Performance analysis of a novel decentralised MAC protocol for cognitive radio networks

Due to the demand of emerging Cognitive Radio (CR) technology to permits using the unused licensed spectrum parts by cognitive users (CUs) to provide opportunistic and efficient utilisation of the white spaces. This requires deploying a CR MAC with the required characteristics to coordinate the spectrum access among CUs. Therefore, this paper presents the design and implementation of a novel Medium Access Control (MAC) protocol for decentralised CRNs (MCRN). The protocol provides efficient utilisations of the unused licensed channels and enables CUs to exchange data successfully over licensed channels. This is based on the observation procedure of sensing the status of the Licensed Users (LUs) are ON or OFF over the licensed channels. The protocol is validated with the comparison procedure against two different benchmark protocols in terms of the network performance; communication time and throughput. Therefore, performance analysis demonstrated that the proposed MCRN perform better and achieve higher throughput and time benefits than the benchmarks protocols

Spectrum sharing security and attacks in CRNs: a review

Cognitive Radio plays a major part in communication technology by resolving the shortage of the spectrum through usage of dynamic spectrum access and artificial intelligence characteristics. The element of spectrum sharing in cognitive radio is a fundament al approach in utilising free channels. Cooperatively communicating cognitive radio devices use the common control channel of the cognitive radio medium access control to achieve spectrum sharing. Thus, the common control channel and consequently spectrum sharing security are vital to ensuring security in the subsequent data communication among cognitive radio nodes. In addition to well known security problems in wireless networks, cognitive radio networks introduce new classes of security threats and challenges, such as licensed user emulation attacks in spectrum sensing and misbehaviours in the common control channel transactions, which degrade the overall network operation and performance. This review paper briefly presents the known threats and attacks in wireless networks before it looks into the concept of cognitive radio and its main functionality. The paper then mainly focuses on spectrum sharing security and its related challenges. Since spectrum sharing is enabled through usage of the common control channel, more attention is paid to the security of the common control channel by looking into its security threats as well as protection and detection mechanisms. Finally, the pros and cons as well as the comparisons of different CR - specific security mechanisms are presented with some open research issues and challenges

A Novel Device-to-Device Discovery Scheme for Underlay Cellular Networks

Tremendous growing demand for high data rate services such as video, gaming and social networking in wireless cellular systems, attracted researchers' attention to focus on developing proximity services. In this regard, device-to-device (D2D) communications as a promising technology for future cellular systems, plays crucial rule. The key factor in D2D communication is providing efficient peer discovery mechanisms in ultra dense networks. In this paper, we propose a centralized D2D discovery scheme by employing a signaling algorithm to exchange D2D discovery messages between network entities. In this system, potential D2D pairs share uplink cellular users' resources with collision detection, to initiate a D2D links. Stochastic geometry is used to analyze system performance in terms of success probability of the transmitted signal and minimum required time slots for the proposed discovery scheme. Extensive simulations are used to evaluate the proposed system performance.Comment: Accepted for publication in 25'th Iranian Conference on Electrical Engineering (ICEE2017

Challenges and methodologies in Passive Ocean Acoustic Tomography: An approach based on ICA

International audienceThe main idea of this manuscript consists on applying signal processing methods along with statistical approaches to achieve the characteriza- tion of a complex and hostile environment such as the oceanic underwater environment. In order to study, characterize (i.e. the measurement of the pressure, the salinity, the temperature, etc.) and extract the topographical features (depth, etc), Ocean acoustic tomography methods are widely used. For many reasons, Passive ocean Acoustic Tomography (PAT) configuration becomes more attractive than the classic active one. However the new passive configuration is more challenging. In our project, some of the facing problems have been addressed. In this manuscript, major problems are described and a new separation scheme based on modified ICA algorithms is proposed. Finally, our experiment results corroborate the effectiveness of our proposed approach. KEY WORD: Underwater acoustic applications, passive acoustic tomography, second order statistics in frequency domain, multipath channel, sparseness or non-stationary signals

Mining Sebha University Student Enrolment Data Using Descriptive and Predictive Approach

One of the main concerns of higher educational system is evaluating and enhancing the educational organization. For achieving this quality objective the organizations need deep knowledge assess, evaluate and plan towards better decision making process. Data mining techniques are analysis tools that can be used to extract meaningful knowledge from large databases. This study presents applying data mining in the field of higher educational especially for Sebha University in Libya. The main contribution of the study is an analysis model that can be used as a decision support tool. It acts as a guideline or roadmap to identify which part of the processes can be enhanced through data mining technology and how the technology could improve the conventional processes by getting advantages of it. Two main approaches were used in this study. Firstly the descriptive statistics, particularly cross tabulation analysis was carried out and presents a lot of useful information within data. Cluster analysis was performed to group the data into clusters based on its similarities. The clusters were also used as targets for prediction experiment. For predictive analysis, three techniques have been used Neural Network, Logistic regression and the Decision Tree. The study shows that Neural Network obtains the highest results accuracy among the three techniques

Biology of the Antarctic Krill (Euphausia superba) and Fisheries Management Under the Convention on the Conservation of Antarctic Marine Living Resources

Interest in the development of a commercial fishery for Antarctic krill, the lack of a legal regime to govern and promote rational exploitation of Antarctic living resources, and a desire to avoid future conflicts between claimants and non-claimants regarding the control of an Antarctic marine fishery, as well as the desire to set a precedent for management and exploitation of non-living resources were incentives for the negotiation of the Convention on the Conservation of Antarctic marine Living Resources (CCAMLR). Furthermore, the Antarctic Treaty Consultative parties wished to maintain and solidify their control and enhance their collective legitimacy in the Antarctic region. Concomitantly, the Treaty group wished to avoid the potential threat of interference from the rest of the international community. The objectives of this paper are to define the Antarctic species of potential commercial interest, to discuss the biology of Antarctic krill, Euphausia superba, to evaluate the impact of a commercial krill fishery on the Antarctic ecosystem, to evaluate and analyze the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) and, to determine whether the convention can obtain the goal of ecosystem management-allowing for rational utilization while protecting krill-dependent species in Antarctica

Simulation discounted cash flow valuation for internet companies

Discounted cash flow (DCF) is the most accepted approach for company valuation. It is well grounded in theory and practice. However, the DCF approach, which is commonly used for traditional companies valuation, presents a number of serious weaknesses within the Internet companies’ context. One of these weaknesses is tackling the uncertainty that characterize future cash flows of these companies. Specifically DCF assumes that future cash flow streams are highly predictable. The effects of uncertainty are therefore tackled implicitly by discounting the expected value of the cash flows at a risk-adjusted interest rate. However, under uncertainty, future cash flows of these companies can no longer be characterized by a single value but rather by a range of values of its possible consequences. This paper looks at the way in which uncertainty can be incorporated into the traditional DCF approach so that the latter, which is otherwise conceptually sound, becomes relevant. This is done by recognizing that the DCF input variables are uncertain and will have a probability distribution pertaining to each of them. Thus by utilizing a probability-based valuation model (using Monte Carlo Simulation) it is possible to incorporate uncertainty into the analysis and address the shortcomings of the current model. The MC simulation assigns a range of values in order to cope with uncertainty underlies each key cash flow variable. The process leads to a probability distribution of the valuation criterion used, giving investors a quantitative measure of risk involved