There is a paradigm shift from traditional power distribution systems to smart grids (SGs) due to advances in information and communication technology. An advanced metering infrastructure (AMI) is one of the main components in an SG. Its relevance comes from its ability to collect, process, and transfer data through the internet. Although the advances in AMI and SG techniques have brought new operational benefits, they introduce new security and privacy challenges. Security has emerged as an imperative requirement to protect an AMI from attack. Currently, ensuring security is a major challenge in the design and deployment of an AMI. This study provides a systematic survey of the security of AMI systems from diverse perspectives. It focuses on attacks, mitigation approaches, and future visions. The contributions of this article are fourfold: First, the vulnerabilities that may exist in all components of an AMI are described and analyzed. Second, it considers attacks that exploit these vulnerabilities and the impact they can have on the performance of individual components and the overall AMI system. Third, it discusses various countermeasures that can protect an AMI system. Fourth, it presents the open challenges relating to AMI security as well as future research directions. The uniqueness of this review is its comprehensive coverage of AMI components with respect to their security vulnerabilities, attacks, and countermeasures. The future vision is described at the end.

Abstract One of the critical challenges in multicarrier‐based systems is peak to average power ratio (PAPR). Recently, Filter Bank Multicarrier (FBMC) is proved to be a promising candidate that can replace the traditional orthogonal multiplexing division (OFDM) scheme due to its better spectral efficiency, reducing both inter‐channel interference (ICI) and PAPR. Due to their advantages in reducing the PAPR without impacting the BER, the Hadamard transform was used in the proposed FBMC based VLC system. Furthermore, here, the discreet cosine transform (DCT) precoding is also used to boost the potential for PAPR reduction and the BER efficiency. The negative signals are not clipped off as in traditional asymmetrically‐clipped optical OFDM (ACO‐OFDM) signals to reduce the impact of large‐amplitude signal reduction nor add dc biasing for cancelling negative signals as in traditional DC‐biased optical OFDM (DCO‐OFDM) signals. Furthermore, a Clipping ratio is introduced to allow a trade‐off between bit error rate (BER) and PAPR reduction, and the optimal PAPR reduction is investigated. The obtained results show that the proposed FBMC based VLC system with DCT and clipping technique can reduce the PAPR and achieve good BER efficiency compared to the OFDM based VLC system.

The Internet of Things (IoT) paradigm serves as an enabler technology in several domains. Healthcare is one of the domains in which the IoT plays a vital role in increasing quality of life. On the one hand, the Internet of Healthcare Things (IoHT) creates smart environments and increases the efficiency and intelligence of the provided services. On the other hand, unfortunately, it suffers from security vulnerabilities inside and outside. There are various techniques used to identify, access, and securely manage IoT devices. Additionally, sensors, monitoring, key confidentiality management, integrity, and sensitive data accessibility are required. This study focuses on the IoT perception layer and offers a comprehensive review of the IoHT or the Internet of Medical Things (IoMT). The paper covers the current trends and open challenges in IoHT device authentication mechanisms, such as the physically unclonable function (PUF) and blockchain-based techniques. In addition, IoT simulators and verification tools are included. Finally, a future vision regarding the evolution of IoHT device authentication in terms of the utilization of different technologies, such as artificial intelligence, cloud computing, and 5G, is provided at end of this review.

A hybrid nonlinear time series predictor was proposed in which a nonlinear sub-predictor (NSP) and a linear sub-predictor (LSP) are combined in a cascade form. In this paper, we propose a separate learning method, in which the NSP is trained until convergence, then the LSP is trained using the final NSP weights. If the NSP and the LSP are trained simultaneously, the input of the LSP will be far from the correct prediction at the early iterations. This causes disturbance in the LSP learning process. The proposed separate learning method gives better results than the simultaneous one. Furthermore, a new learning algorithm for the NSP is proposed. By enforcing the NSP weights and biases to take large values until a certain number of the learning iterations, the input potential of the hidden neurons are expanded and shifted towards the saturation regions of the sigmoid functions. As a result, noise effects can be suppressed. Computer simulations, using real world time series, demonstrates usefulness of the proposals.