Cognitive network

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

Fog-Computing-Enabled Cognitive Network Function Virtualization for an Information-Centric Future Internet

Abstract: Information-centric networking (ICN) is an important trend that will impact the future of the Internet. ICN caters to large content consumption patterns while achieving high performance. New features in the information-centric future Internet, such as caching, name-based routing, and content-based security, bring novel challenges to a decentralized environment. On one hand, the processing capabilities on the edge in an information-centric future Internet need to implement smart analysis for large quantities of content. On the other hand, the computational and storage resources need to be configured and controlled on demand and based on cognition of the content from users. To address these challenges, this article proposes a fog-computing- enabled cognitive network functions virtualization approach for an information-centric future Internet. We first propose an on-demand caching function virtualization scheme and design a communication scheme between the fog nodes and the future Internet nodes for the forwarding process. Then, to attain smart control for related operations (i.e., routing, cache policy, and security), we propose a control function virtualization approach. Finally, a cognitive resource configuration mechanism is proposed. The simulation results show the advantages and efficiency of the proposed approach.

Indirect Reciprocity Game Modelling for Cooperation Stimulation in Cognitive Networks

Abstract: In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is "I help you not because you have helped me but because you have helped others". We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

A Software Defined Cognitive Radio System: Cognitive Wireless Cloud

Abstract: This paper introduces the concept, features and architecture of a software defined cognitive radio system: Cognitive Wireless Clouds (CWC) that can realize user-centric and scalable network based on unique cognitive spectrum access, cross-network signaling, network optimization, and fast reconfiguration methods. Then, this paper shows results of feasibility studies on a software defined cognitive radio (SDCR) terminal that can access to the CWC. This includes the configuration of the SDCR terminal and a measurement data for spectrum sensing period and reconfiguration period by using software packages of W-CDMA and IEEE802.11a.

The evolution of representation in simple cognitive networks

Abstract: Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks-an artificial neural network and a network of hidden Markov gates-to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts features of the environment our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success.