Fog robotics-based intelligence transportation system using line-of-sight intelligent transportation

Poornima, E.; Muthu, BalaAnand; Agrawal, Ruchi; Kumar, Sanjeev; Dhingra, Mallika; Asaad, Renas Rajab; Jumani, Awais Khan

doi:10.1007/s11042-023-15086-6

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…To substantiate the theoretical discourse, the paper will present empirical evidence derived from simulated SDN environments, demonstrating the practical efficacy and reliability of ML-augmented load balancing [15]. These experiments highlight the performance improvements in terms of reduced network latency, efficient resource utilization, enhanced traffic management, and overall service quality.…”

Section: Introductionmentioning

confidence: 90%

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Joldasbayev,

Sapakova,

Zhaksylyk

et al. 2023

IJACSA

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The burgeoning complexity in network management has garnered considerable attention, specifically focusing on Software-Defined Networking (SDN), a transformative technology that addresses limitations inherent in traditional network infrastructures. Despite its advantages, SDN is often susceptible to bottlenecks and excessive load issues, underscoring the necessity for more robust load balancing solutions. Previous research in this realm has predominantly concentrated on employing static or dynamic methodologies, encapsulating only a handful of parameters for traffic management, thereby limiting their effectiveness. This study introduces an innovative, intelligence-led approach to service delivery systems in SDN, specifically by orchestrating packet forwarding-encompassing both TCP and UDP traffic-through a multi-faceted analysis utilizing twelve distinct parameters elaborated in subsequent sections. This research leverages advanced machine learning algorithms, notably K-Means and DBSCAN clustering, to discern patterns and optimize traffic distribution, ensuring a more nuanced, responsive load balancing mechanism. A salient feature of this methodology involves determining the ideal number of operational clusters to enhance efficiency systematically. The proposed system underwent rigorous testing with an escalating scale of network packets, encompassing counts of 5,000 to an extensive 10,000,000, to validate performance under varying load conditions. Comparative analysis between K-Means and DBSCAN's results reveals critical insights into their operational efficacy, corroborated by juxtaposition with extant scholarly perspectives. This investigation's findings significantly contribute to the discourse on adaptive network solutions, demonstrating that an intelligent, parameter-rich approach can substantively mitigate load-related challenges, thereby revolutionizing service delivery paradigms within Software-Defined Networks.

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Section: Introductionmentioning

confidence: 90%

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Joldasbayev,

Sapakova,

Zhaksylyk

et al. 2023

IJACSA

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“…The rich biodiversity of Earth and the challenges posed by similar physical characteristics among species underscore the relevance of these classification efforts [25]. As the field of ML continues to evolve, Iris flower classification remains a valuable domain for exploring the capabilities of various algorithms and advancing the understanding of pattern recognition and classification [26]. (Zhou, 2022) [27] introduced Classification algorithms within the field of machine learning are crucial for categorizing data into distinct classes, making them invaluable for tasks like data mining.…”

Section: B Literature Reviewmentioning

confidence: 99%

Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

Renas Rajab Asaad,

M. Abdulazeez

2024

ijcs

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This study employs robust machine learning techniques to comprehensively assess the classification of Iris flower species. This study investigates the effectiveness of several machine learning algorithms in reliably classifying Iris flower species by utilizing a dataset that includes crucial morphological attributes such as sepal length, sepal width, petal length, and petal width. The algorithms under consideration are Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Every algorithm has its own distinct methodology for classification, where Decision Trees offer clear interpretability and Random Forest and XGBoost offer strong and complex ensemble techniques. The primary aim of this study is to assess and contrast different algorithms, considering not only their classification accuracy but also significant performance metrics including precision, recall, F1-score, ROC AUC, and specificity. This research provides valuable insights into the capabilities and constraints of each methodology when implemented on a meticulously organized and defined botanical dataset. It is expected that the results of this study will contribute significantly to the fields of artificial intelligence and botanical taxonomy, highlighting the capacity of these methods to accurately identify and categorize plant species.

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“…Fog/cloud computing, IoT, and AI have transformed robotics, creating a new era of autonomous systems. Robots can now sense, reason, and act autonomously in varied situations because of this combination of cutting-edge technologies [1], [2]. AI-driven robotic systems can now make real-time decisions thanks to fog/cloud computing and IoT.…”

Section: Introductionmentioning

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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Fog robotics-based intelligence transportation system using line-of-sight intelligent transportation

Cited by 7 publications

References 45 publications

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

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

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