Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities.

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities' IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

Common Mental Disorders in Smart City Settings and Use of Multimodal Medical Sensor Fusion to Detect Them.

Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.

A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials.

Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results.

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques.

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.

Towards a Security Reference Architecture for NFV.

Network function virtualization (NFV) is an emerging technology that is becoming increasingly important due to its many advantages. NFV transforms legacy hardware-based network infrastructure into software-based virtualized networks. This transformation increases the flexibility and scalability of networks, at the same time reducing the time for the creation of new networks. However, the attack surface of the network increases, which requires the definition of a clear map of where attacks may happen. ETSI standards precisely define many security aspects of this architecture, but these publications are very long and provide many details which are not of interest to software architects. We start by conducting threat analysis of some of the NFV use cases. The use cases serve as scenarios where the threats to the architecture can be enumerated. Representing threats as misuse cases that describe the modus operandi of attackers, we can find countermeasures to them in the form of security patterns, and we can build a security reference architecture (SRA). Until now, only imprecise models of NFV architectures existed; by making them more detailed and precise it is possible to handle not only security but also safety and reliability, although we do not explore those aspects. Because security is a global property that requires a holistic approach, we strongly believe that architectural models are fundamental to produce secure networks and allow us to build networks which are secure by design. The resulting SRA defines a roadmap to implement secure concrete architectures.

Deep Learning-Based Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

An Overview of Fog Computing and Edge Computing Security and Privacy Issues.

With the advancement of different technologies such as 5G networks and IoT the use of different cloud computing technologies became essential. Cloud computing allowed intensive data processing and warehousing solution. Two different new cloud technologies that inherit some of the traditional cloud computing paradigm are fog computing and edge computing that is aims to simplify some of the complexity of cloud computing and leverage the computing capabilities within the local network in order to preform computation tasks rather than carrying it to the cloud. This makes this technology fits with the properties of IoT systems. However, using such technology introduces several new security and privacy challenges that could be huge obstacle against implementing these technologies. In this paper, we survey some of the main security and privacy challenges that faces fog and edge computing illustrating how these security issues could affect the work and implementation of edge and fog computing. Moreover, we present several countermeasures to mitigate the effect of these security issues.