Targeted at an inclusive society in the EU and Germany, an accessibility law came into effect in 2025 for all web agencies and developing companies. The work is devoted to the analysis of the opportunities for the creation of barrier-free or accessible content, as well as the features and abilities of AI (Artificial Intelligence) powered tools for so-called accessible websites. Such AI-powered tools include LLMs (Large Language Models) like ChatGPT products and corresponding APIs. An example workflow to enhance accessibility for blind and visually impaired individuals was constructed. In the experimental part, ChatGPT Vision APIs and OpenAI's Text-to-Speech (TTS) were used to explore and enhance accessibility for blind and visually impaired individuals. The contribution addresses the ongoing challenges to modern web apps to provide highly accessible content for inclusiveness.

The high volatility of renewable energy sources makes their widespread use more difficult. The work discusses how a safe, resilient, and sustainable energy supply to power grids can be guaranteed through the deployment of storage technologies. Sustainable and intelligent energy supply services and Smart Grid enable better achievement of SDGs (Sustainable Development Goals by UNO) which address global challenges, including better access to power grids and energy, education, health care, and IT communication services by the population.

Healthcare and medical facilities depend on a continuous power supply and reliable transport for medical staff, patients, and materials. Cost-effective and energy-efficient IoT solutions play a crucial role in ensuring seamless operations. The contribution provides an overview of IoT hardware designed for sustainable and intelligent healthcare, with a particular focus on emergency management during power outages. The role of self-driving vehicles in transporting doctors, patients, and medical supplies is also explored. Additionally, the work examines Internet emergency outages and appropriate networked solutions, including emerging 6G mobile technology, Starlink, and LoRa. It also addresses new challenges posed by NIS-2 and KRITIS regulations. Finally, key aspects of cybersecurity, privacy, and personal data protection in healthcare IoT are discussed.

In this chapter the historical review and current state of neural network development is presented . It is pointed out that the modern philosophical concept of linguistic neural networks, which has been in development in the last 60–70 years, is based on both ancient and current history of human knowledge. From a mathematical point of view, the concepts of single-layer and multilayer perceptron’s, corresponding schemes, and corresponding mathematical relations are discussed. Probabilistic models of linguistic neural networks are also considered. Namely, classic recurrent networks, networks with encoders and decoders for translation, networks with attentional mechanisms, and the model of modern Transfer Technology are considered. It is pointed out that modern models of neural networks are based on converting words into vectors and using vector and matrix operations. As examples on using word vectors for text encoding and decoding, context modeling algorithms and length estimation between the symbols are considered. A comparison of these two methods of text coding is also given. Novel approaches and standards in large language models’ neural networks are also considered. Several practical examples are given. Due to immense development, AI-driven apps based on LLMs can be deployed practically everywhere. These areas include healthcare, finances, industry, traffic and logistics, education, science, and customer services. As an example, for further deployment areas, programming and software technology have been considered.

The work is devoted to the problems of collecting automated networking security information and critical event management for small and medium-sized enterprises (SMEs). So-called SIEM as advanced technology was examined. This technology’s potential for SMEs was analyzed and an outlook on further developments has been provided. Several case studies enable us to outline the advantages and disadvantages of the automated way compared to the manual process. Legal requirements for cybersecurity were examined.

This work is devoted to an actual survey on advanced security and ensured user privacy for (Highly-) Distributed Systems. The last term belongs to thin and thick apps, robot and mobile apps, (micro-)service-oriented applications, and IoT applications. The most dangerous vulnerabilities, intrusion analysis techniques and models, and countermeasures to increase the safety of the cyber-systems are discussed. AI-based methods are favored nowadays. However, generative models provide some new risks and vulnerabilities. EU legal regulations are considered. Several case studies (among others, for telemedicine and e-health) are examined.

Up-to-date software technology paradigms consider widely modern market demands, used application types as well as appropriate frameworks and platforms. A state-of-the-art overview of the used paradigms and approaches is provided as well as the challenges for IT experts are formulated. The paper is devoted to the advanced software technology (SWT) paradigms with the support of modern building blocks as well as LLMs (Large Language Model) and AI (Artificial Intelligence) deployment. The contribution addresses the ongoing challenges to modern software and provides quality and experience optimization for the development process. Between the advanced paradigms, DevOps is considered, and the pros and cons of the approach are discussed. As case studies an efficient combination of DevOps and CVS (Concurrent Versioning System) is discussed, titled GitOps, promising rapid AI deployment purposed to automate project stages as well as quality optimization. As an advanced process model, the above-mentioned GitOps approach provides multiple pros for the acceleration of secure and error-free code development, increased reuse of the source fragments, and cost reduction. The further advantages are formulated and proven as follows: (1) Single Source of Truth: easier to identify the causes of errors and to restore the infrastructure at any time; (2) Automation and Recovery: the entire deployment pipeline is automated; (3) Security: because changes to the infrastructure are controlled via pull requests and peer reviews, potential security risks and errors are reduced; (4) Portability: the entire infrastructure is defined as code what allows easy portability between different cloud platforms; (5) Multiple Stages: providing different stages for development and "hot fixes" in emergency.

