scholarly journals A Flexible IoT Stream Processing Architecture Based on Microservices

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 565
Author(s):  
Luca Bixio ◽  
Giorgio Delzanno ◽  
Stefano Rebora ◽  
Matteo Rulli
Keyword(s):  
Real Time ◽  
Query Language ◽  
Stream Processing ◽  
Heterogeneous Data ◽  
Core Level ◽  
Processing Unit ◽  
Reference Architecture ◽  
Real Time Processing ◽  
Stream Processing Engines ◽  
Processing Engine

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.

scholarly journals A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3166
Author(s):  
Adeyinka Akanbi ◽  
Muthoni Masinde
Keyword(s):  
Big Data ◽  
Environmental Monitoring ◽  
Real Time ◽  
Stream Processing ◽  
Heterogeneous Data ◽  
Legacy Systems ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Distributed Stream Processing ◽  
Processing Engine

In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

scholarly journals RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1985
Author(s):  
Qi Wang ◽  
Meihan Wu ◽  
Fei Yu ◽  
Chen Feng ◽  
Kaige Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Semantic Segmentation ◽  
Processing Unit ◽  
Real Time Processing ◽  
Side Scan Sonar ◽  
Network Training ◽  
Sonar Sensor ◽  
Sonar Images

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

Design Concept of Responsive Multithreaded Processor for Distributed Real-Time Control

2004 ◽  
Vol 16 (2) ◽  
pp. 194-199 ◽  
Author(s):  
Nobuyuki Yamasaki ◽  
Keyword(s):  
Real Time ◽  
Design Concept ◽  
Processing Unit ◽  
Home Automation ◽  
Real Time Control ◽  
Real Time Processing ◽  
Time Control ◽  
Multithreaded Processor ◽  
On Chip ◽  
Asic Chip

This paper describes the design concept of Responsive MultiThreaded (RMT) Processor for distributed real-time control that controls various embedded systems including robots, home automation, factory automation, etc. RMT processor integrates an 8-way multithreaded processor (RMT processing unit) for real-time processing, four sets of Responsive Link II for real-time communication, and I/O peripherals including DDR SDRAM I/Fs, DMAC, PCI64, USB2.0, IEEE1394, PWM generators, pulse counters, etc., into an ASIC chip. System designers can use various on-chip functions easily by connecting required I/Os to this chip directly. The designers can also realize distributed control systems by connecting several RMT processors with their own functions via Responsive Link II.

scholarly journals Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1372 ◽  
Author(s):  
Manuel Garcia Alvarez ◽  
Javier Morales ◽  
Menno-Jan Kraak
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Spatial Information ◽  
Smart Cities ◽  
Spatial Data Analysis ◽  
Sensor Data ◽  
Reference Architecture ◽  
Real Time Processing ◽  
Time Processing ◽  
Event Driven

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

scholarly journals Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

2021 ◽  
Vol 13 (18) ◽  
pp. 3674
Author(s):  
Guangqi Xie ◽  
Mi Wang ◽  
Zhiqi Zhang ◽  
Shao Xiang ◽  
Luxiao He
Keyword(s):  
Real Time ◽  
Graphics Processing Unit ◽  
Geometric Distortion ◽  
Processing Unit ◽  
Registration Method ◽  
Real Time Processing ◽  
Time Processing ◽  
Central Processing ◽  
Registration Errors ◽  
Processing Accuracy

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

scholarly journals New Parallel Technics for GPU, Fast SURF Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 389-393
Keyword(s):  
Real Time ◽  
Graphics Processing Unit ◽  
Computation Time ◽  
Processing Unit ◽  
Real Time Processing ◽  
Time Usage ◽  
Computation Algorithm ◽  
Original Application ◽  
Graphics Processing ◽  
Point Detection

Computer vision algorithms, especially real-time tasks, require intensive computation and reduced time. That’s why many algorithms are developed for interest point detection and description. For instance, SURF (Speeded Up Robust Feature) is extensively adopted in tracking or detecting forms and objects. SURF algorithm remains complex and massive in term of computation. So, it’s a challenge for real time usage on CPU. In this paper we propose a fast SURF parallel computation algorithm designed for Graphics-Processing-Unit (GPU). We describe different states of the algorithm in detail, using several optimizations. Our method can improve significantly the original application by reducing the computation time. Thus, it presents a good performance for real-time processing

Enhancement-mode ion-based transistor as a comprehensive interface and real-time processing unit for in vivo electrophysiology

2020 ◽  
Vol 19 (6) ◽  
pp. 679-686 ◽  
Author(s):  
Claudia Cea ◽  
George D. Spyropoulos ◽  
Patricia Jastrzebska-Perfect ◽  
José J. Ferrero ◽  
Jennifer N. Gelinas ◽  
...  
Keyword(s):  
Real Time ◽  
Processing Unit ◽  
In Vivo Electrophysiology ◽  
Real Time Processing ◽  
Time Processing ◽  
Enhancement Mode

scholarly journals Real-time motion tracking using optical flow on multiple GPUs

2014 ◽  
Vol 62 (1) ◽  
pp. 139-150 ◽  
Author(s):  
S.A. Mahmoudi ◽  
M. Kierzynka ◽  
P. Manneback ◽  
K. Kurowski
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Motion Tracking ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
High Definition ◽  
Real Time Processing ◽  
Multiple Gpus ◽  
Time Motion ◽  
Harris Corner

Abstract Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem. In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well

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