The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods (original) (raw)

Machine learning and data analytics for the IoT

Neural Computing and Applications, 2020

The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

Machine Learning Powered IoT for Smart Applications

2021

With the coming of fast advancements, with the assistance of IoT, a great percentage of heterogeneous devices can be connected with each other. The technology with the relationship of different devices through the internet is named the internet of things (IoT), makes a wide number of different characteristics and qualities of data. IoT and Machine learning (ML) guarantees the widespread advancement to grow the insights of the IoT devices and applications. Over the final few years, artificial intelligence and machine learning have advanced very significantly. It allows a machine or system to learn more effectively than people learn on their own. When we learn some kind of system about the concept of our trial or the knowledge obtained after evaluating it. Combining IoT with rapidly advancing ML technologies can make 'smart machines' that mimic smart action to do well-informed resolve with little or no human involvement. There are at least two fundamental reasons, why machine ...

Application of Machine Learning in Internet of Things (IoTs

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Machine learning is a subset of artificial intelligence in which machines learn through real data rather than being explicitly taught to do so. With the growing number of devices on business networks, IT teams are finding it difficult to design adequate security procedures. IoT security is crucial for corporate survival and success, but it comes with its own set of issues. Some of these issues can be addressed using a machine learning (ML) strategy to IoT security. It eliminates the problem of recognizing unknown devices on a network, ensures that they are included in the current security framework, and simplifies IoT management for busy IT teams. In this paper, application of the Machine Learning algorithms in IoT sectors are explored.

The use of machine learning in the Internet of Things

ITM Web of Conferences

In the Internet of Things and wireless sensor networks period, a large number of connected objects and seeing bias are devoted to collecting, transferring, and inducing a huge quantum of data for a wide variety of fields and operations. To effectively run these complex networks of connected objects, there are several challenges like topology changes, link failures, memory constraints, interoperability, network traffic, content, scalability, network operation, security, and sequestration to name many. therefore, overcoming these challenges and exploiting them to support this technological outbreak would be one of the most pivotal tasks of the ultramodern world. Recently, the development of Artificial Intelligence(AI) led to the emergence of Machine Learning (ML), which has become the crucial enabler to figure out results and literacy models in an attempt to enhance the quality of service parameters of Internet of Things and wireless sensor networks. By learning from one gest, ML ways...

Rapid prototyping IoT solutions based on Machine Learning

Proceedings of the European Conference on Cognitive Ergonomics 2017 - ECCE 2017, 2017

Nowadays M achine Learning (M L) has reached an all-time high, and this is evident by considering the increasing number of successful start-ups, applications and services in this domain. M L techniques are being developed and applied to an ever-growing range of fields, from on-demand delivery to smart home. Nevertheless, these solutions are failing at getting mainstream adoption among interaction designers due to high complexity. In this paper we present the integration of two M achine Learning algorithms into UAPPI, our open source extension of the prototyping environment M IT App Inventor. In UAPPI much of the complexity related to M L has been abstracted away, providing easy-to-use graphical blocks for rapid prototyping Internet of Things solutions. We report on limits and opportunities emerged from the first two scenario-based explorations of our design process. 1 CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI) • Computing methodologies → Machine learning • Computer systems organization → Embedded and cyberphysical systems

Machine Learning based data analytics for IoT enabled Industry Automation

International Journal of Scientific Research in Science, Engineering and Technology, 2022

The main aims of this projects to the replacement of old communication that uses wired links with new communication that is wireless communication.The main reason to move to wireless communication is to improve the mobility, reduce the deployment cost, reduce cable damage and to improve the scalability.The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence. The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence.The current industrial revolution is the 4.0 industrial revolution which combines different technologies such as Internet of Things (IOT), robotics, virtual reality and artificial intelligence.The second aim of this project is to connect devices to IOT so as to improve theaccessibility of the industry from anywhere in the world. These services are known as Best Effort services.

IOT TECHNOLOGIES AND MACHINE LEARNING ALGORITHMS – A STUDY

IoT – The technology buzzword of the internet has gained attention among researchers, scientist, engineers, software developers, academia and much more professionals over the years for its effectiveness and increased use in many applications. IoT technology enables communication among real world objects to interact in an intelligent way. In turn, IoT is all about connectivity and automation that require intelligent algorithms to make the smart objects work together. On this aspect, this paper aims to discuss the technology elements of IoT, applications and how machine-learning algorithms are applied in IoT data classification and prediction.

Machine Learning with Internet of Things: A Comprehensive Survey

2019

Rapid evolutions in the hardware and software that uses devices which are used in technologies in communications have introduced the concept of IOT that is Internet of Things. IOT involves the connection of devices with one another in such a way, so that they can share information with each other and gather large number of facts on daily basis. But the disadvantage involved in the analysis of the data collected, extraction of the information and creation of the applications is that all these require human interference. IOT devices must be intelligent which can create automated smart applications introducing the concepts of Machine Learning with<br> IOT can led to huge improvements in the application. In this paper a review is conducted on the existing work done by the researchers in using Machine learning with IOT which includes the application areas. Also the major challenges which are faced in<br> using Machine Learning with IOT are briefly discussed. The aim of this p...

An Efficient Machine Learning Software Architecture for Internet of Things

2021

Internet of Things (IoT) software is becoming a critical infrastructure for many domains. In IoT, sensors monitor their environment and transfer readings to cloud, where Machine Learning (ML) provides insights to decision-makers. In the healthcare domain, the IoT software designers have to consider privacy, real-time performance and cost in addition to ML accuracy. We propose an architecture that decomposes the ML lifecycle into components for deployment on a two-tier cloud, edge-core. It enables IoT time-series data to be consumed by ML models on edge-core infrastructure, with pipeline elements deployed on any tier, dynamically. The architecture feasibility and ML accuracy are validated with three brain-computer interfaces (BCI) based use-cases. The contributions are two-fold: first, we propose a novel ML-IoT pipeline software architecture that encompasses essential components from data ingestion to runtime use of ML models; second, we assess the software on cognitive applications ...