Spectrum leasing in cognitive radio networks: a survey

Cognitive Radio (CR) is a dynamic spectrum access approach, in which unlicensed users (or secondary users, SUs) exploit the underutilized channels (or white spaces) owned by the licensed users (or primary users, PUs). Traditionally, SUs are oblivious to PUs, and therefore the acquisition of white spaces is not guaranteed. Hence, a SU must vacate its channel whenever a PU reappears on it in an unpredictablemanner,which may affect the SUs’ network performance. Spectrumleasing has been proposed to tackle the aforementioned problem through negotiation between the PU and SU networks, which allows the SUs to acquire white spaces for a guaranteed period of time.Through spectrumleasing, the PUs and SUs enhance their network performances, and additionally PUs maximize their respective monetary gains. Numerous research efforts have been made to investigate the CR, whereas the research into spectrum leasing remains at its infancy. In this paper, we present a comprehensive review on spectrum leasing schemes in CR networks by highlighting some pioneering approaches and discuss the gains, functionalities, characteristics, and challenges of spectrum leasing schemes along with the performance enhancement in CR networks. Additionally, we discuss various open issues in order to spark new interests in this research area

Reinforcement learning-based trust and reputation model for spectrum leasing in cognitive radio networks

Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users(PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field

IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Addressing the major information technology challenges of electronic textbooks

Electronic textbooks (e-Textbooks) are digitized forms of textbooks which are envisioned to replace existing paper-based textbooks. After intensive literature review, together with interview results, our study has figured out four major IT-based challenges associated with e-Textbooks in its pursuit to replace the traditional textbooks, namely standardizing format of content, improving service reliability, improving quality and accuracy of content, and improving readability. This paper also provides an extensive review on how these challenges have been approached using existing e-Textbook solutions, such as N-Screen services, cloud computing, open market place, P2P between devices and HTML5. For each solution, we develop a usage scenario in which users apply the aforementioned technologies to interact with e-Textbooks and share contents among themselves. This article aims to provide a strong foundation for further investigations into the development and distribution of e-Textbooks for eventual successful adoption of e-Textbooks in school education

Can an electronic textbooks be part of K-12 education?: Challenges, technological solutions and open issues

An electronic textbook (e-Textbook) is a digitized (or electronic) form of textbook, which normally needs an endorsement by the national or state government when it is used in the K-12 education system. E-Textbooks have been envisioned to replace existing paper-based textbooks due to its educational advantages. Hence, it is of paramount importance for the relevant parties (i.e. national and state governments, or school districts) to draw a comprehensive roadmap of technologies necessary for the successful adoption of e-Textbooks nationwide. This paper provides a brief overview of e-Textbooks and subsequently an extensive discussion on challenges associated with e-Textbooks in the pursuit of replacing traditional textbooks with e-Textbooks. This paper further provides an extensive review on how the challenges have been approached using existing e-Textbook technologies, such as multi-touch technology, e-Paper, Web 2.0 and cloud computing. Literature review and interview have been conducted to identify the challenges of e Textbooks implementation in terms of e-Textbook usage levels and the reasons of its refusal. There were 180 students and 20 academic staff participated as a sample for interviews. Eight categories of key challenges were identified. Subsequently, assessment was performed on how the evolving e-Textbook technology has been applied to address the key challenges and problems. This article aims to provide a strong foundation for further investigations in e-Textbooks for successful adoption of e-Textbooks in school education

Exploiting the power of multiplicity: a holistic survey of network-layer multipath

The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

SMART: A SpectruM-Aware clusteR-based rouTing scheme for distributed cognitive radio networks

Cognitive radio (CR) is the next-generation wireless communication system that allows unlicensed users (or secondary users, SUs) to exploit the underutilized spectrum (or white spaces) in licensed spectrum while minimizing interference to licensed users (or primary users, PUs). This article proposes a SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network. An intrinsic characteristic of CRNs is the dynamicity of operating environment in which network conditions (i.e., PUs’ activities) change as time goes by. Based on the network conditions, SMART enables SUs to adjust the number of common channels in a cluster through cluster merging and splitting, and searches for a route on the clustered network using an artificial intelligence approach called reinforcement learning. Simulation results show that SMART selects stable routes and significantly reduces interference to PUs, as well as routing overhead in terms of route discovery frequency, without significant degradation of throughput and end-to-end delay