Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Janbi, Nourah; Katib, Iyad; Mehmood, Rashid

doi:10.1016/j.iswa.2023.200231

Cited by 4 publications

(1 citation statement)

References 190 publications

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“…It can create intelligent systems that may learn, operate, and respond intelligently like human behaviors [6,7]. In fact, AI is a field of intelligence systems that covers not only algorithms for data processing but also offers a wide range of tools and techniques that help computers perform specific tasks [8]. Machine learning, Natural Language Processing (NLP), deep learning, pattern recognition, optimization, robotics, and computer vision can be included as subfields of AI.…”

Section: Introductionmentioning

confidence: 99%

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Panduman,

Funabiki,

Fajrianti

et al. 2024

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In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

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Section: Introductionmentioning

confidence: 99%

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Panduman,

Funabiki,

Fajrianti

et al. 2024

Information

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ML2MAS: A Multi-Agent Reinforcement Learning and BNNs-GAN Integration Framework for Smart Manufacturing Optimization

Baroud,

Yahaya

2024

Preprint

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A snapshot of parallelism in distributed deep learning training

Romero-Sandí,

Núñez,

Rojas

2024

Rev. colomb. comput.

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The accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters. Therefore, it makes your training almost impossible without the parallelization of training. Parallelism applied with different approaches is the mechanism that has been used to solve the problem of training on a large scale. This paper presents a glimpse of the state of the art related to parallelism in deep learning training from multiple points of view. The topics of pipeline parallelism, hybrid parallelism, mixture-of-experts and auto-parallelism are addressed in this study, which currently play a leading role in scientific research related to this area. Finally, we develop a series of experiments with data parallelism and model parallelism. The objective is that the reader can observe the performance of two types of parallelism and understand more clearly the approach of each one.

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Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Cited by 4 publications

References 190 publications

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

ML2MAS: A Multi-Agent Reinforcement Learning and BNNs-GAN Integration Framework for Smart Manufacturing Optimization

A snapshot of parallelism in distributed deep learning training

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