Cognitive network

About: Cognitive network is a research topic. Over the lifetime, 4213 publications have been published within this topic receiving 107093 citations.

Spectrum Access and Management for Cognitive Radio Networks

Abstract: This book presents cutting-edge research contributions that address various aspects of network design, optimization, implementation, and application of cognitive radio technologies. It demonstrates how to make better utilization of the available spectrum, cognitive radios and spectrum access to achieve effective spectrum sharing between licensed and unlicensed users. The book provides academics and researchers essential information on current developments and future trends in cognitive radios for possible integration with the upcoming 5G networks. In addition, it includes a brief introduction to cognitive radio networks for newcomers to the field.

Chapter 6 – A Cognitive Random Forest: An Intra- and Intercognitive Computing for Big Data Classification Under Cune Condition

Abstract: In this chapter, a cognitive computing model, a cognitive computing architecture, and a cognitive computing system are proposed and tested to address a big data classification problem. The proposed cognitive computing model is called the STE-M model and it adopts the standard components, senses (S), thoughts (T), experiences (E), and memory (M), of human cognition to describe the processes involved in cognitive computing for big data classification. Similarly, the proposed cognitive computing architecture is called the cognitive random forest and it amalgamates the STE-M model and a set of random forest classifiers to enhance continuous learning. It also includes intra- and intercognitive computing models to connect STE-M and random forest models and improve classification accuracy with spatial and temporal reasoning. A cognitive computing system is also proposed and it is used to validate the proposed cognitive computing architecture. Experiments with a robotic navigation scenario under different environmental conditions show that the proposed cognitive random forest is capable of handling the environmental conditions what we called the cune conditions for big data.

Never Die Network Based on Cognitive Wireless Network and Satellite System for Large Scale Disaster.

Abstract: The Great East Japan Earthquake caused many casualties and radiation contamination from the Fukushima nuclear power plant, and many problems still remain in the disaster area. The communication network was severely affected by the earthquake. The network disconnection greatly delayed the rescue work and isolated many residential areas. This lack of robust network connection has become one of the major topics for any discussion of a Disaster Information Network System. This paper proposes a Never Die Network (NDN) which will consist of a Cognitive Wireless Network (CWN) and a Satellite Network. The best possible wireless links and routes are selected out of multiple wireless networks. This proposal, first of all, puts forward a cognition cycle which has a continuous network and user changing environment. Secondly, the optimal link selection will adapt the extended Analytic Hierarchy Process (AHP) method by a change of network environment and user policy during a disaster. Then, if the network environment or user environment can be changed, a proper route selection method can be conducted by the proposed extended Ad Hoc On-Demand Distance Vector (AODV) method with Min-Max AHP values. The simulation described in this paper contains an evaluation of the proposed methods by comparing a single ordinal wireless network system and a CWN for the disaster situations. The probable effectiveness of the proposed methods is discussed in this paper.

System Identification and Adaptive Control: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models

Abstract: Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN modelstems from fuzzy cognitive maps and uses the notion of concepts and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: contemporary power generation; process control and conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.

Bio-inspired time synchronization for cognitive radio ad hoc networks

Abstract: Harnessing the full power of the paradigm-shifting cognitive radio ad hoc networks (CRAHNs) hinges on solving the problem of time synchronization between the radios during the different stages of the cognitive radio cycle The dynamic network topology, the temporal and spatial variations in spectrum availability, and the distributed multi-hop architecture of CRAHNs mandate novel solutions to achieve time synchronization and efficiently support spectrum sensing, access, decision and mobility In this paper, we advance this research agenda by proposing the novel Bio-inspired time SynChronization protocol for CRAHNs (BSynC) The protocol draws on the spontaneous firefly synchronization observed in parts of Southeast Asia The significance of BSynC lies in its capability of promoting symmetric time synchronization between pairs of network nodes independent of the network topology or a predefined sequence for synchronization It enables the nodes in CRAHNs to synchronize in a decentralized manner efficiently, and is reliably The findings presented in the paper suggest that BSynC improves convergence time, thereby favoring deployment in dynamic network scenarios