The cognitive smart grid (SG) communication paradigm aims to mitigate quality of service (QoS) issues in obsolete communication architecture associated with the conventional electrical grid. This paradigm entails the integration of advanced information and communication technologies (ICTs) into power grids, enabling a two-way flow of information. However, due to the exponential increase in wireless applications and services, also driven by the deployment of the Internet of Things (IoT) smart devices, SG communication systems are expected to handle large volumes of data. As a result, the operation of SG networks is confronted with the major challenge of managing and processing data in a reliable and secure manner. The existing works in the literature proposed architectures with the objective to mitigate the underlying QoS issues such as latency, bandwidth, data congestion, energy efficiency, etc. In addition, a variety of communication technologies have been analyzed for their capacity to support stringent QoS requirements for diverse SGs environments. This notwithstanding, a standard architecture designed to mitigate the aforementioned issues for SG networks remains a work-in-progress. The main objective of this paper is to investigate the emerging technologies such as cognitive radio networks (CRNs) as part of the Fifth-Generation (5G) mobile technology for reliable communication in SG networks. Furthermore, a hybrid architecture based on the combination of fog computing and cloud computing is proposed. In this architecture, real-time latency-sensitive information is given high priority, with fog edge based servers deployed in close proximity to home area networks (HANs) for preprocessing and analyzing of information collected from smart IoT devices. In comparison to the recent works in the literature, which are mainly based on CRNs and 5G separately, the proposed architecture in this paper incorporates the combination of CRNs and 5G for reliable and efficient communication in SG networks.

Data aggregation may be considered as the technique through which streams of data gathered from Smart Meters (SMs) can be processed and transmitted to a Utility Control Center (UCC) in a reliable and cost-efficient manner without compromising the Quality of Service (QoS) requirements. In a typical Smart Grid (SG) paradigm, the UCC is usually located far away from the consumers (SMs), which has led to a degradation in network performance. Although the data aggregation technique has been recognized as a favorable solution to optimize the network performance of the SG, the underlying issue to date is to determine the optimal locations for the Data Aggregation Points (DAPs), where network coverage and full connectivity for all SMs deployed within the network are achieved. In addition, the main concern of the aggregation technique is to minimize transmission and computational costs. In this sense, the number of DAPs deployed should be as minimal as possible while satisfying the QoS requirements of the SG. This paper presents a Neighborhood Area Network (NAN) placement scheme based on the unsupervised K-means clustering algorithm with silhouette index method to determine the efficient number of DAPs required under different SM densities and find the best locations for the deployment of DAPs. Poisson Point Process (PPP) has been deployed to model the locations of the SMs. The simulation results presented in this paper indicate that the NAN placement scheme based on the ageless unsupervised K-means clustering algorithm not only improves the accuracy in determining the number of DAPs required and their locations but may also improve the network performance significantly in terms of network coverage and full connectivity.

The unique nature of wireless sensor networks (WSNs) poses new design challenges for development of applications, protocols and algorithms. In recent years, a substantial amount of research effort has been directed towards overcoming the underlying challenges to minimize energy consumption by the sensor nodes. The exchange of routing control packets has a significant impact on energy consumption from the small irreplaceable and non rechargeable batteries of the sensor nodes. In this paper, we propose a cross-layer adaptive routing (CLAR) protocol for WSNs, which aims to minimize the use of routing control packets in WSNs; thereby minimizing energy consumption which in turn prolongs network-lifetime. We also develop analytical model to derive energy consumption for route establishment and present simulation results, which reveal better performance and improved energy efficiency in WSNs when using the proposed cross-layer adaptive routing protocol.

Channel assembling (ChA) is a technique through which a user performs concurrent transmissions through multiple channels to maximize capacity in wireless networks. This paper proposes a hybrid ChA scheme for overlay and underlay secondary user (SU) transmissions in cognitive radio networks (CRNs). The main objective is to determine the optimal channel selection together with the associated power distribution. This aims to maximize SU capacity subject to total power constraint, minimum quality-of-service (QoS) requirement on capacity, collision constraint and transmission power constraint to protect primary user (PU) services. A Lagrangian framework is employed to reformulate the otherwise mixed integer nonlinear programming (MINLP) problem, based on which Lambert-W function with dual decomposition and subgradient iterative search methods are used to determine the optimal solution. In general, the presented simulation results illustrate significant performance improvement in terms of channel capacity, outage probability and collision probability when employing the proposed hybrid ChA scheme for SU transmissions.

Channel assembling (ChA) has been proposed to maximize capacity in wireless communications. This paper determines the optimal channel selection and power distribution for secondary user (SU) transmissions under varying channel conditions, subject to power constraint, minimum quality of service (QoS) requirement for capacity, and collision probability threshold to protect primary user (PU) services. The nature of the optimization problem turns out to be in a form of mixed integer nonlinear programming (MINLP), which is generally nondeterministic in polynomial time. A Lagrangian framework is therefore employed to reformulate the optimization problem, based on which dual decomposition with subgradient and Newton-Raphson methods are used to determine a relaxed optimal solution. Thereafter, accelerated branch-and-bound (BnB) with sequential fixing (SF) is applied to determine an optimal solution to the original MINLP problem. The simulation results presented in this paper demonstrate that adaptive ChA with channel characterization improves performance in terms of channel capacity, outage probability and collision probability.

Smart Grids (SGs) aim to improve the aging power system grid into a modernized grid with the utilization of the advanced communication technologies in the industry. The incorporation of communications technology in power systems enables two-way flow of electricity and information within the grid system. SGs emerge as the next generation technology in power systems, as a result of the increasing demand of upgrading the conventional grid into the more modernized grid, with the aim of resolving some of the major crisis such as the environmental and energy crisis posed by the existing grid. In order, to deploy this intelligent grid, a sustainable, energy efficient, flexible, scalable, and secure communication infrastructure need to be designed and implemented to address these issues. There are several surveys and studies on the Information and communication technologies (ICT) architectures to develop a suitable protocol of applying the proposed advanced and up-to-date communication and networking technologies into the power system, to enable the intelligence features of the grid system. This paper reviews the works on communications technologies on SGs, with the objective of addressing the issues related to ICT infrastructure, and the recent communication technologies with their corresponding communication requirements.