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Emerging Distributed/Parallel Computing Systems

This Reprint presents eight papers from the Special Issue “Emerging Distributed and Parallel Computing Systems” that span the core system challenges where computation, networking, and trust meet. Two contributions develop lightweight authentication and key agreement schemes for distributed wireless...

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Պահպանված է:
Ձևաչափ: Online
Լեզու:անգլերեն
Հրապարակվել է: MDPI - Multidisciplinary Digital Publishing Institute 2026
Խորագրեր:
SD-WAN
SDN
Traffic engineering
Network optimization and systems
Service orchestration
Security issues
Internet of Vehicles
Certificate-less aggregate signature
Authentication key negotiation scheme
Elliptic curve
TLS 1.3
Encrypted traffic analysis
Machine learning
Interception techniques
Searchable encryption
Origin–destination matrix
Differential privacy
Distributed privacy-preserving framework
Recommendation systems
Multi-level attention mechanisms
High-reward item features
Deep reinforcement learning
Authentication protocol
Physical impersonation attack
Physically unclonable function
Chebyshev chaotic mapping
Distributed wireless sensor networks
Parallel computing
Distributed systems
Emerging trends
System challenges
Future directions
Multi-view clustering
Tensor kernel norm
Projection learning
Graph learning
N
A
thema EDItEUR::A The Arts
thema EDItEUR::A The Arts::AM Architecture
Առցանց հասանելիություն:ONIX_20260416T142754_9783725868582_27
Ցուցիչներ: Ավելացրեք ցուցիչ
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Ամփոփում:This Reprint presents eight papers from the Special Issue “Emerging Distributed and Parallel Computing Systems” that span the core system challenges where computation, networking, and trust meet. Two contributions develop lightweight authentication and key agreement schemes for distributed wireless sensor networks and for the 5G Internet of Vehicles, using practical hardware security and anonymous negotiation to strengthen resilience in resource constrained settings. Privacy-preserving distributed analytics is represented by a framework for origin–destination matrix computation that balances utility and overhead via hybrid differential privacy. Regarding data-driven computing, the Reprint includes advances in machine learning methods, including a deep reinforcement learning recommender with multi-level attention and a tensor-based multi-view projection clustering approach. Three surveys consolidate current knowledge on trends in parallel and distributed systems, analysing TLS 1.3-encrypted traffic, and software-defined wide-area networks, outlining design trade-offs and open research directions. Together, these papers provide an up-to-date snapshot of methods and insights for building scalable, secure, and intelligent distributed computing platforms.

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