Internet Of Things Data Research Articles

Securing IoT network with hybrid evolutionary lion intrusion detection system: a composite motion optimisation algorithm for feature selection and ensemble classification

ABSTRACT Networks connected to the Internet of Things (IoT) are often vulnerable to attacks. Several existing methods in the intrusion detection system for securing IoT have been presented with ensemble classifier, but it does not accurately classify attack, and also it takes high computation time. With intention of solving the security issues, Intrusion Detection System using Hybrid Evolutionary Lion and Balancing Composite Motion Optimisation Algorithm espoused feature selection with Ensemble Classifier (IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost) is proposed for Securing IoT Network. At first, data were accumulated from the NSL-KDD data set. Afterward, data is fed to pre-processing, where it restored missing value using mean curvature flow method. At feature selection, optimum features are compiled under Hybrid Evolutionary Lion and Balancing Composite Motion Optimisation Algorithm. Based upon the optimum features, intruders of IoT data are categorised as denial-of-service (DoS), probe, remote to local attack (R2L), user to root attack (U2R), normal (no attack) with the help of Ensemble classifier. Proposed IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost approach is constructed utilising Python. Then, proposed IDS-IoT-Hybrid ELOA-BCMOA-Ensemble-DT-LSVM-RF-XGBoost approach attains 21.11%, 19.58%, 24.61% and 9.52% higher accuracy; 94.47%, 93.95%, 93.08% and 90.59% lower error rate, and 62.94%, 36.69%, 64.17% and 50.97% less computation time analysed with existing models.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.

Optimizing energy‐efficient data replication for IoT applications in fog computing

SummaryThe rise of the Internet of Things (IoT) has given rise to an era marked by interconnected devices and substantial data generation. This has led to an increased reliance on cloud computing for data processing and storage, primarily due to its cost‐effective pay‐for‐use model. However, this dependence has prompted critical inquiries into the optimal replication of data: what data to replicate, when to replicate it, and where to place new replicas strategically. Conventional cloud data replication often results in resource overutilization, performance bottlenecks, increased workloads, energy consumption, prolonged user wait times, and suboptimal response times. In response to these challenges, this paper introduces a novel approach named Multiobjective Optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA). This approach employs multiobjective optimization (MOO) alongside Harris Hawks Optimization (HHO) and IoT‐based Salp Swarm Algorithm (SSA) for cloud computing environments. MOHHOSSA efficiently identifies data replication opportunities and strategically allocates them across nodes in cloud computing infrastructures. The algorithm aims to enhance key performance metrics, including energy consumption, carbon dioxide emission rate, and mean service time. Extensive experimental validation demonstrates MOHHOSSA's superior performance compared to alternative algorithms. It excels in optimizing energy efficiency, load distribution, mean service time, and the establishment of cost‐effective communication paths between nodes. This research represents a significant advancement in addressing challenges related to IoT data replication in cloud computing, ultimately promoting more sustainable, efficient, and responsive cloud‐based services.

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities.

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

Differential Privacy-Preserving IoT Data Sharing Through Enhanced PSO

ABSTRACT With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we propose a novel approach for achieving differential privacy in IoT data sharing through an improved Particle Swarm Optimization (PSO) algorithm. Our objective is to find the optimal configuration of privacy-preserving mechanisms that maximizes privacy while maintaining data utility. The research begins with a comprehensive overview of differential privacy in IoT data sharing, highlighting the limitations of existing optimization algorithms. We then present our proposed improvements to the traditional PSO algorithm, including the design of a suitable fitness function, the use of a dynamic inertia weight, exploration of different neighborhood topologies, and adaptive acceleration coefficients. We define a set of performance metrics, including privacy metrics (e.g. ε-differential privacy parameter) and utility measures (e.g. accuracy, utility loss), to assess the algorithm’s effectiveness. The results of our experiments demonstrate that the improved PSO algorithm achieves higher privacy guarantees while maintaining competitive levels of data utility compared to existing approaches. The proposed algorithm exhibits faster convergence, better exploration of the search space, and improved scalability. Our research contributes to the field of privacy-preserving IoT data sharing by providing an efficient and effective optimization algorithm that enables secure and privacy-aware data sharing while facilitating valuable insights and analysis.

Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things

Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative data aggregation models. The cloud edge computing continuum (CECC) has been proposed as an evolution of the traditional central ultra-high-end processing cloud into a continuum of collaborative processing elements distributed from the cloud to the network edge. Until today, incorporating existing centralized and monolithic LPWAN architectures in the CECC faces multiple security-related implications. We propose Edge2LoRa, a complete secure solution to incorporate LPWAN architectures in CECC enabling faster data processing while reducing the transmission of sensitive data. It improves network performance through data pre-processing, traffic flow optimization, and real-time local analysis. Edge2LoRa gradually transform existing LPWAN deployments into agile and versatile infrastructures that enable the seamless and efficient processing of data throughout the CECC while guaranteeing service continuity and full-backwards compatibility. We implement Edge2LoRa in hardware compliant with the Things Stack and the LoRaWAN v1.0.4 and v1.1. We evaluate the performance in terms of networking and computing resource utilization, quality of service and security. The results provide a clear indication of the improvements to public and private LoRaWAN infrastructures without any disruption or service degradation for existing legacy services. In public LoRaWAN deployments where large-scale IoT data streams drive big data analytics, we demonstrate core network bandwidth usage reductions of up to 90% and data processing latency improvements by a ×10 factor.

A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.

Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing.

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems.

Integrity and Privacy Assurance Framework for Remote Healthcare Monitoring Based on IoT

Remote healthcare monitoring (RHM) has become a pivotal component of modern healthcare, offering a crucial lifeline to numerous patients. Ensuring the integrity and privacy of the data generated and transmitted by IoT devices is of paramount importance. The integration of blockchain technology and smart contracts has emerged as a pioneering solution to fortify the security of internet of things (IoT) data transmissions within the realm of healthcare monitoring. In today’s healthcare landscape, the IoT plays a pivotal role in remotely monitoring and managing patients’ well-being. Furthermore, blockchain’s decentralized and immutable ledger ensures that all IoT data transactions are securely recorded, timestamped, and resistant to unauthorized modifications. This heightened level of data security is critical in healthcare, where the integrity and privacy of patient information are nonnegotiable. This research endeavors to harness the power of blockchain and smart contracts to establish a robust and tamper-proof framework for healthcare IoT data. Employing smart contracts, which are self-executing agreements programmed with predefined rules, enables us to automate and validate data transactions within the IoT ecosystem. These contracts execute automatically when specific conditions are met, eliminating the need for manual intervention and oversight. This automation not only streamlines the process of data processing but also enhances its accuracy and reliability by reducing the risk of human error. Additionally, smart contracts provide a transparent and tamper-proof mechanism for verifying the validity of transactions, thereby mitigating the risk of fraudulent activities. By leveraging smart contracts, organizations can ensure the integrity and efficiency of data transactions within the IoT ecosystem, leading to improved trust, transparency, and security. Our experiments demonstrate the application of a blockchain approach to secure transmissions in IoT for RHM, as will be illustrated in the paper. This showcases the practical applicability of blockchain technology in real-world scenarios.

DSChain: A Blockchain System for Complete Lifecycle Security of Data in Internet of Things

DSChain: A Blockchain System for Complete Lifecycle Security of Data in Internet of Things

IoT Forensic Cyber Activities Detection and Prevention with Automated Machine Learning Model

The Internet of Things (IoT) has been deployed in a vast range of applications with exponential increases in data size and complexity. Existing forensic techniques are not effective for the accuracy and detection rate of security issues in IoT forensics. Cyber forensic comprises huge volume constraints that are processing huge volumes of data in the Information and Communication Technology (ICT) comprised of IoT devices and platforms. Trust blockchain is effective technology those are utilized to assess the tamper-proof records in all transaction in the IoT environment. With the implementation of trust blockchain the record and transaction are processed with a distributed ledger that is managed by the network nodes. The challenge associated with the trust blockchain in IoT forensics is cost and security. To achieve significant cost-effectiveness organizations, need to evaluate the risks and benefits associated with IoT forensics in the trust blockchain technology. In this paper, developed a Block Chain Enabled Cyber-Physical system with distributed storage. The developed Blockchain model is termed as Integrated Hadoop Blockchain Forensic Machin Learning (IHBF-ML). The IHBF-ML model uses the Hadoop Distributed File System (HDFS) with cyberspace to improve security. Within the IHBF-ML model, IoT data communication is established with the smart contract. The smart contract-based blockchain process uses the Machine Learning model integrated with Cat Boost classification model for anomaly detection. Cost in IoT forensic is minimized with the parallel processing of the data through MapReduce Framework for the traffic translation, extraction, and analysis of the dynamic feature traffic from the IoT environment. The experimental analysis stated that constructed IHBF-ML model reduces the cost by ~25% than the other conventional blockchain Ethereum and EOS.

