Cognitive Network Process Research Articles

Fuzzy cognitive network process for software reliability and quality measurement: comparisons with fuzzy analytic hierarchy process

Software reliability and quality measurement has a long-lasting impact on the final products and user experiences. The analytic hierarchy process (AHP) and its various hybrid models, including fuzzy AHP, have been applied to software reliability and quality measurement in various aspects. Related studies indicate that limitations in the paired ratio scales of AHP may lead to misapplications, which most AHP users might not be aware of. To address this issue, the fuzzy cognitive network process (FCNP) is proposed as a promising alternative applied for software reliability and quality measurement. One application based Fuzzy AHP is revisited to demonstrate the feasibility and usability of the FCNP. The paper discusses conversion from the FAHP to the FCNP, examining their reproducibility, comparability, merits, and limitations. The proposed fuzzy CNP method can a valuable tool for software customers, designers, developers, testers, and purchasers, to evaluate the level of software reliability and quality in general applications within intelligent environment.

Quantum optical techniques for quality data transmission process in cognitive networks

Quantum optical techniques for quality data transmission process in cognitive networks

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters.

Comprehensive Performance Analysis of Soft Data Fusion Schemes under SSDF Attacks in Cognitive Radio Networks

Background: Trust and security are the biggest challenges facing the Cooperative Spectrum Sensing (CSS) process in Cognitive Radio Networks (CRNs). The Spectrum Sensing Data Falsification (SSDF) attack is considered the biggest threat menacing CSS. Methods: This paper investigates the performance of different soft data combining rules such as Maximal Ratio Combining (MRC), Square Law Selection (SLS), Square Law Combining (SLC), and Selection Combining (SC), in the presence of Always Yes and Always No Malicious User (AYMU and ANMU). Results: This comparative study aims to assess the impact of such malicious users on the reliability of various soft data fusion schemes in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are performed to show that the soft data fusion scheme using MRC is the best in the field of soft data computing. Conclusion: ANMU has a slight impact on CSS. Yet, AYMU affects the cooperative detection performance.

Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process

Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process

Spectrum sensing and energy detection in cognitive networks

<span>It is worth mentioning that the use of wireless systems has been increased in recent years and supposed to highly increase in the few coming years because of the increasing demands of wireless applications such as mobile phones, Internet of Things (IoT), wireless sensors networks (WSNs), mobile applications and tablets. The scarcity of spectrum needs to be into consideration when designing a wireless system specially to answer the two following questions; how to use efficiently the spectrum available for the available networks in sharing process and how to increase the throughput delivered to the serving users. The spectrum sharing between several types of wireless networks where networks are called cognitive networks is used to let networks cooperate with each other by borrowing some spectrum bands between them especially when there is an extra band that is not used. In this project, the simulation of spectrum sensing and sharing in cognitive networks is performed between two cognitive networks. This project discusses the performance of probability of energy detected (Pd) with different values of false alarm (Pf) and Signal-To-Noise Ratio (SNR) values to evaluate the performance of the sensing and sharing process in cognitive networks. The results show that when the request of sharing spectrum increased, the full sharing process occurs for a long time and the error rate decreases for small values of SNR.</span>

To Relay or Not to Relay in Cognitive Radio Sensor Networks

A recent investigation on the medium access control (MAC) layer in cognitive radio networks (CRNs) proposed the primary packet relaying by the secondary node maintaining an extra queue used for this particular addable functionality. Nevertheless, relaying of primary packets may introduce delays on secondary packets called secondary delay and may require an additional power budget to forward the primary packets. Power budget is particularly crucial when a type of sensor network is deployed using devices of limited power resources. In this paper, admission control is employed to efficiently manage this packet-wise relaying process in cognitive radio sensor networks (CRSNs). To be specific, we assume a cognitive packet-relaying scenario with two pairs of primary and secondary users, i.e., transmitter and receiver. We analyze and formulate the secondary delay and the required power budget of the secondary sensor node in relation to the acceptance factor (i.e., admission control parameter) that indicates whether the primary packets are admitted for relaying or not. Having defined the above, we present a tradeoff between the secondary delay and the required power budget by tuning the acceptance factor, which can be tailored to specific chosen values. Based on this behavior, we formulate an optimization problem to minimize the secondary delay over the admission control parameter subject to a limit on the required power budget. Additionally, the constraints related to the stabilities of all individual queues at the primary and secondary networks are taken into account in the proposed optimization problem, due to their interdependence relations. The solution of this problem is provided using iterative decomposition methods, i.e., dual and primal decompositions, using Lagrange multipliers that simplify the original complicated problem and result in a final equivalent dual problem that includes the initial Karush–Kuhn–Tucker (KKT) conditions. We obtain the optimal acceptance factor, while in addition, we highlight the opportunities for extra delay minimization that is provided by relaxing the initial constraints through changing the values of the Lagrange multipliers. Finally, we present the behavior of the secondary delay, assuming infinite and finite queues and assessing thereby the overflow and blocking probabilities, respectively.