This chapter discusses the growing importance of voice control systems in today’s digital world. They are becoming an integral part of our daily lives. They are used in a variety of areas, including smartphones, smart devices, industry, and transportation, changing the way we interact with our environment. In this context, special attention is paid to exploring different strategies for deploying such systems in terms of business requirements, scalability, flexibility, reliability and cost. For this purpose, we developed a comparative model to estimate the cost and performance of existing voice control systems. Two main voice control paradigms are compared in detail: embedded artificial intelligence (AI) and cloud services. The comparison has shown that during the initial months of development, the cost of a system with embedded AI per user is higher due to the need for additional resources. However, over time, these costs become similar to those incurred when using cloud services. The study also included an experimental evaluation of the accuracy and processing speed of the embedded artificial intelligence model and the cloud-based Speech-to-Text API. The choice between these approaches depends on the specific requirements and limitations of the application. If speed is crucial, embedded artificial intelligence may be the better option, but if precision and flexibility are priorities, then the cloud-based Speech-to-Text API with a custom algorithm can offer more advantages.

This work is aimed to explore the potential of advanced network technologies to support Artificial Intelligence (AI) applications and so-called Digital Ecosystems, with a focus on interconnecting both under improving user Quality of Experience (QoE). The work provides an analysis of current opportunities, challenges, and case studies and examines ongoing models and algorithms for future Digital Ecosystems: architectures, platforms, smart applications, and services. The slogan is as follows “Networks meet AI as well as AI meets Networking!”.

The digital landscape will experience the next disruption through the rise of Large Language Models (LLMs), which will be most probably adopted in practice in the coming years. Most prominently known from ChatGPT’s success, LLMs have produced a boom in different communities and inspired tons of possible usage scenarios. Now, when the situation has settled a bit, it is time to reflect on them and see which additional scenarios will be enabled by them and how they will influence the existing digital ecosystems. Thus, we classify the most promising scenarios of LLMs to be adopted in the coming years, but also sketch which challenges are appearing, and show how they will amend the current scenarios. The scenarios will be demonstrated in case studies of different business and technical scenarios.

The contribution is devoted to the defining properties of the future 6G Mobile Radio / NET -2030. The combination of SAT-Radio and mobile terrestrial services will be constructed on the new principles and cumulated ideas from the previous 5G and Beyond. The QoS advantages of the 5G structures, spectra assigning, modulation, and coding are discussed too. The proximity to the theoretical Post-Shannon-border is examined.

This position work is based on the best practices of Smart Grid, chosen case studies for energy efficiency, and an in-depth analysis of energy conversion problems in Germany. In addition, real examples based on the authors’ own experiences with energy-efficient Smart Home were conducted. The focus has been shifted to the requirements for modern power grids due to the volatility of renewable energies. Furthermore, the discussion of dynamic network control problems that can be solved by infrastructure approaches such as Smart Grid, NGN with 5G and Beyond, as well as Smart Home, and, certainly, by Artificial Intelligence (AI) methods that will play an increasingly important role. In particular, we proposed a proof-of-concept test-bed for AI-supported dynamic control of the smart grid. We have successfully developed a solar power generation forecasting model based on the Long Short-Term Memory (LSTM) algorithm and implemented an automatic underfloor heating control system that uses these forecasts to optimize energy usage in a private household. We achieved a high accuracy of 91

This work is dedicated to the Smart Office and Smart Home design: a brief smart survey of the used protocols, platforms, and best practices under consideration of the following criteria like price factor, easy configuring and manageability, data security, and privacy aspects, as well as energy efficiency. The practical problems for IoT and IIoT components compatibility as well as cloud independence, are discussed too. The authors mostly favor open-source and cloud-free solutions for Smart Home and established commercial platforms for Smart Office with advanced security, as shown in the presented case studies. Blockchaining of IoT and IIoT contributes to the compulsoriness and commitment in the decentralized world of "smart things" at home and in office rooms. However, secured smart devices can only be achieved with the combination of known crypto-technologies. Through a step-by-step provision of different blockchain-based platforms, the declared protection goals can be reached. The main features of the functioning of IoT, Edge, and Cloud computing technologies are considered. An intelligent IoT system has been developed to collect data from a specific location and transmit it to an Edge device for on-site processing. This data will be transferred to the cloud system or further processing or remote management, if needed. The solution aims to reduce the cost of maintaining the system by reducing the volume of messages that are sent to the cloud platform, which is quite expensive and a weighty factor in using the system, to provide remote management without buying a fixed server part. An algorithm for predicting temperature values in the server room of a smart office has been proposed and implemented in a designed IoT system based on Raspberry Pi 4 using an LSTM model. Using this solution in practice will increase the reliability of server equipment by early prediction, warning, and taking measures in case of temperature rise.

Emerging Networking (EmN) appears nowadays spontaneous and accompanies us in our everyday life as well as supports modern industry processes, ongoing digitalization of workflows and multiple Smart Home scenarios. This work is devoted to the Smart Home design: a brief smart survey of the used protocols, platforms and best practices under considering of price mirror, easy configuring and manageability, data security and privacy aspects as well as energy-efficiency. The practical problems for network and IoT components compatibility as well as cloud-independence are solved. The authors favor the open source and cloud-free solutions as it was shown in the presented case studies. Smart Applications become not only cloud-centric, but also container- and microservice-based.