Modernizing sports an intelligent strategy for entertainment through internet of things in sports

A significant transformation towards a more engaging and entertaining viewer experience is provided by the incorporation of Internet of Things (IoT) innovation into the ever-changing world of current sports. With an emphasis on real-time tracking and assessment of athletic achievement, this study presents an extensive IoT-based strategy designed for the modern sports industry. Athlete tales, halftime displays and the general spectacle of big athletic events contribute to the entertainment value of the game. To provide a thorough assessment of athlete achievement in a timely manner, this research proposes a unique decision-making approach built on Adaptive Coral Reefs Optimized Xgboost (ACRO-XB). In particular, sport-specific characteristics are obtained through the use of electronic devices and an energy-effective procedure. To provide monitoring experts and competitors with efficient decision-making solutions, the ACRO-XB framework was developed. To validate the ACRO-XB approach, the simulation was run on a difficult dataset of 22,500 instances which is cricketers. Many modern analytical methods were used to undertake a comparison study. The simulation outcomes demonstrated that the ACRO-XB approach performed superior in conditions of recall, accuracy, f1-score and specificity by leveraging IoT data effectively for precise and timely athlete performance assessments, surpassing traditional methods in sports analytics. Furthermore, the suggested ACRO-XB approach was found to result in increased cell efficiency and stability.

IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node’s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm’s effectiveness compared to other benchmarks.

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. Quantum computing can enhance encryption algorithms to make IoT data more secure for intelligent IoT applications. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

COSIER: A comprehensive lightweight blockchain system for IoT networks

Internet of Things (IoT) networks have spread to many important fields and applications. The security of these networks remains a big challenge due to their unique characteristics and delicate operations. The blockchain has recently been proposed as a system to secure IoT data and communications. However, the blockchain demands a heavy load that cannot be handled by light IoT devices. For this reason, a large number of “lightweight blockchain” solutions were proposed that aim to modify the blockchain structure and characteristics to make it suitable for IoT networks. In this paper, we propose a novel and unique blockchain system for IoT networks that is based on four lightweight features: “lightweight architecture”, “lightweight authentication”, “lightweight consensus”, and “lightweight storage”. While previous research works focused on one or two of these features, we propose a system that combines the four features into a comprehensive lightweight blockchain system that utilizes a “lightweight cryptography” mechanism to achieve better performance from several perspectives, such as transaction latency, block generation delay, network overhead, and resilience to attacks.

Unlocking the Potential: Synergizing IoT, Cloud Computing, and Big Data for a Bright Future

The convergence and integration of the Internet of Things (IoT), Cloud Computing (CC), and Big Data (BD) offer huge potential for transformative progress that will support the massive industrial revolution that is so prevalent in today’s digital landscape. This prompts an exploration of the synergies among these three domains and investigates how their integration can unlock new opportunities for a brighter future. This integration seamlessly connects billions of IoT devices, leverages the potential of CC, and efficiently manages vast datasets generated by these devices. The amalgamation of BD analytics with IoT and CC empowers organizations to extract invaluable insights, foster data-driven decision-making, and fuel innovation across diverse industries. Nevertheless, the incorporation of these systems also gives rise to notable difficulties, encompassing issues such as data protection, worries about privacy, the ability to scale, and the management of data. The paper delves into these challenges, explores strategies, and examines the hurdles and best practices to address them effectively through a comprehensive examination of the potential benefits, challenges, and mitigation strategies. In addition, this paper offers insights into how synergizing IoT, CC, and BD can pave the way for a brighter, more promising future in technology and society.

