Analysis of Spectrum Occupancy Using Machine Learning Algorithms

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.Comment: 21 pages, 6 figure

Machine learning and energy efficient cognitive radio

With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, including re y algorithm (FFA), fish school search (FSS) and particle swarm optimization (PSO), have been utilized in this thesis to evaluate the optimal weighting vectors for cooperative spectrum sensing (CSS) and spectrum allocation in the cognitive radio systems. This evaluation is performed for more realistic signals that suffer from the non-linear distortions, caused by the power amplifiers. The thesis then takes the investigation further by analysing the spectrum occupancy in the cognitive radio systems using different machine learning techniques. Four machine learning algorithms, including naive bayesian classifier (NBC), decision trees (DT), support vector machine (SVM) and hidden markov model (HMM) have been studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy has been presented. In addition to this, the thesis investigates the energy efficient cognitive radio systems because energy harvesting enables the perpetual operation of the wireless networks without the need of battery change. In particular, energy can be harvested from the radio waves in the radio frequency spectrum. For ensuring reliable performance, energy prediction has been proposed as a key component for optimizing the energy harvesting because it equips the harvesting nodes with adaptation to the energy availability. Two machine learning techniques, linear regression (LR) and decision trees (DT) have been utilized to predict the harvested energy using real-time power measurements in the radio spectrum. Furthermore, the conventional energy harvesting cognitive radios do not assume any energy harvesting capability at the primary users (PUs). However, this is not the case when primary users are wirelessly powered. In this thesis, a novel framework has been proposed where PUs possess the energy harvesting capabilities and can get benefit from the presence of the secondary user (SU) without any predetermined agreement. The performances of the wireless powered PUs and the SU has also been analysed. Numerical results have been presented to show the accuracy of the analysis. First, it has been observed that bio-inspired techniques outperform the conventional algorithms used for collaborative spectrum sensing and allocation. Second, it has been noticed that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, a new SVM algorithm has been proposed by combining SVM with FFA. It has also been observed that SVM+FFA outperform all other machine leaning classifiers Third, it has been noticed in the energy predictive modelling framework that LR outperforms DT by achieving smaller prediction error. It has also been shown that optimal time and frequency attained using energy predictive model can be used for defining the scheduling policies of the harvesting nodes. Last, it has been shown that wirelessly powered PUs having energy harvesting capabilities can attain energy gain from the transmission of SU and SU can attain the throughput gain from the extra transmission time allocated for energy harvesting PUs

T-CHAT educational framework for teaching cyber-physical system engineering

Cyber-physical systems (CPS) are increasingly used in manufacturing, transportation, health, and other industries. To develop these complex interdisciplinary systems, highly qualified CPS engineers are required who possess sound engineering knowledge and excellent transferable skills. Academic institutions offer a range of modules and curricula to teach CPS engineering. However, the literature reports a gap between expectations of industry and competencies of CPS graduates. To close this gap, this paper introduces and describes a holistic educational framework (T-CHAT) for teaching CPS engineering at the module level. To evaluate this framework, two use cases were analysed by conducting self-perception surveys and semi-structured interviews with students. Descriptive statistics and t-tests were calculated for the survey data. Interviews were coded and analysed using a General Inductive Approach. The analysis results were discussed by the comparison of the T-CHAT implementations in these two use cases

Education in the digital age : learning experience in virtual and mixed realities

In recent years Virtual Reality has been revitalized, having gained and lost popularity between the 1960s and 1990s, and is now widely used for entertainment purposes. However, Virtual Reality, along with Mixed Reality and Augmented Reality, has broader application possibilities, thanks to significant advances in technology and accessibility. In the current study, we examined the effectiveness of these new technologies for use in education. We found that learning in both virtual and mixed environments resulted in similar levels of performance to traditional learning. However, participants reported higher levels of engagement in both Virtual Reality and Mixed Reality conditions compared to the traditional learning condition, and higher levels of positive emotions in the Virtual Reality condition. No simulator sickness was found from using either headset, and both headsets scored similarly for system usability and user acceptance of the technology. Virtual Reality, however, did produce a higher sense of presence than Mixed Reality. Overall, the findings suggest that some benefits can be gained from using Virtual and Mixed Realities for education

Evaluation instrument for engineering modules and courses

Engineers of today are required to fulfil the growing demand of interdisciplinary skills required by society and industry. They are expected to possess not only profound disciplinary knowledge and skills, but also a range of methodical, social and personal competencies. A number of teaching modules have been delivered that aim to enhance those competencies in engineering students. To evaluate quality of engineering modules an instrument is developed. This instrument measures acquired competencies, quality of the teaching process and settings. This paper presents the evaluation instrument and reports on its validity and reliabilit

Early model-analysis of logistics systems

In logistics, as in many other business sectors, service-oriented architectures (SOAs) are offering the possibility for applications to interact with each other across languages and platforms, and for external services to be procured automatically through dedicated middleware components. In this setting, one of the critical business aspects that need to be supported is the negotiation of non-functional quantitative aspects, such as costs, leading to service-level agreements (SLAs) between business parties. In this paper, we present a formal, probabilistic approach through which services can be analysed in relation to the probability that a quality of service property is satisfied.</p

RF energy modeling using machine learning for energy harvesting communications systems

Machine learning (ML) theories and methods are mainly based on probability theory and statistics. It is a very powerful tool for data modelling. On the other hand, energy harvesting has been regarded as a viable solution to extending battery lifetime of wireless sensor network. Motivated by these, modelling of the radio frequency (RF) energy available to the wireless nodes is required for efficient operation of wireless networks. In this work, we will use different ML algorithms to model the RF energy data for efficient operation of energy harvesting communication systems. Four ML algorithms are studied and compared in terms of the accuracy for RF energy modelling using the energy data in the band between 1805 and 1880 MHz. The results show that linear regression (LR) has the highest accuracy and the most stable performance, while decision tree is the worst model. Also, in terms of the operation efficiency of the system, LR has the best performance, followed by support vector machine and random forest algorithm

Methodology for creating digital twins for green hydrogen production based on simulation and gamification

The creation of digital twins offers a virtual environment that facilitates the understanding of technology in aspects of functionality and operation, as well as the validation of the performance of the same in several territories under different conditions. The methodology presented in this paper shows the stages for the creation of digital twins, from data processing, and simulations of the technology in the HOMER PRO software to the integration of these techniques in a 3D model within a virtual environment adding the gamification component to promote learning around the technology. A roadmap is presented for the evaluation and estimation, in this case, of the production of green hydrogen in the Wayuu Community La Paz, Manaure, Colombia using Digital Twins around the safety to treat hydrogen and the evaluation of the technology in the territory