter delays, these solutions are considered as a major key to improving the quality of services. Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput. The proposed architectures, however, cannot reach a compromise among power consumption, memory usage, and throughput rate. In view of this, the architecture proposed in this paper contains a pipeline-based micro-core that is used in network processors to classify packets. To this end, three architectures have been implemented using the proposed micro-core. The first architecture performs parallel classification based on header fields. The second one classifies packets in a serial manner. The last architecture is the pipeline-based classifier, which can increase performance by nine times. The proposed architectures have been implemented on an FPGA chip. The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput. The architecture has a power consumption of is 1.294w, and its throughput with a frequency of 233 ​MHz exceeds 147 Gbps.

Securing IoT-Empowered Fog Computing Systems: Machine Learning Perspective

The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. Software and communication technology have advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a new era of intelligent devices. However, the rapid expansion has raised security concerns. Another challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric strategy results from network congestion, data bottlenecks, and longer response times to security threats. Fog computing addresses these difficulties by bringing computation to the network edge. The current research provides a comprehensive review of the IoT evolution, Fog computation, and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog computing security concerns. Additionally, it covers future research objectives in the crucial field of IoT security.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

Smart Garden Planning and Design Based on the Agricultural Internet of Things.

To improve the effect of urban agricultural garden landscape planning and design, this paper combines the agricultural Internet of Things technology to construct a smart garden planning and design system. Moreover, this paper selects the LEACH protocol that can support monitoring for a long time according to actual application needs, introduces the latest swarm intelligence optimization algorithm, the gray wolf algorithm, to optimize some of the problems in the LEACH protocol, and conducts simulation experiments on the improved algorithm. The simulation experiment results show that the improved algorithm has obvious advantages in cluster head selection, data transmission within the cluster, and route maintenance. After constructing a smart garden planning system based on the agricultural Internet of Things, the effect of the agricultural Internet of Things data processing in this paper is evaluated. Finally, this paper constructs a garden simulation system and analyzes the performance of the system. The results verify that the agricultural Internet of Things has a good effect in the planning and design of smart gardens.

Overview of Present-Day IoT Data Processing Technologies

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution.

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.

Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks

Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on high-rate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There’s a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88%. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks.