IoT Data Streams Research Articles

An integrated federated learning with CRSO of attention-based LSTM framework for efficient IoT DataStream prediction

An integrated federated learning with CRSO of attention-based LSTM framework for efficient IoT DataStream prediction

Fft-asvr: an adaptive approach for accurate prediction of IoT data streams

Fft-asvr: an adaptive approach for accurate prediction of IoT data streams

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

Prescriptive Analytical Models for Dynamic IoT Data Streams: A Review

Adaptable and Interpretable Framework for Anomaly Detection in SCADA-based industrial systems

In this paper, we introduce an Adaptable and Interpretable Framework for Anomaly Detection (AID) designed for industrial systems utilizing IoT data streams on top of well-established SCADA systems. AID leverages dynamic conditional probability distribution modeling to capture the normal operation of dynamic systems and isolate the root causes of anomalies at the level of individual inputs. The self-supervised framework dynamically updates parameters of underlying model, allowing it to adapt to non-stationarity. AID interprets anomalies as significant deviations from conditional probability, encompassing interactions as well as both spatial and temporal irregularities by exposing them as features. Crucially, AID provides dynamic operating limits to integrate with existing alarm handling mechanisms in SCADA-based IoT systems. Two industrial-scale case studies demonstrate AID’s capabilities. The first study showcases AID’s effectiveness on energy storage system, adapting to changes, setting context-aware limits for SCADA, and ability to leverage a physics-based model. The second study monitors battery module temperatures, where AID identifies hardware faults, emphasizing its relevance to energy storage safety. A benchmark evaluation on real data shows that AID delivers comparable performance to other self-learning adaptable anomaly detection methods, with the significant advancement in diagnostic capabilities for improved system reliability and performance.

Real-time processing and optimization strategies for IoT data streams

Abstract With the development of industrial IoT and the arrival of smart manufacturing, the field of edge computing has gained more and more attention. However, traditional industrial computing scenarios relying on industrial clouds make data latency a greater challenge. In this paper, for the contradiction between edge devices and task resource allocation encountered in edge computing scenarios in smart manufacturing, we propose an industrial internet task scheduling model for smart manufacturing and introduce a scheduling node state matrix to realize the state management of each scheduling subtask. Aiming at the problem of multiple tasks seizing resources in a complex, intelligent manufacturing environment, the study combines the caching mechanism to realize the task offloading computational processing of order scheduling, in which the caching mechanism is used to solve the problem of computational resource limitations at the edge. It is found through simulation that when the computational task factor ξk =2 is larger, more offloading power is allowed to be transmitted to the edge ser ver for computation. For computational tasks with smaller task factor ξk , the device tends to allocate more computational rate to that computational task. Eventually the data queue length will be continuously reduced and the data queue is concentrated in the interval of very small values, this result verifies that the task scheduling algorithm is able to perform task scheduling efficiently and reduce the latency.

OpenTwins: An open-source framework for the development of next-gen compositional digital twins

Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving complex digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization, which are rarely seamlessly aligned in open-source solutions. In this paper, we present an open-source framework for the development of compositional digital twins, i.e., advanced digital twins that link individual entities or subsystems to create a higher degree digital twin, allowing knowledge sharing and data relationships. In this open framework, digital twins can be easily developed and orchestrated with 3D-connected visualizations, IoT data streams, and real-time machine-learning predictions. To demonstrate the feasibility of the framework, a use case in the Petrochemical Industry 4.0 has been developed.

A big data exploration approach to exploit in-vehicle data for smart road maintenance

In modern Smart Cities, pervasive collection of sensor-based and IoT data streams is a challenging opportunity for improving mobility resilience. Among the potential applications, sensor-based data streams provide valuable information about the quality of the area-wide road surface. Modern vehicle black boxes are also able to estimate the type of anomaly (e.g., bump, hole, rough ground, depression), based on real-time analysis of acceleration data streams. Road maintainers may use all this information to improve monitoring and maintenance activities. However, the volume of data streams, the variety of road network and different degrees of seriousness of detected anomalies call for methods to support maintainers in the exploration of available data. To this aim, in this paper, we propose a methodological approach, based on big data exploration techniques. The approach is grounded on: (i) a multi-dimensional model, apt to organise data streams according to different dimensions and enable data exploration; (ii) data summarisation techniques, based on an incremental clustering algorithm, to simplify the overall view over massive data streams and to cope with their dynamic nature; (iii) a measure of relevance, to focus the attention on road portions that present critical conditions. The innovative contributions regard the formalisation of the exploration methodology, the definition of exploration scenarios, based on road maintainers’ goals and the measure of relevance, and an extensive experimentation on a real world case study, addressed in a research project on smart and resilient mobility. Experimental results show how relevance evaluation is able to efficiently attract the road maintainers’ attention on road portions that present the most critical conditions and the proposed incremental clustering algorithm outperforms existing algorithms in the literature.