Fuzzy Cognitive Network Process: Comparisons With Fuzzy Analytic Hierarchy Process in New Product Development Strategy

Fuzzy analytic hierarchy process (F-AHP) has increasingly been applied in many areas. However, as the perception and cognition toward the semantic representation for the linguistic rating scale used by the fuzzy pairwise comparison in F-AHP are still open to discuss, F-AHP very likely produces misapplications. This research proposes the fuzzy cognitive network process (F-CNP) as an ideal alternative to F-AHP. A new product development application using F-AHP is revised using F-CNP. This study shows that the proposed F-CNP yields better results due to the appropriate mathematical definition of the fuzzy paired interval scale for the human perception of paired difference.

A hybrid fuzzy quality function deployment framework using cognitive network process and aggregative grading clustering: An application to cloud software product development

Quality function deployment (QFD) is an essential decision tool for product development in various domains. QFD enables the cross-functional team to translate the customer requirements into engineering characteristics during product development. Whilst there are some limitations for criteria evaluation and analysis in QFD, this study proposes a hybrid framework of Fuzzy Cognitive Network Process, Aggregative Grading Clustering, and Quality Function Deployment (F-CNP−AGC−QFD) for the criteria evaluation and analysis in QFD. The fuzzy number applied to the QFD, i.e. FQFD, enables rating flexibility for the expert judgment to handle uncertainty. The Fuzzy Cognitive Network Process (FCNP) is used for the criteria weights/priorities evaluation. The Fuzzy Aggregative Grading Clustering (FAGC) classifies the weights/priorities as ordinal grades. The proposed hybrid QFD approach applied to the cloud software product development is demonstrated to show the validity and applicability.

The Primitive Cognitive Network Process in healthcare and medical decision making: Comparisons with the Analytic Hierarchy Process

Analytic Hierarchy Process (AHP) is increasingly applied to healthcare and medical research and applications. However, knowledge representation of pairwise reciprocal matrix is still dubious. This research discusses the related drawbacks, and recommends pairwise opposite matrix as the ideal alternative. Pairwise opposite matrix is the key foundation of Primitive Cognitive Network Process (P-CNP), which revises the AHP approach with practical changes. A medical decision treatment evaluation using AHP is revised by P-CNP with a step-by-step tutorial. Comparisons with AHP have been discussed. The proposed method could be a promising decision tool to replace AHP to share information among patients or/and doctors, and to evaluate therapies, medical treatments, health care technologies, medical resources, and healthcare policies.

Toward a Ranking Strategy for E-commerce Products in an E-alliance Portal Using Primitive Cognitive Network Process

E-alliance is the platform of promoting e-commerce products. An efficient approach to evaluate and rank the suitable e- commerce products is essential for the business value of an e-alliance portal, where the business partners or consumers efficiently search the best fit e-commerce products for their transaction in internet. This paper proposes the Primitive Cognitive Network Process for the ranking strategy considering multiple criteria and alternatives.

Toward a Hybrid Approach of Primitive Cognitive Network Process and Particle Swarm Optimization Neural Network for Forecasting

Forecasting by artificial neural network is a popular approach in recent years. This paper proposes a new hybrid approach PCNP-PSONN which combines the Primitive Cognitive Network Process (PCNP) and Particle Swarm Optimization Neural Network (PSONN) for forecasting. PCNP is a rectified approach of the Analytic Hierarchy Process (AHP), to quantify the influence of factors, whilst Particle Swarm Optimization has been used for optimizing the neural network by improving the learning efficiency. The combination of PCNP and PSONN, PCNP-PSONN, can increase accuracy of network through selection of high influenced factors.

THE PRIMITIVE COGNITIVE NETWORK PROCESS: COMPARISONS WITH THE ANALYTIC HIERARCHY PROCESS

This research proposes primitive cognitive network process (P-CNP), which comprises five decision processes, as an alternative of analytic hierarchy process (AHP). Two published cases using AHP are revised by P-CNP, and the validity and applicability of P-CNP are demonstrated. The comparison results indicate that AHP produces questionable results due to the ill-defined axioms of the perception of the paired difference, and suggest that the proposed P-CNP performs better than AHP in multicriteria decision-making problems.