Carbon emissions assessment of cement mixing piles for soft loess improvement and carbon emission reduction using white mud-cement composite material

During the highway construction in the soft loess region of Northwestern China, there are always tens of thousands of cement mixing piles were used. Which generates a large amount of carbon emissions, thereby affecting the environment. The Simplified Energy and Emissions Assessment Model (SEEAM) is used to quantify carbon emissions considering monitoring data of IoT (Internet-of-Things) system. To reduce the error of the carbon emissions of large samples, Monte Carlo simulation was used to quantify carbon emissions based on the small samples. The results showed that cement had a 47,530.78 t carbon missions contributing the 95.64 % of the total carbon emissions. To concurrently ensure strength and reduce carbon emissions, this study investigates the unconfined compressive strength variations in white mud-cement composite material solidified soft loess. It indicated that 10 % and 20 % white mud provided an alkaline environment and nucleation sites for cement hydration reactions, combining the filling voids. According to the observation results of TG and SEM, the content of 10 % white mud increased the quantity of early hydration product CSH in cement. Which maintained a high level of strength by comparing the cement solidified soft loess. In addition, when the white mud content is 50 %, the white mud-cement composite material reduced 576 % carbon emissions in the cement mixing pile engineering. This study can provide guidance for the selection of engineering construction design schemes, as low-carbon emission schemes are conducive to green and sustainable development of the environment.

Online learning and continuous model upgrading with data streams through the Kafka-ML framework

A pipeline of constant data streams is being built by the Internet of Things (IoT) to monitor information about the physical environment. In parallel, Artificial Intelligence (AI) is constantly developing and enhancing industrial, economic, and academic endeavors as well as quality of life thanks to these IoT data. In streaming contexts, Kafka-ML is our open-source framework that enables the management of Machine Learning (ML) and AI pipelines over data streams. Accordingly, it simplifies the deployment of Deep Neural Networks (DNNs) in practical applications. Nonetheless, this framework did not support the possibility of carrying out an Online Learning (OL) process, which is needed when new data are continuously arriving, and the models need to adapt to them on the fly. In this work, we have extended our previous work, the Kafka-ML framework, to enhance the management of ML/AI pipelines with OL features to enable both ML/AI distributed and centralized models to learn indefinitely over time. These models are continuously upgraded thanks to a process where automatic and flexible inference is carried out when improvements in the model performance are achieved. This opens up a large number of new possibilities within different fields of application, development, and work under the premise of incremental learning with ML models such as Electrical Vehicles and Industry 5.0. We have validated these new features by adapting and deploying state-of-the-art DNN models in different online scenarios, for both single and distributed configurations. The results show the capability of Kafka-ML to execute effective online training processes for ML models, improving their performance over time as new data becomes available.

Data collection in IoT networks: Architecture, solutions, protocols and challenges

AbstractThe Internet of Things (IoT) is the recent technology intended to facilitate the daily life of humans by providing the power to connect, control and automate objects in the physical world. In this logic, the IoT helps to improve our way of producing and working in various areas (e.g. agriculture, industry, healthcare, transportation etc). Basically, an IoT network comprises physical devices, equipped with sensors and transmitters, that are interconnected with each other and/or connected to the Internet. Its main objective is to gather and transmit data to a storage system such as a server or cloud to enable processing and analysis, ultimately facilitating rapid decision‐making or enhancements to the user experience. In the realm of Connected Objects, an effective IoT data collection system plays a vital role by providing several benefits, such as real‐time data monitoring, enhanced decision‐making, increased operational efficiency etc. However, because of the resource limitations linked to connected objects, such as low memory and battery, or even single‐use devices etc. IoT data collecting presents several challenges including scalability, security, interoperability, flexibility etc. for both researchers and companies. The authors categorise current IoT data collection techniques and perform a comparative evaluation of these methods based on the topics analysed and elaborated by the authors. In addition, a comprehensive analysis of recent advances in IoT data collection is provided, highlighting different data types and sources, transmission protocols from connected sensors to a storage platform (server or cloud), the IoT data collection framework, and principles for streamlining the collection process. Finally, the most important research questions and future prospects for the effective collection of IoT data are summarised.