Mubashir Husain Rehmani

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.

Primary radio user activity models for cognitive radio networks: A survey

Abstract Cognitive Radio Networks have been emerged as a promising solution for solving the problem of spectrum scarcity and improving spectrum utilization by opportunistic use of spectrum. Cognitive radio networks utilize the spectrum which is licensed to primary radio users when they are not utilizing it, i.e., when the spectrum is idle. Thus, the performance of cognitive radio networks is highly dependent upon the activity of primary radio users. Hence, it is very important to model primary radio users activity in cognitive radio networks. By keeping this in mind, several models in the literature have been proposed for modeling primary radio users activity. But there is not any source which consolidate all these models into single platform. Therefore, this paper combines all the primary radio user activity models for cognitive radio networks at a single place. The goal of this paper is to provide a single source in the form of survey paper by which a reader can get an idea about which primary radio user activity models have been used in the literature for cognitive radio networks and how the modeling is performed. Furthermore, we also discuss issues, challenges and future directions for primary radio activity models. In fact, in this paper, different primary radio user activity models have been presented along with their classification. This paper also discusses those approaches which performed real implementation for spectrum occupancy along with spectrum bands on which the implementation is performed and location where implementation is carried out. In summary, this paper provides up-to-date survey of primary radio user activity models for cognitive radio networks.

SURF: A Distributed Channel Selection Strategy for Data Dissemination in Multi-Hop Cognitive Radio Networks

In this paper, we propose an intelligent and distributed channel selection strategy for efficient data dissemination in multi-hop cognitive radio network. Our strategy, SURF, classifies the available channels and uses them efficiently to increase data dissemination reliability in multi-hop cognitive radio networks. The classification is done on the basis of primary radio unoccupancy and of the number of cognitive radio neighbors using the channels. Through extensive NS-2 simulations, we study the performance of SURF compared to four related approaches. Simulation results confirm that our approach is effective in selecting the best channels for efficient communication (in terms of less primary radio interference) and for highest dissemination reachability in multi-hop cognitive radio networks.

A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) use the unlicensed industrial, scientific, and medical (ISM) band for transmissions. However, with the increasing usage and demand of these networks, the currently available ISM band does not suffice for their transmissions. This spectrum insufficiency problem has been overcome by incorporating the opportunistic spectrum access capability of cognitive radio (CR) into the existing WSN, thus giving birth to CR sensor networks (CRSNs). The sensor nodes in CRSNs depend on power sources that have limited power supply capabilities. Therefore, advanced and intelligent radio resource allocation schemes are very essential to perform dynamic and efficient spectrum allocation among sensor nodes and to optimize the energy consumption of each individual node in the network. Radio resource allocation schemes aim to ensure QoS guarantee, maximize the network lifetime, reduce the internode and internetwork interferences, etc. In this paper, we present a survey of the recent advances in radio resource allocation in CRSNs. Radio resource allocation schemes in CRSNs are classified into three major categories, i.e., centralized, cluster-based, and distributed. The schemes are further divided into several classes on the basis of performance optimization criteria that include energy efficiency, throughput maximization, QoS assurance, interference avoidance, fairness and priority consideration, and hand-off reduction. An insight into the related issues and challenges is provided, and future research directions are clearly identified.

Integration of Cognitive Radio Technology with unmanned aerial vehicles

Unmanned aerial vehicles have gained much popularity for applications which do not require human operator or are too dangerous for human operators. They operate on IEEE L-Band, IEEE S-Band and ISM band. However, with recent advances in technology, new wireless devices have been developed which also operate on these bands. Therefore, these bands have become overcrowded and unmanned aerial vehicles may face the problem of spectrum scarcity. Moreover, there are specific challenges associated with aeronautical communication links. Cognitive radio has emerged as a promising solution for solving the challenges caused by scarce spectrum. We focus on the integration of unmanned aerial vehicles with cognitive radio technology. Our main objective is to highlight and discuss some of the issues, challenges, and future research challenges associated with the integration of unmanned aerial vehicles and cognitive radio technology which occur as a result of the intrinsic characteristics of unmanned aerial vehicles or cognitive radio technology.

