Fast Data Processing for Large-Scale SOA and Event-Based Systems

2015 ◽  
Vol 5 (4) ◽  
pp. 54-77
Author(s):  
Marcel Tilly ◽  
Stephan Reiff-Marganiec
Keyword(s):  
Data Processing ◽  
Information Flow ◽  
Mobile Networks ◽  
Large Scale ◽  
New Concepts ◽  
Combining Data ◽  
Access To Data ◽  
Processing Techniques ◽  
Event Based ◽  
The Internet Of Things

The deluge of intelligent objects that are providing continuous access to data and services on one hand and the demand of developers and consumers to handle these data on the other hand require us to think about new communication paradigms and middleware. In hyper-scale systems, such as in the Internet of Things, large scale sensor networks or even mobile networks, one emerging requirement is to process, procure, and provide information with almost zero latency. This work is introducing new concepts for a middleware to enable fast communication by limiting information flow with filtering concepts using policy obligations and combining data processing techniques adopted from complex event processing.

scholarly journals On Enhancing TCP to Deal with High Latency and Transmission Errors in Geostationary Satellite Network for 5G-IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Liang Zong ◽  
Yong Bai ◽  
Chenglin Zhao ◽  
Gaofeng Luo ◽  
Zeyu Zhang ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Large Scale ◽  
Satellite Networks ◽  
Satellite Network ◽  
Packet Losses ◽  
Multihop Network ◽  
Transmission Errors ◽  
Geo Satellite ◽  
Tcp Veno ◽  
The Internet Of Things

The geostationary (GEO) satellite networks have two important influencing factors: high latency and transmission errors. Similarly, they will happen in the large-scale multihop network of the Internet of things (IoT), which will affect the application of 5G- (5th-generation mobile networks-) IoT. In this paper, we propose an enhanced TCP mechanism that increases the amount of data transferred in the slow start phase of TCP Hybla to mitigate the effect of long RTT and incorporates a refined mechanism of TCP Veno, which can distinguish packet loss between random and congestion. This scheme is evaluated and compared with NewReno, Hybla, and Veno by simulation, and the performance improvement of the proposed TCP scheme for GEO satellite network in the presence of random packet losses is demonstrated. At the same time, the enhanced TCP scheme can improve the transmission performance in the future 5G-IoT heterogeneous network with high delay and transmission .

Big Data Processing

2020 ◽  
pp. 111-132
Author(s):  
Can Eyupoglu
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Large Scale ◽  
Scale Up ◽  
It Industry ◽  
Governmental Agencies ◽  
Digital World ◽  
Large Scale Data ◽  
International Data ◽  
Processing Techniques

Big data has attracted significant and increasing attention recently and has become a hot topic in the areas of IT industry, finance, business, academia, and scientific research. In the digital world, the amount of generated data has increased. According to the research of International Data Corporation (IDC), 33 zettabytes of data were created in 2018, and it is estimated that the amount of data will scale up more than five times from 2018 to 2025. In addition, the advertising sector, healthcare industry, biomedical companies, private firms, and governmental agencies have to make many investments in the collection, aggregation, and sharing of enormous amounts of data. To process this large-scale data, specific data processing techniques are used rather than conventional methodologies. This chapter deals with the concepts, architectures, technologies, and techniques that process big data.

scholarly journals ASSESSMENT OF INFLUENCE OF IMAGE PROCESSING ON FULLY AUTOMATIC UAV PHOTOGRAMMETRY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 269-275 ◽  
Author(s):  
C. Feng ◽  
D. Yu ◽  
Y. Liang ◽  
D. Guo ◽  
Q. Wang ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Data Processing ◽  
Image Matching ◽  
Large Scale ◽  
Cost Effective ◽  
Aerial Images ◽  
Image Processing Techniques ◽  
Dense Matching ◽  
Processing Techniques

<p><strong>Abstract.</strong> Nowadays UAVs have been widely used for large scale surveying and mapping. Compared with traditional surveying techniques, UAV photogrammetry is more convenient, cost-effective, and responsive. Aerial images, Position and Orientation System (POS) observations and coordinates of ground control points are usually acquired during a surveying campaign. Aerial images are the data source of feature point extraction, dense matching and ortho-rectification procedures. The quality of the images is one of the most important factors that influence the accuracy and efficiency of UAV photogrammetry. Image processing techniques including image enhancement, image downsampling and image compression are usually used to improve the image quality as well as the efficiency and effectiveness of the photogrammetric data processing. However, all of these image processing techniques bring in uncertainties to the UAV photogrammetry. In this work, the influences of the aforementioned image processing techniques on the accuracy of the automatic UAV photogrammetry are investigated. The automatic photogrammetric data processing mainly consists of image matching, relative orientation, absolute orientation, dense matching, DSM interpolation and orthomosaicing. The results of the experiments show that the influences of the image processing techniques on the accuracy of automatic UAV photogrammetry are insignificant. The image orientation and surface reconstruction accuracies of the original and the enhanced images are comparable. The feature points extraction and image matching procedures are greatly influenced by image downsampling. The accuracies of the image orientations are not influenced by image downsampling and image compression at all.</p>

Enabling Things To Talk in Heterogeneous Self-Organizing Networks—An Exhaustive Critique For IOT

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 728-750
Author(s):  
Naeem Z Azeemi ◽  
Saira Khan ◽  
Sharmini Enoch ◽  
Riktesh Srivastava ◽  
Omar al Basheer ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Adaptive Modulation ◽  
Sustainable Growth ◽  
Adaptive Modulation And Coding ◽  
Network Architectures ◽  
Multiple Antenna ◽  
Multiple Access Schemes ◽  
Natural Outcome ◽  
Air Interfaces ◽  
The Internet Of Things

The superstructure network in the Internet of Things (IoT) is an emerging network targeted to enable an ecosystem of smart applications and services. It connectsphysical resources and peopletogether with software, hence contribute to sustainable growth, provided it combines and guarantees trustand security for people and businesses.  In this work we presented smart city viewpoint opt-in to the Firth Generation (5G) mobile networks. Both a framework and deployment are explored rigorously to assist and predicting robustness of IoT technologies and applications as a natural outcome of the Third Generation Partnership Project (3GPP) in general and LTE in particular. These technologies are compared on the basis of Air Interfaces and their Specifications i.e. Adaptive Modulation and Coding, Multiple Access Schemes and Multiple Antenna Techniques along with the evolution and comparison of the Network Architectures.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

Review of data processing techniques for density profile evaluation from broadband FM-CW reflectometry on ASDEX Upgrade

2006 ◽  
Vol 46 (9) ◽  
pp. S693-S707 ◽  
Author(s):  
P Varela ◽  
M.E Manso ◽  
A Silva ◽  
the CFN Team ◽  
the ASDEX Upgrade Team
Keyword(s):  
Data Processing ◽  
Density Profile ◽  
Processing Techniques

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

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