Apache Kafka

Zelenin, Anatoly; Kropp, Alexander

doi:10.3139/9783446470460.fm

Cited by 9 publications

(5 citation statements)

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“…Apache Kafka [8] is an open-source distributed event streaming system. This platform is commonly used for high-performance data pipelines, data integration, flow analytics, and mission-critical applications.…”

Section: Apache Kafkamentioning

confidence: 99%

“…In other words, as the number of cameras increases then the system load should be distributed accross more than one server. Apache Kafka [8] provides a real-time platform, best known as opensource distributed event streaming. It is widely used for real-time processing, high-performance data pipelines, stream processing, data integration, and mission-critical applications.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Scalable System For Face Tracking In Multi-Camera

OZDEMIR,

HANBAY

2024

Journal of Polytechnic

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Face detection and tracking have become increasingly popular in recent years. It has critical importance in security, defense, and robotics applications uses encountered in everyday life. For this purpose, many decision support or expert systems have been developed using artificial intelligence and machine learning. Thanks to the developments in the field of deep learning and hardware many effective and reliable face tracking systems have been realized. However there are still very few real-time scalable end-to-end systems. Also, the realization of this system on multiple cameras is a real challenge. In this study, a real-time, multi-camera, deep learning-based face tracking system has been developed. In the realized system, SCRFD model is used for face detection, ArcFace model is used for face recognition, and an updated DeepSORT algorithm is used for more stable face tracking. In addition, Apache Kafka stream processing system and Socket.IO bidirectional communication library were used to process multi-camera data in real-time and scalable. In the proposed system, when an image is input into the system, it can be displayed on the web page after approximately 127 ms

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Section: Apache Kafkamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Scalable System For Face Tracking In Multi-Camera

OZDEMIR,

HANBAY

2024

Journal of Polytechnic

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“…However, the information exchanged may relate to the final behavior of said elements, even if modeled. For this reason, the use of messaging-based flow management tools, such as MQTT [59], RabbitMQ [60], or Apache Kafka [61] is proposed, which would allow multiplexing of information, processing of independent data streams, and real-time communication between the two twins.…”

Section: ) Phase 5 Provisioningmentioning

confidence: 99%

“…The computers that act as servers to collect the information reported by the agents of both twins, in turn, act as communication gateways that allow the application of mechanisms for encrypting the traffic to ensure the security of the system. The information exchanged between the two gateways is in turn demultiplexed into communication flows based on messaging protocols such as MQTT [59], Kafka [61] or RabbitMQ [60], so that a direct relationship can be established between each specific element of the physical twin and its digital counterpart as shown in Figure 8.…”

Section: ) Communication and Interconnection Between Twinsmentioning

confidence: 99%

Digital Twins for 5G Networks: A Modeling and Deployment Methodology

et al. 2023

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A Mobile Networks Digital Twin (MNDT) is a virtual replica of a mobile communication network that accurately models the devices, the communication links, the operating environment, and the applications that run on the physical network. By replicating different environments in a laboratory and running multiple scenarios, Digital Twins provide a cost-effective way to evaluate performance, predict the effects of network changes, optimize network management, and make appropriate decisions. This paper presents a methodology for automatically creating and using Network Digital Twins, along with a proposed architecture for performing this implementation. This work is framed in the context of the B5GEMINI project, whose goal is to develop an MNDT applied to a 5G core environment and its evolution towards 6G, being able to apply it to use cases in advanced scenarios such as cybersecurity or Industry 4.0. The proposed methodology covers the entire lifecycle of the MNDT, from the initial phases of data acquisition and modeling to the phases of use and bidirectional connection between physical and digital elements.

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“…A dedicated and optimized data interpretation component performs conversions to/from the binary encoding in the wireless network, validating the data sources and expanding any sensor data contained in the beacons into the corresponding highlevel topics. The Apache Kafka [5] framework has been selected as the OSM message queue broker, providing a robust, scalable, replicable and secure event-streaming infrastructure as explained in the following section (Software -Middleware).…”

Section: Communicationmentioning

confidence: 99%

OffshoreMuster: An Integrated Real Time Localisation, Mustering and Evacuation Management System for Offshore Oil & Gas Health and Safety Operations

Panaretou¹,

Hadjithephanous²,

Kassapoglou-Faist³

et al. 2021

Day 4 Thu, November 18, 2021

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Oil & Gas offshore platforms are industrial "towns", ranked among the most hazardous working environments. Emergency situations in such environments are unpredictable and characterized by time pressure and rapidly changing conditions. OffshoreMuster brings together the latest technological advancements in real-time personnel tracking and e-mustering, complementing the existing Health and Safety (HSE) procedures, by enabling situation awareness over personnel location and status which is a key factor supporting better decision-making towards zero casualty in emergency situations. The system's underlying technology, developed after years of dedicated research and development efforts, consists of specialised low-power wireless wearable devices, customised gateways and a secure backbone network infrastructure feeding a modular decision support software system with real-time streams of data for processing and visualisation of information relevant to personnel situation assessment. HSE processes have been transformed into systematic procedures, allowing additional computer-aided decision support features, like the real-time observation of the fire-fighting team response status, the concentration of people in specific areas, instant alerting or the last-known position of a missing person. Lightweight ubiquitous devices in the form of a bracelet or embedded in the uniform are assigned to People on Board (PoB) and periodically transmit real-time location and status awareness data. A network of dedicated gateways, which are placed at specific locations on the platform or vessel, connected through the infrastructure's ethernet or wireless network, relay the data to a central decision support system. Specialised localisation algorithms and data analytics tools process the data to estimate the personnel positions and PoB status information, interactively visualising in real-time location awareness, incident escalation and alerting, which can significantly reduce response time and speed up a safe evacuation procedure. Computer-aided decision support combined with ultra-low power autonomous IoT technologies systems play a significant role in controlling, managing, and preventing critical incidents in harsh working environments, contributing into minimisation of accidents occurrence in Oil & Gas environments. The presented underling technology has been validated in maritime environments with more than 500 people taking part in real drills (TRL-8). The technology has been tailored to enhance the safety of personnel working in offshore Oil & Gas assets, currently being under laboratory testing and evaluation while a full-scale industrial deployment is scheduled for the autumn of 2021. The OffshoreMuster hardware and software components, integrated into a unified solution tailored for the offshore Oil & Gas industry, are presented for the first time. The system has been designed and developed with the support of the European Commission, co-funded by the Fast Track to Innovation Program (Grant Agreement Number 878950).

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Apache Kafka

Cited by 9 publications

References 0 publications

Real-Time Scalable System For Face Tracking In Multi-Camera

Real-Time Scalable System For Face Tracking In Multi-Camera

Digital Twins for 5G Networks: A Modeling and Deployment Methodology

OffshoreMuster: An Integrated Real Time Localisation, Mustering and Evacuation Management System for Offshore Oil & Gas Health and Safety Operations

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