An efficient trajectory design for mobile sink in a wireless sensor network

Mobile sink trajectory plays a pivotal role for network coverage, data collection and data dissemination in wireless sensor networks. Considering this, we propose a novel approach for mobile sink trajectory in wireless sensor networks. Our proposed approach is based on Hilbert Space Filling Curve, however, the proposed approach is different from the previous work in a sense that the curve order changes according to node density. In this paper, we investigate the mobile sink trajectory based on Hilbert Curve Order which depends upon the size of the network. Second, we calculate the Hilbert Curve Order based on node density to re-dimension the mobile sink trajectory. Finally, we perform extensive simulations to evaluate the effectiveness of proposed approach in terms of network coverage and scalability. Simulation results confirm that our proposed approach outperforms with size based Hilbert Curve in terms of network coverage, packet delivery ratio and average energy consumption.

Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions

Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaningless if IoT objects are not equipped with cognitive radio capability. Equipping IoT objects with CR capability has lead to a new research dimension of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We highlight potential applications of CR-based IoT systems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues, research challenges, and future direction for these CR-based IoT networks.

Mobile Edge Computing: Opportunities, solutions, and challenges

Abstract The emergence of several new computing applications, such as virtual reality and smart environments, has become possible due to availability of large pool of cloud resources and services. However, the delay-sensitive applications pose strict delay requirements that transforms euphoria into a problem. The cloud computing paradigm is unable to meet the requirements of low latency, location awareness, and mobility support. In this context, Mobile Edge Computing (MEC) was introduced to bring the cloud services and resources closer to the user proximity by leveraging the available resources in the edge networks. In this paper, we present the definitions of the MEC given by researchers. Further, motivation of the MEC is highlighted by discussing various applications. We also discuss the opportunities brought by the MEC and some of the important research challenges are highlighted in MEC environment. A brief overview of accepted papers in our Special Issue on MEC is presented. Finally we conclude this paper by highlighting the key points and summarizing the paper.

A survey on network coding

Network coding is a technique in which transmitted data is encoded and decoded to enhance network throughput, reduce delays and construct a more robust network. Network coding has been used in many networks such as wireless sensor networks, traditional wireless networks, video multicast networks, Peer-to-Peer (P2P) networks and many others. Nowadays, with the emergence of Cognitive Radio Networks (CRNs), network coding is evolving from traditional wireless networks to CRNs. Therefore in this paper, our goal is to provide a survey of network coding specific for CRNs. In CRNs, a user (called Secondary User or SU) can intelligently judge and scrutinize the environment and then make decisions to adapt transmission schemes. Thus, SUs in CRNs employ network coding for data transmission. However, since CRNs is an emerging field, therefore, many technical issues exist. In order to provide a better understanding of the research challenges of Network Coding in CRNs, in this article, we present a detailed investigation and comparison of current state-of-the-art protocols and algorithms for Network Coding in Cognitive Radio Networks. Before this, in order to better understand network coding, we have discussed five illustrative examples of network coding, applications of network coding and network coding in traditional wireless networks, including classification and benefits, so that the readers can have handful knowledge of network coding.

Full-Duplex Communication in Cognitive Radio Networks: A Survey

Wireless networks with their ubiquitous applications have become an indispensable part of our daily lives. Wireless networks demand more and more spectral resources to support the ever increasing numbers of users. According to network engineers, the current spectrum crunch can be addressed with the introduction of cognitive radio networks (CRNs). In half-duplex (HD) CRNs, the secondary users (SUs) can either only sense the spectrum or transmit at a given time. This HD operation limits the SU throughput, because the SUs cannot transmit during the spectrum sensing. However, with the advances in self-interference suppression (SIS), full-duplex (FD) CRNs allow for simultaneous spectrum sensing and transmission on a given channel. This FD operation increases the throughput and reduces collisions as compared with HD-CRNs. In this paper, we present a comprehensive survey of FD-CRN communications. We cover the supporting network architectures and the various transmit and receive antenna designs. We classify the different SIS approaches in FD-CRNs. We survey the spectrum sensing approaches and security requirements for FD-CRNs. We also survey major advances in FD medium access control protocols as well as open issues, challenges, and future research directions to support the FD operation in CRNs.