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.

Multiobjective Data-Driven Production Optimization With a Feedback Mechanism

Operation recommendations and automation for various industries rely on optimization over the top of the real-time IoT data streams. Such recommendation applications have become imperative for industrial manufacturing, such as petroleum, chemicals, and food processing. We build a novel system of user-in-the-loop multi-objective optimization under the initial uncertainty of the optimization objectives, wherein the uncertainty is iteratively resolved via user feedback. We propose an interactive optimization system wherein both business and operational goals become defined as the optimization processes and where objective selection is incorporated as part of the optimization procedure. We show that such a solution exists during the iteration process if the feasibility space is not empty initially and is constrained by industry operations. Using an oil sands application, we demonstrate this approach and compare modeling results in values, response to business and operational priorities, and interpretability to the weighted sum optimization.

Preface

The 2022 4th International Conference on Wireless Communications and Smart Grid (ICWCSG 2022) was successfully held on August 19-21, 2022 in Hulunbuir, China in the form of online conference. ICWCSG 2022 promoted research and developmental activities in wireless communications and smart grid as well as scientific information interchange between researchers, engineers, and practitioners working all around the world.We were delighted and honored to invite both Prof. Jizhong Zhu from South China University of Technology, China and Prof. Ting Yang from Tianjin University, China to serve as our General Conference Chairs. There were 90 individuals and enterprises attending the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors by turns.During the conference, seven sophisticated professors were invited to address keynote speeches. Among them, Prof. Tao Zhang from North China University of Technology, China delivered a speech on the topic: Application of Urban Brain in Data Center. In this report, he explained that urban brain is an artificial intelligence development, innovation and operation platform based on cloud computing, big data, artificial intelligence and the new generation of information technology of the Internet of things. Additionally, Prof. Chuanming Liu from National Taipei University of Technology (NTUT), China gave a report on the Uncertain Data Management over IoT Data Streams in Edge Computing. The wireless sensor networks to the Internet of Things (IOT) with cloud computing is one of the examples that modern computing environments tend to be decentralized and autonomous. In this presentation, he introduced the trends of emerging computing environments and the promising edge cloud systems with IoT applications. The wonderful and dramatic speeches of each keynote speaker had triggered heated discussion. Moreover, every participant praised this conference for disseminating useful and insightful knowledge.After months of well preparation and hard work, the proceedings of ICWCSG 2022 covering a bunch of papers is smoothly published. These papers feature but are not limited to the following areas: Signal Processing & Applications, Optical Communications and Networking, Microwave Engineering, Communications Networks and Systems, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.On behalf of the conference organizing committee, we would like to express our heartfelt appreciation to all the keynote speakers, peer reviewers, and all the participants. In particular, we would like to acknowledge the Journal of Physics: Conference Series, for the endeavor of all the colleagues in publishing this paper volume. We firmly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.List of Committee member is available in this Pdf.

Fog Data Processing and Analytics for Agriculture IoT Data Streams

Pervasiveness rise of smart devices and sensor-based gadgets in building IoT systems is increasing unprecedented in technological innovations. The emergence of the Internet of Things technologies reshaped nearly every sector including agriculture. The agriculture sector contributes a significant figure to the country’s economy and it has a wide-ranging involvement in the advancement of human civilization. In the current scenario, the proper techniques of farming are needed as utmost propriety for better crop quality and quantity in a high-competition market. Crop disease prediction is key to shattering the problems of the farmer, reducing the usage of insecticides, and pesticides, and improving the financial conditions of the farmer. The Internet of Things and data analytics possess the ability to positively modernize the agricultural sector. However, Internet of Things-based applications needed to be deployed on a platform that offers real-time experience, reduced latency, and optimal bandwidth usage. Fog computing extends the computational power closer to the edge network where the devices reside and facilitate edge intelligence. In this paper, the fog importance, fog computing architecture along with the perceptions of data analysis in a fog environment, and emerging research challenges are discussed. Furthermore, in this paper an IoT-Fog based framework for the prediction of crop disease is proposed. The proposed framework consists of four phases, sensor layer, fog layer, cloud layer and End-user. The proposed framework may be beneficial in the farming domain for the analysis of crop disease prediction in the early stage and may reduce the chances of disease outbreaks in the field

