scholarly journals Large-Scale Real-Time Semantic Processing Framework for Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xi Chen ◽  
Huajun Chen ◽  
Ningyu Zhang ◽  
Jue Huang ◽  
Wen Zhang
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Semantic Processing ◽  
Large Scale ◽  
Sensor Data ◽  
Sensor Technology ◽  
Semantic Web Technologies ◽  
Computing Platform ◽  
Iot Applications ◽  
Processing Framework

Nowadays, the advanced sensor technology with cloud computing and big data is generating large-scale heterogeneous and real-time IOT (Internet of Things) data. To make full use of the data, development and deploy of ubiquitous IOT-based applications in various aspects of our daily life are quite urgent. However, the characteristics of IOT sensor data, including heterogeneity, variety, volume, and real time, bring many challenges to effectively process the sensor data. The Semantic Web technologies are viewed as a key for the development of IOT. While most of the existing efforts are mainly focused on the modeling, annotation, and representation of IOT data, there has been little work focusing on the background processing of large-scale streaming IOT data. In the paper, we present a large-scale real-time semantic processing framework and implement an elastic distributed streaming engine for IOT applications. The proposed engine efficiently captures and models different scenarios for all kinds of IOT applications based on popular distributed computing platform SPARK. Based on the engine, a typical use case on home environment monitoring is given to illustrate the efficiency of our engine. The results show that our system can scale for large number of sensor streams with different types of IOT applications.

scholarly journals Research on Real-time Application of Ubiquitous Power Internet of Things Information Based on Smart Sensor Technology

2021 ◽  
Vol 714 (4) ◽  
pp. 042046
Author(s):  
Jiangping Nan ◽  
Yajuan Jia ◽  
Xuezhen Dai ◽  
Yinglu Liu ◽  
Xiaowen Ren ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Sensor Technology ◽  
Smart Sensor ◽  
Real Time Application

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

Real-time force monitoring of smart grippers for Internet of Things (IoT) applications

2018 ◽  
Vol 11 ◽  
pp. 19-28 ◽  
Author(s):  
Zhuming Bi ◽  
Yanfei Liu ◽  
Jeremiah Krider ◽  
Joshua Buckland ◽  
Andrew Whiteman ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Iot Applications ◽  
Force Monitoring

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

scholarly journals Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10015-10027 ◽  
Author(s):  
Adnan Akbar ◽  
George Kousiouris ◽  
Haris Pervaiz ◽  
Juan Sancho ◽  
Paula Ta-Shma ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Probabilistic Data ◽  
Iot Applications

scholarly journals Internet of Things (IoT) Platform for Multi-Topic Messaging

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3346
Author(s):  
Mahmoud Hussein ◽  
Ahmed I. Galal ◽  
Emad Abd-Elrahman ◽  
Mohamed Zorkany
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Real Time ◽  
Communication Protocol ◽  
Real Time Systems ◽  
Real Time System ◽  
Client Server ◽  
Iot Applications ◽  
Iot Platforms ◽  
Time Systems

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols.

scholarly journals An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1971 ◽  
Author(s):  
Sangrez Khan ◽  
Ahmad Naseem Alvi ◽  
Muhammad Awais Javed ◽  
Byeong-hee Roh ◽  
Jehad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Fine Tuning ◽  
Wireless Sensor ◽  
Bandwidth Utilization ◽  
Iot Applications ◽  
Ieee 802.15.4 Standard

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.

scholarly journals AN INTEGRATED RAPID MAPPING SYSTEM FOR DISASTER MANAGEMENT

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 499-504 ◽  
Author(s):  
D. Hein ◽  
S. Bayer ◽  
R. Berger ◽  
T. Kraft ◽  
D. Lesmeister
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Large Scale ◽  
Software Tool ◽  
Aerial Images ◽  
Sensor Data ◽  
Camera System ◽  
Rapid Mapping ◽  
Mapping System ◽  
Effective Assessment

Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Effective, fast and coordinated disaster management crucially depends on the availability of a real-time situation picture of the affected area. However, in situ situation assessment from the ground is usually time-consuming and of limited effect, especially when dealing with large or inaccessible areas. A rapid mapping system based on aerial images can enable fast and effective assessment and analysis of medium to large scale disaster situations. This paper presents an integrated rapid mapping system that is particularly designed for real-time applications, where comparatively large areas have to be recorded in short time. The system includes a lightweight camera system suitable for UAV applications and a software tool for generating aerial maps from recorded sensor data within minutes after landing. The paper describes in particular which sensors are applied and how they are operated. Furthermore it outlines the procedure, how the aerial map is generated from image and additional gathered sensor data.

scholarly journals Security Challenges and Countermeasures for the Heterogeneity of IoT Applications

2019 ◽  
Vol 1 (2) ◽  
pp. 16 ◽  
Author(s):  
Deepak Choudhary
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Physical World ◽  
The Internet ◽  
Smart Objects ◽  
Security Issues ◽  
Iot Applications ◽  
Security Challenges ◽  
The Internet Of Things

The Internet of Things (IoT) enables the integration of data from virtual and physical worlds. It involves smart objects that can understand and react to their environment in a variety of industrial, commercial and household settings. As the IoT expands the number of connected devices, there is the potential to allow cyber-attackers into the physical world in which we live, as they seize on security holes in these new systems. New security issues arise through the heterogeneity  of  IoT  applications and devices and their large-scale deployment.

scholarly journals Semantic Representation and Internet of Things in Cultural Heritage Preventive Conservation

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Konstantinos Michalakis ◽  
Efthymia Moraitou ◽  
John Aliprantis ◽  
George Caridakis
Keyword(s):  
Internet Of Things ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Semantic Representation ◽  
Environmental Parameters ◽  
Streaming Data ◽  
Sensor Data ◽  
Physical Interaction ◽  
Semantic Web Technologies ◽  
Preventive Conservation

Preservation of Cultural Heritage (CH) collections in the best possible condition for the longest time possible is a crucial part of CH Institutions activity, since it ensures artefacts’ effective function in perpetuity. In this context, preservation processes that do not include any physical interaction with an object or collection can be regarded as preventive conservation. Preventive conservation measures and activities include among others the monitoring and management of environmental factors, in order to reduce potential risks of collections condition. The advent of the Internet of Things (IoT) can help towards this goal by automating the collection of data through sensors deployed in the cultural space and providing available services based on the IoT ecosystem. IoT technologies can facilitate the preventive conservation of tangible CH by exploiting streaming data produced by networks of sensors that keep track of changes in environmental parameters of a particular museum, in order to monitor the condition of its collections. Moreover, Semantic Web (SW) technologies could increase the efficiency of sensed data management by introducing reasoning mechanisms that will result in useful inferences regarding the combination of long-term or short-term records of sensed data and material decay. This work summarizes current state-of-the-art frameworks and monitoring systems that collect data from sensors in CH environments and the use of semantic web technologies for the efficient management of conservation and sensor data. Based on this study, it proposes an IoT infrastructure with semantic tools, which aims to enhance preventive conservation science.

Sign in / Sign up

Export Citation Format

Share Document