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.

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

"The objective of the book is to introduce and bring together well-known circuit design aspects, as well as to cover up-to-date outcomes of theoretical studies in decision-making, biologically-inspired, and artificial intelligent learning techniques"--Provided by publisher.

Seeking new methods to satisfy increasing communication demands, researchers continue to find inspiration from the complex systems found in nature. From ant-inspired allocation to a swarm algorithm derived from honeybees, Bio-Inspired Computing and Networking explains how the study of biological systems can significantly improve computing, networki

Network on Chip (NoC) addresses the communication requirement of different nodes on System on Chip. The bio-inspired algorithms improve the bandwidth utilization, maximize the throughput and reduce the end-to-end latency and inter-flit arrival time. This book exclusively presents in-depth information regarding bio-inspired algorithms solving real world problems focussing on fault-tolerant algorithms inspired by the biological brain and implemented on NoC. It further documents the bio-inspired algorithms in general and more specifically, in the design of NoC. It gives an exhaustive review and analysis of the NoC architectures developed during the last decade according to various parameters. Key Features: Covers bio-inspired solutions pertaining to Network-on-Chip (NoC) design solving real world examples Includes bio-inspired NoC fault-tolerant algorithms with detail coding examples Lists fault-tolerant algorithms with detailed examples Reviews basic concepts of NoC Discusses NoC architectures developed-to-date

Computational intelligence is a component of Encyclopedia of Technology, Information, and Systems Management Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Computational intelligence is a rapidly growing research field including a wide variety of problem-solving techniques inspired by nature. Traditionally computational intelligence consists of three major research areas: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Neural networks are mathematical models inspired by brains. Neural networks have massively parallel network structures with many neurons and weighted connections. Whereas each neuron has a simple input-output relation, a neural network with many neurons can realize a highly non-linear complicated mapping. Connection weights between neurons can be adjusted in an automated manner by a learning algorithm to realize a non-linear mapping required in a particular application task. Fuzzy systems are mathematical models proposed to handle inherent fuzziness in natural language. For example, it is very difficult to mathematically define the meaning of “cold” in everyday conversations such as “It is cold today” and “Can I have cold water”. The meaning of “cold” may be different in a different situation. Even in the same situation, a different person may have a different meaning. Fuzzy systems offer a mathematical mechanism to handle inherent fuzziness in natural language. As a result, fuzzy systems have been successfully applied to real-world problems by extracting linguistic knowledge from human experts in the form of fuzzy IF-THEN rules. Evolutionary computation includes various population-based search algorithms inspired by evolution in nature. Those algorithms usually have the following three mechanisms: fitness evaluation to measure the quality of each solution, selection to choose good solutions from the current population, and variation operators to generate offspring from parents. Evolutionary computation has high applicability to a wide range of optimization problems with different characteristics since it does not need any explicit mathematical formulations of objective functions. For example, simulation-based fitness evaluation is often used in evolutionary design. Subjective fitness evaluation by a human user is also often used in evolutionary art and music. These volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers.

The 5th Symposium on Data Mining Applications (SDMA 2018) provides valuable opportunities for technical collaboration among data mining and machine learning researchers in Saudi Arabia, Gulf Cooperation Council (GCC) countries and the Middle East region. This book gathers the proceedings of the SDMA 2018. All papers were peer-reviewed based on a strict policy concerning the originality, significance to the area, scientific vigor and quality of the contribution, and address the following research areas.• Applications: Applications of data mining in domains including databases, social networks, web, bioinformatics, finance, healthcare, and security.• Algorithms: Data mining and machine learning foundations, algorithms, models, and theory.• Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.• Framework: Data mining frameworks, platforms and systems implementation.• Visualizations: Data visualization and modeling.