Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the internet of Vehicles (ioV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the internet of Vehicles (ioV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in ioV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. it employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in ioV.
Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniq...
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ISBN:
(数字)9783319089157
ISBN:
(纸本)9783319089140
Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems.
This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@*** held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.
This book covers a domain that is significantly impacted by the growth of soft computing. internet of Things (ioT)-related applications are gaining much attention with more and more devices which are getting connected...
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ISBN:
(数字)9783030901196
ISBN:
(纸本)9783030901189;9783030901219
This book covers a domain that is significantly impacted by the growth of soft computing. internet of Things (ioT)-related applications are gaining much attention with more and more devices which are getting connected, and they become the potential components of some smart applications. Thus, a global enthusiasm has sparked over various domains such as health, agriculture, energy, security, and retail. So, in this book, the main objective is to capture this multifaceted nature of ioT and machine learning in one single place. According to the contribution of each chapter, the book also provides a future direction for ioT and machine learning research. The objectives of this book are to identify different issues, suggest feasible solutions to those identified issues, and enable researchers and practitioners from both academia and industry to interact with each other regarding emerging technologies related to ioT and machine learning. in this book, we look for novel chapters that recommend new methodologies, recent advancement, system architectures, and other solutions to prevail over the limitations of ioT and machine learning.
This book constitutes the refereed proceedings of the 8th international Conference on Language and Automata Theory and Applications, LATA 2014, held in Madrid, Spain in March 2014.;The 45 revised full papers presented...
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ISBN:
(数字)9783319049212
ISBN:
(纸本)9783319049205
This book constitutes the refereed proceedings of the 8th international Conference on Language and Automata Theory and Applications, LATA 2014, held in Madrid, Spain in March 2014.;The 45 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 116 submissions. The papers cover the following topics: algebraic language theory; algorithms on automata and words; automata and logic; automata for system analysis and program verification; automata, concurrency and Petri nets; automatic structures; combinatorics on words; computability; computational complexity; descriptional complexity; DNA and other models of bio-inspired computing; foundations of finite state technology; foundations of XML; grammars (Chomsky hierarchy, contextual, unification, categorial, etc.); grammatical inference and algorithmic learning; graphs and graph transformation; language varieties and semigroups; parsing; patterns; quantum, chemical and optical computing; semantics; string and combinatorial issues in computational biology and bioinformatics; string processing algorithms; symbolic dynamics; term rewriting; transducers; trees, tree languages and tree automata; weighted automata.
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