Mobile and Wireless Communications Networks ; IFIP TC6 / WG6.8 Conference on Mobile and Wireless Communication Networks (MWCN 2004) October 25-27, 2004 Paris, France
Mobile Ad hoc NETworks (MANETs) has attracted great research interest in recent years. A Mobile Ad Hoc Network is a self-organizing multi-hop wireless network where all hosts (often called nodes) participate in the routing and data forwarding process. The dependence on nodes to relay data packets for others makes mobile ad hoc networks extremely susceptible to various malicious and selfish behaviors. This point is largely overlooked during the early stage of MANET research. Many works simply assume nodes are inherently cooperative and benign. However, experiences from the wired world manifest that the reverse is usually true; and many works [3] [10] [9] [8] [12] [19] have pointed out that the impact of malicious and selfish users must be carefully investigated. The goal of this research is to address the cooperation problem and related security issues in wireless ad hoc networks. As a rule of thumb, it is more desirable to include security mechanisms in the design phase rather than continually patching the system for security breaches. As pointed out in [2] [1], there can be both selfish and malicious nodes in a mobile ad hoc network. Selfish nodes are most concerned about their energy consumption and intentionally drop packets to save power. The purpose of malicious nodes, on the other hand, is to attack the network using various intrusive techniques. In general, nodes in an ad hoc network can exhibit Byzantine behaviors.
كتب مشابهة
New challenges in software engineering ; Vol 1
Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.
Fundamentals of manufacturing engineering using digital visualization
Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI in banking : Practical applications and case studies
Delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. it provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, bayesian networks, edge computing, and more. this book stands as a rare and practical guide to AI projects in the banking industry.



