scholarly journals Towards data sharing economy on Internet of Things: a semantic for telemetry data

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Dareen K. Halim ◽  
Samuel Hutagalung
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Physical World ◽  
Sharing Economy ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Telemetry Data ◽  
Time Data ◽  
Domain Specific Knowledge ◽  
Iot Devices

AbstractInternet of Things (IoT) provides data processing and machine learning techniques with access to physical world data through sensors, namely telemetry data. Acquiring sensor data through IoT faces challenges such as connectivity and proper measurement requiring domain-specific knowledge, that results in data quality problems. Data sharing is one solution to this. In this work, we propose IoT Telemetry Data Hub (IoT TeleHub), a general framework and semantic for telemetry data collection and sharing. The framework is principled on abstraction, layering of elements, and extensibility and openness. We showed that while the framework is defined specifically for telemetry data, it is general enough to be mapped to existing IoT platforms with various use cases. Our framework also considers the machine-readable and machine-understandable notion in regard to resource-constrained IoT devices. We also present IoThingsHub, an IoT platform for real-time data sharing based on the proposed framework. The platform demonstrated that the framework could be implemented with existing technologies such as HTTP, MQTT, SQL, NoSQL.

scholarly journals Big Data Integration with IoT to Achieve the Challenges

2019 ◽  
Vol 8 (S3) ◽  
pp. 45-49
Author(s):  
V. Bhagyasree ◽  
K. Rohitha ◽  
K. Kusuma ◽  
S. Kokila
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Real World ◽  
Physical World ◽  
Sensor Data ◽  
The Internet ◽  
The Future ◽  
Iot Devices ◽  
The Right ◽  
The Internet Of Things

The Internet of Things anticipates the combination of physical gadgets to the Internet and their access to wireless sensor data which makes it useful to restrain the physical world. Big Data convergence has many aspects and new opportunities ahead of business ventures to get into a new market or enhance their operations in the current market. The existing techniques and technologies is probably safe to say that the best solution is to use big data tools to provide an analytical solution to the Internet of Things. Based on the current technology deployment and adoption trends, it is visioned that the Internet of Things is the technology of the future; while to-day’s real-world devices can provide best and valuable analytics, and people in the real world use many IOT devices. In spite of all the advertisements that companies offer in connection with the Internet of Things, you as a liable consumer, have the right to be suspicious about IoT advertisements. This paper focuses on the Internet of things concerning reality and what are the prospects for the future.

scholarly journals Next Generation of SDN in Cloud-Fog for 5G and Beyond-Enabled Applications: Opportunities and Challenges

Network ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 28-49
Author(s):  
Ehsan Ahvar ◽  
Shohreh Ahvar ◽  
Syed Mohsan Raza ◽  
Jose Manuel Sanchez Vilchez ◽  
Gyu Myoung Lee
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Mobile Networks ◽  
The Other ◽  
The Internet ◽  
Hybrid Cloud ◽  
Next Generation ◽  
Delay Constraints ◽  
Mission Critical ◽  
Iot Devices

In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today’s Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloudrelated technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.

scholarly journals Developing an On-Demand Cloud-Based Sensing-as-a-Service System for Internet of Things

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Mihui Kim ◽  
Mihir Asthana ◽  
Siddhartha Bhargava ◽  
Kartik Krishnan Iyyer ◽  
Rohan Tangadpalliwar ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Service System ◽  
Value Added ◽  
Computing System ◽  
Sensor Data ◽  
Dynamic Provisioning ◽  
On Demand ◽  
Cloud Computing System ◽  
Iot Devices

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

How the Internet of Things can help knowledge management: a case study from the automotive domain

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Internet Of Things ◽  
Current Knowledge ◽  
Time Data ◽  
Content Type ◽  
Useful Knowledge ◽  
Iot Devices ◽  
The Internet Of Things

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

scholarly journals Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1578 ◽  
Author(s):  
Shun-Nien Yang ◽  
Li-Chiu Chang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Forecast Model ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Natural Environments ◽  
Dynamic Feature ◽  
Flood Inundation ◽  
Flood Depth ◽  
Inundation Depth

Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted.

