Abstract:IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of variou… Show more
“…Xu et al [20] propose piFogBedII to support testing of mobile fog applications. PiFogBedII is an enhancement of Pi-FogBed [21], built with Pis by adding mobility and migration management strategies.…”
Smart mobility becomes paramount for meeting net-zero targets. However, autonomous, self-driving and electric vehicles require more than ever before an efficient, resilient and trustworthy computational offloading backbone that expands throughout the edge-to-cloud continuum. Utilizing on-demand heterogeneous computational resources for smart mobility is challenging and often cost-ineffective. This paper introduces SMOTEC, a novel open-source testbed for adaptive smart mobility experimentation with edge computing. SMOTEC provides for the first time a modular end-to-end instrumentation for prototyping and optimizing placement of intelligence services on edge devices such as augmented reality and real-time traffic monitoring. SMOTEC supports a plug-and-play Docker container integration of the SUMO simulator for urban mobility, Raspberry Pi edge devices communicating via ZeroMQ and EPOS for an AI-based decentralized load balancing across edgeto-cloud. All components are orchestrated by the K3s lightweight Kubernetes. A proof-of-concept of self-optimized service placements for traffic monitoring from Munich demonstrates in practice the applicability and cost-effectiveness of SMOTEC.
“…Xu et al [20] propose piFogBedII to support testing of mobile fog applications. PiFogBedII is an enhancement of Pi-FogBed [21], built with Pis by adding mobility and migration management strategies.…”
Smart mobility becomes paramount for meeting net-zero targets. However, autonomous, self-driving and electric vehicles require more than ever before an efficient, resilient and trustworthy computational offloading backbone that expands throughout the edge-to-cloud continuum. Utilizing on-demand heterogeneous computational resources for smart mobility is challenging and often cost-ineffective. This paper introduces SMOTEC, a novel open-source testbed for adaptive smart mobility experimentation with edge computing. SMOTEC provides for the first time a modular end-to-end instrumentation for prototyping and optimizing placement of intelligence services on edge devices such as augmented reality and real-time traffic monitoring. SMOTEC supports a plug-and-play Docker container integration of the SUMO simulator for urban mobility, Raspberry Pi edge devices communicating via ZeroMQ and EPOS for an AI-based decentralized load balancing across edgeto-cloud. All components are orchestrated by the K3s lightweight Kubernetes. A proof-of-concept of self-optimized service placements for traffic monitoring from Munich demonstrates in practice the applicability and cost-effectiveness of SMOTEC.
“…The client can be a publisher or a subscriber. The publisher sends a message to the agent on a specific topic [9], which is a string that identifies the content or destination of the message. Subscribers express their interest in one or more topics to the server and receive messages from the server that match these topics.…”
Currently, the programming of multi robot control systems is difficult and requires high requirements for robot terminals. In response to the drawbacks of complex programming and easy interruption of message redundancy during communication in existing multi robot control systems, a multi robot control system based on MQTT is designed. MQTT is used as the communication protocol for multi robot communication, and a mobile robot experimental platform is built, this greatly reduces the redundancy of messages and the difficulty of manipulation in the control process of multiple robots. At the same time, the system has the function of action reproduction, which can record the actions of mobile robot terminals, reducing the difficulty of programming and debugging. Through experiments, the superiority of using MQTT protocol to control multiple robots has been verified. This system allows the robot to complete synchronous coordination work by only receiving commands from the upper computer and executing commands sent by the upper computer to the robot client, avoiding the need for the robot CPU to process a large amount of information and reducing the production cost of the robot. By controlling the robot’s actions through a human-computer interaction platform, errors can be corrected in a timely manner, and control methods can be adjusted without worrying about the robot’s actions making mistakes due to previous programs.
“…Endüstri 4.0 ile hayatımıza giren Nesnelerin İnterneti (IoT), elektronik cihazların çeşitli sensor ve donanımlarla çevresel durumları algılayabildiği ve aralarında haberleşerek veri üretebilen aygıtlar topluluğudur [1]. Bulut bilişim, sistemin geçmiş verilerinin izlenmesine ve depolanmasına ve uç cihazlara kıyasla daha yüksek bilgi işlem gücü gerektiren karmaşık işlemlerin gerçekleştirilmesine yardımcı olur [2].…”
IoT sistemleri geleneksel buluta bağlı bir mimaride çalışır. IoT cihazlarında oluşturulan veriler buluta aktarılır, orada depolanır ve daha sonra anlamlı bilgiler çıkarmaya çalışarak işlenir. Ancak tercih edilen bu yapıda sürekli buluta bağımlı olmanın dezavantajları oldukça yüksektir. Her bir bilgi parçasının ham olarak buluta aktarılması ağ trafiğini artırırken, verileri yalnızca bulut katmanında işlemek için yüksek donanım gücü gerektirir. UBISOKKAT (Edge Computing Systems Kullanarak Otomatik Kodlayıcı Kullanarak Anomali Teşhisi) sistemi yukarıda belirtilen sorunlara çözüm olarak ortaya çıkmıştır. UBISOKKAT sistemi, IoT sistemleri ve bulut sistemleri arasında bir ara katman görevi görür. IoT noktalarında üretilen her veri önce orta katmandaki UBISOKKAT sistemine gönderilir ve burada bulut katmanına iletilir. Makine öğrenimi modeli daha sonra bulut katmanına yerleştirilir ve ara katman yazılımından aldığı verileri kullanarak kendini eğitmeye başlar. Eğitim süreci tamamlanan modelin çıktıları UBISOKKAT sistemine gönderilir ve otomatik kodlayıcı bulutta değil ara katman yazılımı yazılımında çalıştırılır. Bunun en büyük avantajı, gerçek zamanlı sistemlerde verilerin buluta gönderilmemesi, yerel noktalarda analiz edilmesi, ağ trafiğinin azaltılması ve gecikmenin azaltılmasıdır. Aynı zamanda her veri bulutta analiz edilmediği için yerel noktalarda analiz edilerek bulut ihtiyacı azaltılmakta, yüksek maliyetler düşürülmekte ve sistemin canlılığı arttırılmaktadır. Bu çalışmada son katmanda otomatik kodlayıcı modeli çalıştırılmış ve tek fazlı elektrik motorundan elde edilen verilere dayanarak UBISOKKAT sisteminin uç noktalardaki anomalileri teşhis edebildiği görülmüştür.
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