Use Cases in Living Labs
The Living Labs in XGain were defined as a human-centric, open-innovation ecosystem of relevant stakeholders, focusing on user support, service provision, and broad adoption. Through the Living Labs, researchers and innovators observed and understood user behaviour patterns, including those that were not immediately obvious.
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XGain aimed to demonstrate the developed Knowledge Facilitation Tool and the results of each application across a series of heterogeneous use cases, including location, connectivity needs, local requirements, edge and connectivity potential solutions, and operational business models. The initial business models provided for the use cases were also evaluated according to the project methodology, in parallel with the assessment of the proposed ecosystem of technologies.
Level of Assessment:Â Rural Community
Services: a) Drones operation in rural areas, and b) Additional services with high data rate
Leader:Â I2CAT
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Trial description: A set of drones were deployed at the pilot site location, connected via 5G to the operations centre that hosted various services such monitoring (i.e., live video streaming clients) assisted by video/image processing tasks. High bandwidth, ultra-low latency and reliable 5G communications were exploited to serve the fleet of 5G connected drone operations. The drone centre can be either indoors or outdoors. To enable the envisioned use case, an end-to-end 5G network was deployed (5G UE, drone, 5G base station operating at 3.5 GHz and a virtualized 5G Core) and edge-computing capabilities.
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Main outcomes: The evaluation of how a drone centre could impact on the local community of Mora la Nova and its surroundings has shown strong potential. On one hand, the use of advanced digital technologies, in this case a 5G private network with edge computing capacities, to improve drone services has convinced a wide range of rural stakeholders who see clear benefits. On the other hand, the concept of a drone centre awoke interest and a strong participation of local stakeholders, who identified many interesting scenarios for public and private scenarios. They realised how the advanced digital technologies combined with drones resulted in a high potential of improvement and innovation of rural services.
The technological performance evaluation proved that the choice of 5G in combination with edge computing capacities delivered the expected performance and leaves margin for additional developments and improvements or variations which consider the use of extreme edge devices. In both network and computing aspects, use case demonstrated and validated that private 5G networks have a very high innovation potential, being flexible and capable of hosting varieties of services that require connectivity and processing capacities.
Beyond the technical and operational validation, the strong social acceptance of the drone operations centre concept emerged as a key outcome. Stakeholders from the local community, including public authorities and private service providers, expressed a high level of interest and support for the initiative. Their active participation in workshops, interviews and demonstrations reflected both curiosity and a willingness to engage with advanced digital technologies.
Level of Assessment:Â Region
Services: a) eHealth robots for elderly, b) High data rate services, and c) Tourism
Leader:Â CAP
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Trial description: The scenario aimed at providing health and wellbeing guidance and interventions for people living in rural areas. 5G was exploited to deliver natural language processing (NLP) services and for real time interactions/interventions with/to the respective individual. CAP supported the social interaction of people in rural areas by providing means of interaction through the Robot device including mainly videoconferencing, social gaming, and interaction with other users exploiting the full potential of the enhanced mobile broadband service and low latency transmissions of 4G/5G. In parallel, services requiring high data rate like e-Learning, as well as backhaul connectivity options were explored and tested in lab environment.
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Main outcomes: The use case implementation within the XGain project has demonstrated a promising approach to delivering socially meaningful digital services in remote and rural regions. Through the deployment of ten CAPTAIN Box devices in various areas across Central Macedonia, the project validated a lightweight, adaptable infrastructure capable of supporting streaming-based social engagement services for older adults.
Beyond the technical validation, a dual-layer impact assessment was conducted. On one level, structured self-report instruments, including the General Self-Efficacy Scale, UCLA Loneliness Scale and EQ-5D-5L, were used to assess the system’s influence on the psychological well-being, social connection and perceived health status of older adult users. On the other level, feedback from stakeholders gathered during the final workshop revealed widespread optimism about the role of digitalisation in improving job creation, economic yield and ecosystem development in rural areas.
A key success factor in the use case implementation was the high level of social acceptance observed among older adults and their immediate social networks. The unobtrusive nature of the CAPTAIN Box, combined with its intuitive use and low maintenance requirements, contributed to a positive user experience. Older adults expressed appreciation for the opportunity to engage with digital services without requiring advanced digital skills or disruptive changes to their daily routines.
Level of Assessment:Â Community/Island
Service: a) Precision Agriculture and b) Tourism
Leader:Â CAFA
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Trial description: the use case evaluated different solutions for improving network connectivity and enhancing agricultural practices. The focus was to measure soil humidity, soil and air temperature, soil EC (electrical conductivity) and send all the data to a cloud server in real time, where a farmer could see graphs and live data from different fields. For network connectivity improvements, we had a drone equipped with a Wi-Fi router and also a Starlink connection, which was mounted onto a UGV (Unmanned Ground Vehicle). Multiple tests were made to find out the best option on St Martin’s to improve network connectivity.  For computer vision an UAV (Unmanned Aerial Vehicle) was used with camera with colour detection. Custom-made software for colour detection was made and tested in different environments. Colour detection allows for the identification and prevention of soil damage, crop loss, or the presence of foreign objects in the field.
