Cognitive network

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

Tree-Based Routing Protocol for Cognitive Wireless Access Networks

Abstract: A cognitive wireless network is mostly deployed as wireless access network to optimize the utilization efficiency of radio resource by using multiple wireless systems. In such deployment, the network topology based on a tree structure can be efficiently and quickly constructed among the cognitive terminals (CT) by using the cognitive base station (CBS) as a root, to support the multihop communication. However, the original tree-based routing (TBR) protocol is designed to handle a single wireless system such as IEEE802.11a or lib, and thus can not be applied to the cognitive networks configured with multiple wireless systems, which may have the different bandwidths and transmission ranges. To solve this problem, we propose in this paper an efficient and practical protocol, called cognitive tree-based routing (CTBR) protocol, which extends and significantly enhances the ability of the known TBR protocol to enable it to support multiple wireless systems such as IEEE802.11g and IEEE802.11j. Simulation results reveal that our proposed CTBR protocol that utilizes the cognitive-aware link metric to select a route with the best end-to-end metric and an interface with the least local load for any source-destination pair, achieves much higher performance than the utilization of normal metric associated with the hop count.

Rough Cognitive Networks

Abstract: Decision-making could informally be defined as the process of selecting the most appropriate actions among a set of possible alternatives in a given activity. In recent years several decision models based on Rough Set Theory (e.g. three-way decision rules) and Fuzzy Cognitive Maps have been introduced for addressing such problems. However, most of them are focused on decision-making problems with discrete attributes or they are oriented to specific domains. In this paper we present a decision model called Rough Cognitive Networks that combines the abstract semantic of the three-way decision model with the neural reasoning mechanism of Fuzzy Cognitive Maps for addressing numerical decision-making problems. The contribution of this study is two-fold. On one hand, it allows to explicitly handle decision-making problems with numerical features, where the target object could activate multiple regions at the same time. On the other hand, in such granular networks the three-way decision rules are used to design the topology of the map, addressing in some sense the inherent limitations in the expression and architecture of Fuzzy Cognitive Maps. Moreover, we propose a learning methodology using Harmony Search for adjusting the model parameters, leading to a parameter-free decision model where the human intervention is not required. A comparative analysis with standard classifiers and recently proposed rough recognition models is conducted in order to show the effectiveness of the proposal.

Sliding-Window Algorithm for Asynchronous Cooperative Sensing in Wireless Cognitive Networks

Abstract: To improve the performance of spectrum sensing, cooperation among Cognitive Radios (CRs) has been proposed recently as an effective solution. Most existing works require either time synchronization or extra infrastructure support, which are not always practical. This paper proposes an asynchronous spectrum sensing framework which is of high accuracy, short response time and ease to implement. Within such a framework, each node uses our proposed Sliding-Window algorithm to analyze the spectrum status with the sensing results received from its neighbors. This algorithm maintains a minimal and sufficient number of sensing results using Sliding-Window method, and applies Likelihood Ratio Test (LRT) on them to make a decision. This algorithm is evaluated both in theory and by simulations. The results proved that our proposed cooperative sensing solution can show good performance in various situations.

Topological fractionation of resting-state networks.

Abstract: Exploring topological properties of human brain network has become an exciting topic in neuroscience research. Large-scale structural and functional brain networks both exhibit a small-world topology, which is evidence for global and local parallel information processing. Meanwhile, resting state networks (RSNs) underlying specific biological functions have provided insights into how intrinsic functional architecture influences cognitive and perceptual information processing. However, topological properties of single RSNs remain poorly understood. Here, we have two hypotheses: i) each RSN also has optimized small-world architecture; ii) topological properties of RSNs related to perceptual and higher cognitive processes are different. To test these hypotheses, we investigated the topological properties of the default-mode, dorsal attention, central-executive, somato-motor, visual and auditory networks derived from resting-state functional magnetic resonance imaging (fMRI). We found small-world topology in each RSN. Furthermore, small-world properties of cognitive networks were higher than those of perceptual networks. Our findings are the first to demonstrate a topological fractionation between perceptual and higher cognitive networks. Our approach may be useful for clinical research, especially for diseases that show selective abnormal connectivity in specific brain networks.

Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users

Abstract: Cognitive radio (CR) signaling imposes some threats to the network. One of these common threats is commonly referred to as primary user emulation attack, where some malicious users try to mimic the primary signal and deceive secondary users to prevent them from accessing vacant frequency bands. In this paper, we introduce a smart primary user emulation attacker (PUEA) that sends fake signals similar to the primary signal. We assume a smart attacker, in the sense that it is aware of its radio environment and may choose different transmission strategies and then, we compare it to an always present attacker. In the proposed smart attacker strategy, the occurrence of fake signal is adjusted according to the primary user activity. First, we investigate the received signal at the CR users under such attackers. Then, we formulate and derive cooperative spectrum sensing (CSS) rules for a cognitive network operating in the presence of a PUEA and propose a new spectrum sensing scheme based on energy detection. Simulation results show that our proposed method can mitigate the destructive effect of PUEA in spectrum sensing, compared to conventional energy detection spectrum sensing.