Machine Learning for Internet of Things

2019 ◽  
pp. 57-83 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

Machine Learning for Internet of Things

2021 ◽  
pp. 976-1002
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

Internet of Things (IoT) in Healthcare

2021 ◽  
pp. 237-258
Author(s):  
Shashwat Pathak ◽  
Shreyans Pathak
Keyword(s):  
Internet Of Things ◽  
Recent Decade ◽  
Vital Role ◽  
Healthcare Sector ◽  
Smart Devices ◽  
The Internet ◽  
Time Data ◽  
Iot Devices ◽  
The Many ◽  
Almost All

The recent decade has seen considerable changes in the way the technology interacts with human lives and almost all the aspects of life be it personal or professional has been touched by technology. Many smart devices have also started playing a vital role in many fields and domains and the internet of things (IoT) has been the harbinger of the advent of IoT devices. IoT devices have proven to be monumental in imparting ‘smartness' in the otherwise static machines. The ability of the devices to interact and transfer the data to the internet and ultimately to the end-user has revolutionized the technological world and has brought many seemingly disparate fields in the technological purview. Out of the many fields where IoT has started gaining momentum, one of the most important ones is the healthcare sector. Many wearable smart devices have been developed over time capable to transmit real-time data to hospitals and doctors. It is essential for tracking the progress of the critically ill patients and has opened the horizon for attending patients remotely using these smart devices.

scholarly journals A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises

2021 ◽  
Vol 11 (20) ◽  
pp. 9479
Author(s):  
Alim Yasin ◽  
Toh Yen Pang ◽  
Chi-Tsun Cheng ◽  
Miro Miletic
Keyword(s):  
Data Storage ◽  
Cyber Security ◽  
3D Model ◽  
Low Cost ◽  
Sensor Data ◽  
Time Data ◽  
Communication Framework ◽  
Temperature Detector ◽  
Physical Asset ◽  
Iot Devices

In the last decade, Australian SMEs are steadily becoming more digitally engaged, but they still face issues and barriers to fully adopt Industry 4.0 (I4.0). Among the tools that I4.0 encompasses, digital twin (DT) and digital thread (DTH) technologies hold significant interest and value. Some of the challenges are the lack of expertise in developing the communication framework required for data collection, processing, and storing; concerns about data and cyber security; lack of knowledge of the digitization and visualisation of data; and value generation for businesses from the data. This article aims to demonstrate the feasibility of DT implementation for small and medium-sized enterprises (SMEs) by developing a framework based on simple and low-cost solutions and providing insight and guidance to overcome technological barriers. To do so, this paper first outlines the theoretical framework and its components, and subsequently discusses a simplified and generalised DT model of a real-world physical asset that demonstrates how these components function, how they are integrated and how they interact with each other. An experimental scenario is presented to transform data harvested from a resistance temperature detector sensor connected with a WAGO 750-8102 Programmable Logic Controller for data storage and analysis, predictive simulation and modelling. Our results demonstrate that sensor data could be readily integrated from Internet-of-Things (IoT) devices and enabling DT technologies, where users could view real time data and key performance indicators (KPIs) in the form of a 3D model. Data from both the sensor and 3D model are viewable in a comprehensive history log through a database. Via this technological demonstration, we provide several recommendations on software, hardware, and expertise that SMEs may adopt to assist with their DT implementations.

scholarly journals Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 950-961
Author(s):  
Toufik Ghrib ◽  
Mohamed Benmohammed ◽  
Purnendu.Shekhar Pandey
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Sensitive Data ◽  
Learning Techniques ◽  
Image Set ◽  
Iot Devices ◽  
Table Data ◽  
Multi Class Classification

The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.

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