This study proposes a modular water monitoring IoT system that enables quantitative and qualitative measuring of water in terms of an upgraded version of the water infrastructure to sustain operational reliability. The proposed method could be used in urban and rural areas for consumption and quality monitoring, or eventually scaled up to a contemporary water infrastructure enabling water providers and/or decision makers (i.e., governmental authorities, global water organization, etc.) to supervise and drive optimal decisions in challenging times. The inherent resilience and agility that the proposed system presents, along with the maturity of IoT communications and infrastructure, can lay the foundation for a robust smart water metering solution. Introducing a modular system can also allow for optimal consumer profiling while alleviating the upfront adoption cost by providers, environmental stewardship and an optimal response to emergencies. The provided system addresses the urbanization and technological gap in the smart water metering domain by presenting a modular IoT architecture with consumption and quality meters, along with machine learning capabilities to facilitate smart billing and user profiling.

Towards the advent of the sixth generation (6G) wireless networks, smart technologies play a key role in the end-to-end automation of services across multiple domains. In particular, they create a new reality with multiple benefits, including intent-driven management, ultra-speed communication services and holistic integration within the Internet of Things (IoT). In this context, the Zero-touch Network and Service Management (ZSM) group within the European Telecommunications Standards Institute (ETSI) aims to provide an architectural framework which will allow the zero-touch orchestration of network services in a multi-domain fashion. Therefore, in this paper, we investigate the role of cross-domain storage, communication and analytics services within the architectural framework of ETSI ZSM. For this purpose, we take into account a particular case study which focuses on the orchestration of security services within the 5G core. More specifically, the deployment of a new User Plane Function (UPF) equipped with intrusion detection services that leverage Artificial Intelligence (AI) is investigated. For this purpose, the aforementioned cross-domain services are used to assess the security of the respective AI models before the onboarding of the new UPF within the 5G core. Based on this case study, a new security game is investigated, exploring and modelling the strategies of potential attackers and defenders. Furthermore, the architectural design and implementation of the cross-domain services are provided. Finally, the evaluation results show that the cross-domain analytics services are able to assess the security and resilience of the AI models and guide the orchestration functions.

The SARS‐CoV‐2 pandemic has heightened the need for advanced and automated disinfection methods to ensure workplace safety and hygiene. This study presents the de‐ sign and implementation of a robotic vehicle capable of autonomously disinfecting high‐risk areas in diverse work environments based on human activity levels. The sys‐ tem integrates a machine vision module using YOLOv5 for real‐time human detection, a Decision Support System to assess contamination risks, an autonomous navigation module for path planning, and a user‐friendly interface for operator control. By leveraging real‐time data, the robot precisely applies disinfectant to identified high‐risk zones, dynamically adjusting the spray volume based on the level of contamination. The system was validated in a real‐world workplace setting, demonstrating its ability to autonomously perform targeted disinfection, offering a scalable solution to support workplace hygiene.