Emerging Distributed/Parallel Computing Systems (Hardcover)

Yawen Chen

ISBN 10: 3725868581 / ISBN 13: 9783725868582

Published by Mdpi AG, 2026

About this Item

Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783725868582

Bibliographic Details

• Title: Emerging Distributed/Parallel Computing Systems (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725868581
• ISBN 13: 9783725868582
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.