Machine-Learning Assisted Prediction of Light Consumption

IoT devices enable an assortment of large amounts of information from the environment that may well be used to produce sensible autonomous buildings. For example, within the context of public buildings like hospitals, it's necessary to regulate and predict the indoor environment. Just in case of emergencies or failures, the facility is going to be able to automatically regulate the indoor parameters by using models that learn from the previous usage. This can allow us to acquire advantage of the IoT sensor data and to understand real-time control of the building. The information stream will be unendingly processed and analyzed on-premises. Anomalies can be monitored, and alarms can be triggered accordingly. The objective of this research paper is to construct a classifier to predict the usage of light from the IoT information stream. One manner might be to implement a cloud-based device where all IoT sensors send measurements to an endpoint on the cloud for storage and training of the model. Proposed model will process the IoT data stream and construct a classifier that will be able to accurately predict the target label. The target label for use case is “light.” The research study goal is to suit a model that may predict if the light is on or off and to determine the accuracy of the predicted model using Hoeffding and Naïve Bayes classifier.

Probabilistic Hesitant Fuzzy Methods for Prioritizing Distributed Stream Processing Frameworks for IoT Applications

Distributed stream processing frameworks (DSPFs) are the vital engine, which can handle real-time data processing and analytics for IoT applications. How to prioritize DSPFs and select the most suitable one for special IoT applications is an open issue. To help developers of IoT applications to solve this complex issue, a novel probabilistic hesitant fuzzy multicriteria decision making (MCDM) model is put forward in this paper. To characterize the requirements for large-scale IoT data stream processing, a novel evaluation criteria system including qualitative and quantitative criteria is established. To accurately model the collective opinions from skilled developers and consider their psychological distance, the definition of probabilistic hesitant fuzzy sets (PHFSs) is used. To derive the importance degrees of criteria, a novel probabilistic hesitant fuzzy best-worst (PHFBW) method is proposed based on the score value. To prioritize the DSPFs and choose the most suitable one, a novel probabilistic hesitant fuzzy MULTIMOORA method is put forward. Finally, a practical case composed of four Apache stream processing frameworks, namely, Storm, Flink, Spark, and Samza, is studied. The obtained results indicate that throughput, latency, and reliability are considered to be the three most important criteria, and Flink is the most suitable stream framework.

An Architecture for QoS-Aware Fog Service Provisioning

Abstract The proliferation of IoT technologies and the broad deployment of sensors and IoT devices are changing the way and the speed of delivery of many services. IoT data streams are typically transmitted to cloud services for processing. However, time-sensitive IoT applications cannot tolerate high latency they may experience when IoT data streams are sent to the cloud. Fog computing-based solutions for this kind of applications are becoming more and more attractive due to the low latency they can provide and guarantee. Given the growing deployments of fog nodes, we propose in this paper an architecture for quality of service (QoS) aware fog service provisioning, which allows to schedule the execution of IoT applications’ tasks on a cluster of fog nodes. A fog broker component can implement various scheduling policies to help IoT applications fulfill their QoS requirements. The results of the simulations we performed show that using some simple strategies, it is possible to keep low the latency of applications and distribute the load among the fog nodes of the cluster.

Missing-Value Imputation of Continuous Missing Based on Deep Imputation Network Using Correlations among Multiple IoT Data Streams in a Smart Space

Missing-Value Imputation of Continuous Missing Based on Deep Imputation Network Using Correlations among Multiple IoT Data Streams in a Smart Space

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U= {0,1,⋯,N-1} and answer an emptiness query of the form “S∩[a;b]=0?” for an interval [a;b] of length L (a,b∈U), with a false positive rate e. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by e-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for e-ARESD-problem is n log 2 (nL/e)+Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.

Survey of Real-time Processing Technologies of IoT Data Streams

Survey of Real-time Processing Technologies of IoT Data Streams