Bio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. In this book, we explore how bio-inspired methods can solve different problems linked to computer networks.Future networks are expected to be autonomous, scalable and adaptive. During millions of years of evolution, nature has developed a number of different systems that present these and other characteristics required for the next generation networks. Indeed, a series of bio-inspired methods have been successfully used to solve the most diverse problems linked to computer networks. This book presents some of these techniques from a theoretical and practical point of view. - Discusses the key concepts of bio-inspired networking to aid you in finding efficient networking solutions - Delivers examples of techniques both in theoretical concepts and practical applications - Helps you apply nature's dynamic resource and task management to your computer networks

Biologically Inspired Networking and Sensing: Algorithms and Architectures offers current perspectives and trends in biologically inspired networking, exploring various approaches aimed at improving network paradigms. Research contained within this compendium of research papers and surveys introduces researches in the fields of communication networks, performance modeling, and distributed computing to new advances in networking.

"Nature-inspired" includes, roughly speaking, "bio-inspired"+"physical-inspired"+"social-inspired"+ and so on. This book contains highly original contributions about how nature is going to shape networking systems of the future. Hence, it focuses on rigorous approaches and cutting-edge solutions, which encompass three classes of major methods: 1) Those that take inspiration from nature for the development of novel problem solving techniques; 2) Those that are based on the use of networks to synthesize natural phenomena; and 3) Those that employ natural materials to compute or communicate.

Self-Organization in Sensor and Actor Networks explores self-organization mechanisms and methodologies concerning the efficient coordination between intercommunicating autonomous systems.Self-organization is often referred to as the multitude of algorithms and methods that organise the global behaviour of a system based on inter-system communication. Studies of self-organization in natural systems first took off in the 1960s. In technology, such approaches have become a hot research topic over the last 4-5 years with emphasis upon management and control in communication networks, and especially in resource-constrained sensor and actor networks. In the area of ad hoc networks new solutions have been discovered that imitate the properties of self-organization. Some algorithms for on-demand communication and coordination, including data-centric networking, are well-known examples. Key features include: Detailed treatment of self-organization, mobile sensor and actor networks, coordination between autonomous systems, and bio-inspired networking. Overview of the basic methodologies for self-organization, a comparison to central and hierarchical control, and classification of algorithms and techniques in sensor and actor networks. Explanation of medium access control, ad hoc routing, data-centric networking, synchronization, and task allocation issues. Introduction to swarm intelligence, artificial immune system, molecular information exchange. Numerous examples and application scenarios to illustrate the theory. Self-Organization in Sensor and Actor Networks will prove essential reading for students of computer science and related fields; researchers working in the area of massively distributed systems, sensor networks, self-organization, and bio-inspired networking will also find this reference useful.

This book discusses data communication and computer networking, communication technologies and the applications of IoT (Internet of Things), big data, cloud computing and healthcare informatics. It explores, examines and critiques intelligent data communications and presents inventive methodologies in communication technologies and IoT. Aimed at researchers and academicians who need to understand the importance of data communication and advanced technologies in IoT, it offers different perspectives to help readers increase their knowledge and motivates them to conduct research in the area, highlighting various innovative ideas for future research.

In this book, the major ideas behind Organic Computing are delineated, together with a sparse sample of computational projects undertaken in this new field. Biological metaphors include evolution, neural networks, gene-regulatory networks, networks of brain modules, hormone system, insect swarms, and ant colonies. Applications are as diverse as system design, optimization, artificial growth, task allocation, clustering, routing, face recognition, and sign language understanding.

This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms.

A Complete Roadmap to Wireless Mesh Network Design Principles and Practices Using WiFi, WiMAX, and ZigBEE! Wireless Mesh Networking provides everything needed to create in-demand wireless mesh networks that provide wireless broadband access over wide areas. This resource presents a complete roadmap to the design and operation of wireless mesh networks, covering basic principles, key standards, and all aspects of network operation-from antenna technologies to software to energy management. This skills-building tool is packed with 100 detailed illustrations. Wireless Mesh Networking features: Expert guidance on integrating WiFi, WiMAX, and ZigBEE into seamless wireless networks The latest methods for ensuring security across the wireless network In-depth coverage of data fusion principles Vital information on SmartMesh networking in SensorNets Autonomic selfware and bio-inspired communication artifacts Inside This One-Stop Guide to Wireless Mesh Networks • Wireless mesh communications paradigms • Wireless mesh network theory and models • Wireless mesh architectures and protocols • Wireless mesh network standards (WiFi, WiMAX, ZigBEE) • Use of advanced antenna technologies • Mechanisms for secure communications • Software and middleware • Wireless sensor networking • Foundations of data fusion, gathering, and processing • Energy management • Autonomic selfware communications • Selfware networking

Bachelor Thesis from the year 2009 in the subject Computer Science - Programming, grade: 1, University of Applied Sciences Technikum Vienna (Informations- und Kommunikationssysteme), language: English, abstract: From the sector "Natural Computing" (simulation of natural Phenomena, hardware from nature, nature borrowed methods, etc.), the area "Biological inspired Computing" is selected and described. A systematic literature analysis of this field of research over the past 30 years shows that after a boom in neural networks in the 1990s, in the last five years genetic algorithms, including particularly the methods of genetic programming, came to the foreground. In this heuristic procedure computer programs are optimized in an iterative loop. In the startup phase, programs will be randomly generated. In a frequently recurring cycle, the steps program execution, evaluation of results (determination of fitness); selection and diversification (especially crossover and mutation) are used to "grow" better programs from generation to generation. This work shows criteria to decide in favor of whether or not to use genetic programming. Proven and experimental methods are presented for all phases of the optimization process, and one will find a short survey on how far these methods correlate to their natural role model. This thesis also refers to common problems such as Bloat. A library of methods collected by the author forms a mixture of a cookbook and a toolbox to be used in Genetic Programming. Finally, this thesis provides some examples where with the help of genetic programming award-winning practical applications have been created, which in many cases have outperformed conventionally obtained results.