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Cloud data security for distributed embedded systems using machine learning and cryptography
In the growing demand for distributed embedded systems that efficiently execute complex processes and high-end applications, safeguarding sensitive...
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DNS-embedded service endpoint registry for distributed e-Infrastructures
Distributed e-Infrastructure is a key component of modern BIG Science. Service discovery in e-Science environments, such as Worldwide LHC Computing...
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Parallel and Distributed Computing, Applications and Technologies 23rd International Conference, PDCAT 2022, Sendai, Japan, December 7–9, 2022, Proceedings
This book constitutes the proceedings of the 23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies,...
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Case Study: Methodology for the Design and Development of Distributed Embedded Systems
This project introduces a methodology that is applied to the design and development of an embedded and distributed access control system. This...
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A comparative study on blockchain-based distributed public key infrastructure for IoT applications
Internet of Things (IoT) has gained wide popularity due to its implementation in smart homes and wearables. IoT centralized system increases the risk...
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DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Develo** clustering algorithms for energy optimization, for the Internet of Things (IoT) applications based mainly on wireless sensors networks...
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Embedded decision support platform based on multi-agent systems
There has been an outstanding use of memory storage of processors as current applications: Artificial Intelligence-based applications,...
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Task ordering in multiprocessor embedded system using a novel hybrid optimization model
In a multiprocessor system, the task scheduling function is a vital performance to minimize many issues. A multiprocessor system is applicable for...
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MODES: model-based optimization on distributed embedded systems
The predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is...
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Embedded Edge and Cloud Intelligence
The number of connected edge or internet-of-things (IoT) devices is expected to cross over 20 billion by the 2021. These devices include basic sensor...
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AdaInNet: an adaptive inference engine for distributed deep neural networks offloading in IoT-FOG applications based on reinforcement learning
The increasing expansion of Internet-of-Things (IoT) in the world requires Big Data analytic infrastructures to produce valuable knowledge in IoT...
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TinyML: Tools, applications, challenges, and future research directions
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably,...
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Embedded Systems: Rules to Improve Adaptability
Embedded systems have specific properties, which are consequences of the application domain, namely the close connection to the underlying technical...
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Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs
Many application domains including graph analytics, the Internet-of-Things, precision agriculture, and media processing operate on noisy data and/or...
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Evaluating virtualization for fog monitoring of real-time applications in mixed-criticality systems
Technological advances in embedded systems and the advent of fog computing led to improved quality of service of applications of cyber-physical...
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Embedded Intelligence for Safety and Security Machine Vision Applications
Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as image processing for security...
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Embedded topics in the stochastic block model
Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications,...
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A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments
Task scheduling and resource management are critical for improving system performance and enhancing consumer satisfaction in distributed computing...
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Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems....
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A middleware for providing communicability to Embedded MAS based on the lack of connectivity
An Embedded multi-agent system (Embedded MAS) is an embedded cognitive system based on agents cooperating to control hardware devices. These agents...
