In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants.

In this paper, we propose an effective numerical method to solve the one- and two-dimensional time-fractional convection-diffusion equations based on the Caputo derivative. The presented approach employs a hybrid method that combines Lucas and Fibonacci polynomials with the Caputo derivative definition. The main objective is to transform the problem into a time-discrete form utilizing the Caputo derivative technique and then approximate the function's derivative using Fibonacci polynomials. To evaluate the efficiency and accuracy of the proposed technique, we apply it to one- and two-dimensional problems and compare the results with the exact as well as with existing methods in recent literature. The comparison demonstrates that the proposed approach is highly efficient, accurate and ease to implement.

Once an image has been processed by, for example, a robot machine, for the purpose of, for example, features extraction or meaningful information retrieval, has a secure scheme been applied to preserve security and privacy of such information before sending it to another processing party? A huge number of image-related Internet of Things (IoT) applications face such an issue. But what are applied and potentially being applied image processing techniques that have contributed to enhance the security and privacy of IoT applications? There are numerous IoT applications that utilize image processing techniques in this direction. This article aims to survey and review a number of recently published papers and research studies that encompass proposed methods in which image processing techniques are applied to enhance the security, privacy, and safety of IoT applications. It also aims to help interested researchers in related fields have insights on what the role of image processing in enhancing the security of IoT applications is and what those techniques applied to enhance the security of IoT applications are. A comprehensive framework has been graphically extracted to give readers in the field of IoT security a map with the suitable image processing techniques that serve better to enhancing IoT applications in terms of security and privacy.

This paper proposes a mathematical model to investigate the dynamic behaviors of a modified computer virus model using the Atangana–Baleanu fractional derivative in the Caputo sense, aiming to elucidate the connection between its parameters and network attributes. By introducing a relatively new numerical method, we address the memory-dependent and nonlocal features of the system. The existence and uniqueness of the model’s solution are confirmed. To explore solution trajectories and assess the impact of various input factors on computer virus dynamics, we employ an efficient numerical technique. Our simulations provide insights into the consequences of fractional order, anti-virus measures, asymptotic fraction, damage rate, and removal rate in the system. These findings illuminate the relationships between model parameters, facilitating the design of networks that minimize the risk of virus outbreaks and prevent future cyber threats. By identifying critical factors involved in the progression of viruses, these results enable the development of more effective defense mechanisms.