The electric power system is undergoing considerable changes in operation, maintenance, and planning as a result of the integration of Renewable Energy Resources (RERs). The transition to a smart grid (SG), which employs advanced automation and control techniques, brings with it new difficulties and possibilities. This paper provides an overview of next-generation smart grids by presenting the most current and cutting-edge developments in the SG sector. This paper discusses the benefits, drawbacks, and prospects of smart grids. The difficulties of integrating RERs into the grid, as well as alternative energy storage solutions, are discussed. The unpredictable nature of resources has an impact on RER output. The energy storage system is critical in dealing with RERs’ unpredictable nature and ensuring a smooth and reliable supply to load demand. Smart energy systems provide a number of problems and possibilities in terms of developing, integrating, and implementing electrical grids that incorporate network and communication technologies, as well as important privacy and security concerns for various components within the grid. This paper also shows the influence of SGs on distributed energy generation, as well as a comparative analysis on electric cars (EVs), including classification, i.e., battery, and hybrid electric vehicles, as well as current difficulties and challenges in EV technology. A discussion of SG protection concerns and their resolution is also included.

Advanced Metering Infrastructure (AMI) plays a crucial role in enabling the efficient functioning of Smart Electrical Grids, but its successful implementation hinges on robust cybersecurity measures. To uphold data confidentiality and integrity, the deployment of an effective key management scheme (KMS) for multiple Smart Meters (SMs) and devices is imperative. The AMI exhibits unique characteristics, including storage and computation constraints in SMs, hybrid message transmission techniques, and varying participation levels in Demand Response (DR) projects, necessitating a tailored approach to security compared to other systems. In this research, we propose a KMS that is designed to address the specific security concerns of the AMI. The scheme comprises three key management procedures catering to the unicast, broadcast, and multicast modes of hybrid transmission. Given the resource limitations of SMs, we adopted simple cryptographic techniques for key creation and refreshing policies, ensuring efficiency without compromising on security. Furthermore, considering the variability of participants in DR projects, we established key refreshing policies that adapted to changing involvement. The effectiveness and security of the proposed KMS were rigorously evaluated, demonstrating its practical applicability and ability to safeguard the AMI ecosystem. The results of the evaluation indicate that our approach provides a viable and robust solution to the security challenges faced by AMI systems. By employing the proposed KMS, stakeholders can confidently deploy and manage AMI, ensuring the protection of sensitive data and maintaining the integrity of the Smart Electrical Grid.

The massive growth and use of digital multimedia through computer networks, including video and images, have increased the demand for protecting this digital data. To secure digital video, video encryption is frequently utilized. In this paper, a brand-new video scrambling technique based on two chaotic linearly symmetric maps and one chaotic tent map that has been twisted is suggested. The permutation procedure moves every frame pixel's position using a P-box created by permuting a linearly symmetric chaotic sequence. The diffusion technique employs both linearly symmetric chaos maps and distorted tent maps to create key streams. The keystream closely resembles simple frames because the pixels in the permuted frame indicate which of the two even symmetric chaos maps is replicated each time for the following byte. The information entropy, histogram, neighboring pixel correlation and sensitivity analysis, number of pixels changing regions (NPCR), and unified mean change intensity are used to thoroughly evaluate the recommended method's capacity to improve performance and security (UACI). Comparatively to other methods, the suggested algorithm is resistant to clipping, salt and pepper noise, speckle noise rotation assaults, and clipping. This positive outcome indicates that the plan can be successfully implemented for secure video communication applications.

One of the most prevalent forms of cancer among women worldwide is breast cancer, the leading cause of mortality. The vital procedure of early breast cancer detection can help with the right medical care, prevent the spread of cancer cells, as well as lower the risk of death and disability. The two most prevalent techniques for correctly detecting and classifying breast cancer through the use of Computer-Aided Diagnosis (CAD) technology are artificial intelligence (AI) and machine learning (ML). This work intends to highlight the significance of applying AI for breast cancer early detection to reduce its risks. The various techniques for recognizing breast cancer are also addressed. Also, the design process of the CAD system, including pre-processing stage, segmentation, extraction of features, and feature selection, is explained. The classification methodologies for detecting breast cancer will then be evaluated.

This paper is about enhancing the smart grid by proposing a new hybrid feature-selection method called feature selection-based ranking (FSBR). In general, feature selection is to exclude non-promising features out from the collected data at Fog. This could be achieved using filter methods, wrapper methods, or a hybrid. Our proposed method consists of two phases: filter and wrapper phases. In the filter phase, the whole data go through different ranking techniques (i.e., relative weight ranking, effectiveness ranking, and information gain ranking) The results of these ranks are sent to a fuzzy inference engine to generate the final ranks. In the wrapper phase, data is being selected based on the final ranks and passed on three different classifiers (i.e., Naive Bayes, Support Vector Machine, and neural network) to select the best set of the features based on the performance of the classifiers. This process can enhance the smart grid by reducing the amount of data being sent to the cloud, decreasing computation time, and decreasing data complexity. Thus, the FSBR methodology enables the user load forecasting (ULF) to take a fast decision, the fast reaction in short-term load forecasting, and to provide a high prediction accuracy. The authors explain the suggested approach via numerical examples. Two datasets are used in the applied experiments. The first dataset reported that the proposed method was compared with six other methods, and the proposed method was represented the best accuracy of 91%. The second data set, the generalization data set, reported 90% accuracy of the proposed method compared to fourteen different methods.

The Smart Meter Neighborhood Area Network (SM-NAN), commonly referred to as the last mile network, is a crucial component in enabling communication within the smart grid. This network facilitates the connection between Smart Meters (SMs) and Data Aggregation Points (DAPs), responsible for gathering energy consumption and invoicing data from SMs. The placement of DAPs significantly impacts the distance and transmission lines connecting them to SMs. Our research focuses on optimizing DAP deployment to minimize mean and maximum distances between DAPs and SMs. The paper introduces a system that introduces the concept of network division and develops two techniques to address the DAP placement problem. Through extensive simulations based on actual suburban community topologies, our findings demonstrate the effectiveness of these approaches in reducing the communication gap between DAPs and connected SMs. This research presents promising ideas to enhance the efficiency and performance of the last mile network in the Smart Grid (SG). The performance of two algorithms, DPCA _avg and DPCA_ws, is compared and simulated with varying numbers of DAPs (K = 1, 2, 3, 4, 5, and 6) in the subnetwork, considering maximum and mean distance minimization, as well as the maximum number of hops per path. Results indicate that both DPCA _avg and DPCA_ws effectively reduce mean and maximum distances between DAPs and SMs, respectively. DPCA _avg achieves a maximum distance of 797.3 m, with a mean distance of 345.31 m. In contrast, DPCA_ws focuses on minimizing the maximum distance, resulting in a maximum distance of 750.3 m and a mean distance of 423.1 m when the number of DAPs is one (k = 1). DPCA_ws demonstrates an awareness of maximum distance and minimizes it, while DPCA _avg prioritizes mean distance reduction. Additionally, DPCA_ws reduces the maximum number of hops per path, further optimizing the last mile network.