Studying and Analyzing the Fog-based Internet of Robotic Things

Menbawy, Noha El; Arafat, Hesham; Saraya, Mohamed S.; Ali-Eldin, Amr

doi:10.1109/acit50332.2020.9300093

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…These technologies enable intelligent and efficient data processing, analysis, and decision-making, improving robotic devices in diverse applications. This section will explain fog, cloud, and IoT computing and how they change robotics [8], [9]. Fog computing is a decentralized computing paradigm that moves computation closer to data generation and consumption.…”

Section: Fog/cloud Computing and Iotmentioning

confidence: 99%

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Kiran Deep Singh,

Singh

2024

EAI Endorsed Trans AI Robotics

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Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.

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Section: Fog/cloud Computing and Iotmentioning

confidence: 99%

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Kiran Deep Singh,

Singh

2024

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

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“…This is useful in dynamic contexts where the robot's surroundings and tasks change. Delivery robots can use realtime traffic data and user preferences to design routes for efficient and personalized service [28], [29].…”

Section: Continuous Learning and Adaptationmentioning

confidence: 99%

Machine Learning in Robotics with Fog/Cloud Computing and IoT

Singh,

Singh

2023

EAI Endorsed Trans AI Robotics

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Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from fog/cloud computing, IoT, and machine learning. Robots using distributed computing, real-time IoT data, and advanced machine learning algorithms could alter industries and improve automation. To maximize its potential, this revolutionary combination must overcome several obstacles. This paper discusses the benefits and drawbacks of integrating technologies. It offer rapid model training and deployment for robots ML algorithms like deep learning and reinforcement learning. Case studies demonstrate how this combination might enhance robotics across industries. This study discusses the benefits and drawbacks of fog/cloud computing, IoT, and machine learning in robots. We propose solutions for security and privacy, resource management, latency and bandwidth, interoperability, energy efficiency, data quality, and bias. By proactively addressing these difficulties, we can establish a secure, efficient, and privacy-conscious robotic ecosystem where robots seamlessly interact with the physical world, improving productivity, safety, and human-robot collaboration. As these technologies progress, appropriate integration and ethical principles are needed to maximize their benefits to society.

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Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

et al. 2022

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In today's world, it is anticipated that DTw and IoRT will transform the idea of digital healthcare and advancement of it what was previously unknown. DTw is (physical or virtual) machines or computer-based models capable of mimicking or "twinning" the lives of actual entities such as objects, humans, or human-related features. Since the advent of digital and smart healthcare, the world has swiftly integrated several technologies in healthcare to support improved patient service, extend life expectancy, and lower healthcare expenses.DTw and IoRT are promising technologies and great improvements in this field. In recent years, the concepts of IoRT and DTw have captured the interest of the surgical sector. The flawless integration of data between a physical and virtual machine in either direction is the best way to conduct smart surgery. Artificial intelligence, nanotechnology, various kinds of robotic equipment, and the Internet of Things (IoT) are our pillars of completing the task. Using the DTw technological framework and IoRT infrastructure, this review paper proposes a comparative overview of traditional and IoRT collaborated DTwT and executes an adaptive perspective, opportunities, and challenges in the healthcare sector, namely surgical advances. This is an important addition to digital surgeries and to improve healthcare operations. Our literature review has sorted them according to research areas: the technological innovation, the prospective and opportunities we have, problems facing the surgical industry today and in the future, and offers an evaluation of the enabling technologies for the betterment of employing DTw and IoRT for the developing and secure surgical sector for both patients and doctors.INDEX TERMS Digital twin technology (DTwT), Internet of Robotic things (IoRT), surgery, Future surgical sector, Healthcare,

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Studying and Analyzing the Fog-based Internet of Robotic Things

Cited by 4 publications

References 34 publications

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics

Machine Learning in Robotics with Fog/Cloud Computing and IoT

Toward IoRT Collaborative Digital Twin Technology Enabled Future Surgical Sector: Technical Innovations, Opportunities and Challenges

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