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Main outcomes: In the remote archipelago of the Isles of Scilly, the availability of 4G and 5G mobile connections is limited and variable due to the topography of the Islands. Across the Islands, there is a farming community that uses a certain number of digital devices presently, but is not using and aware of the full range of devices on offer to help their businesses. The use case helped those farmers choose devices that could be helpful to improve their businesses and farm management practices. The use case provided novel and viable ways to connect to high-speed internet from rural locations.
From an impact perspective, this approach has the potential to bring tangible benefits to rural communities. Improved access to real-time data can support smarter farming practices, reduce manual labour and optimise resource use. It also enhances the ability of small communities to monitor environmental conditions, detect infrastructure issues, or deliver goods more efficiently. Community feedback in the Isles of Scilly highlighted increased awareness of how digital tools can directly improve agricultural productivity and logistical resilience. Crucially, the deployment also encouraged local engagement with technology, fostering digital skills and sparking interest in future innovation.
The impact assessment confirms a strong mandate for expanding digital infrastructure on the Isles of Scilly: 60% of respondents expect noticeable economic gains for local enterprises once connectivity improves and 80% are likely to adopt the proposed technologies. Equally important, every interviewee believes that exposure to new tools will lift digital literacy—addressing a need already identified among several farmers.
Level of Assessment:Â Community/Region
Service: a) Precision Agriculture, and b) Forest Management
Leader: BETA VIA (ex ART21)
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Trial description: Use case was tested on Lithuanian Forests in the rural area of Vilnius and Ignalina county. Vilnius county was chosen for the convenience of the location, with 30-minute drive from the city centre (office location) and Ignalina county, due to the more than 80% forest coverage of the territory. The places for initial testing were chosen by the telecommunication network’s (un)availability. AB Telia provides a map where the network coverage is visualised on the map, distinguishing by colour the areas where the 5G network speed is Good, Average, or Available. In this UC, we have tried various locations with various coverage of the mobile network internet. Internet connection in the office is through a fibre connection that could be up to 1Gb/s, while mobile network connection announced by the mobile network provider of 4G/5G can reach the speeds of up to 2 Gb/s. However, the network connection is very drastically different from place to place and in the woodland environment the network connectivity drops to almost no network. Use case aimed to overcome the limitations of the internet connectivity via mobile networks in the woodlands using Telia AB 5G network as a test case. Through the use of drones and the internet connection in the air, the woodland obstacles were avoided with the direct connection to the mobile network tower. The UC4 aimed to detect forest fire-prone areas with a hyperspectral imaging camera, thermal camera and the RGB camera, which were used as different scenarios for test drone flights over the same area.
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Main outcomes: Use case deployment demonstrated the viability and benefits of integrating drone-based monitoring with edge computing and 5G connectivity for forestry and farming applications in rural Lithuania. The technology exceeded performance expectations across all key indicators, including data transmission reliability, flight duration, area coverage and network capacity. Social, economic and environmental impact assessments revealed strong community interest, potential for job creation, improved efficiency and significant reductions in COâ‚‚ emissions and biodiversity loss risk. Importantly, the system enabled more accurate, timely and collaborative decision-making processes for forest fire prevention. While scalability remains a concern, the results suggest that with appropriate support from local authorities, mobile operators and community stakeholders, this technology can serve as a replicable model for enhancing rural resilience and sustainable land management across diverse European regions.
Level of Assessment:Â Farm/Community
Service: a) Water quality monitoring, and b) Remote oyster farming
Leader:Â BENCO
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Trial description: The use case demonstrated how an extended network infrastructure (supplemented by LoRaWAN and satellite comms) can be used to transmit data from offshore sensors to the central monitoring system. Particularly the sensors were connected to the receiver (stations floating on water, float/buoy aid) via underwater cables. Multiple nearby stations were connected to a LoRaWAN network, where an edge device aggregates data, pre-process it and transmits (either raw or processed) data to other existing platforms and monitoring services. To retrieve data from the edge device the satellite IoT network of Astrocast SAÂ was employed.
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Main outcomes: Use case initiative successfully demonstrates how an integrated network infrastructure combining LoRaWAN, satellite communication and edge computing can enable real-time remote water quality monitoring in aquaculture. By transmitting sensor data (temperature, salinity, dissolved oxygen, etc.) to a centralised cloud platform, the system provides end-users with actionable insights through an intuitive dashboard, reducing the reliance on manual checks and improving operational efficiency.
In terms of social acceptance, stakeholders have positively assessed the use case. Most importantly, due to the reduced human intervention in monitoring water quality parameters. Since conducting research takes a lot of time, the technology we have implemented allows it to be done from anywhere without any effort. Moreover, constant training and education about the benefits of the technology would positively affect the further development and advancement of the technology.
Level of Assessment:Â Farm/Community
Service: a) Livestock health and b) Farm management
Leader:Â EVILVO
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Trial description: Use case explores how camera-based precision livestock farming (PLF) technologies can improve animal monitoring in rural dairy systems by leveraging low-power edge computing and smart connectivity. The trial was carried out on a dairy pasture in Flanders, Belgium, where the goal was to assess whether lightweight, solar-powered AI systems can operate reliably in real-world farm conditions and meaningfully contribute to digital transformation in agriculture. The evaluation approach combined technical validation (system latency, throughput and resource utilisation), impact assessment (including detection and behavioural classification accuracy) and end-user engagement through co-creation and training activities. This allowed the team to gauge both the feasibility of deploying such systems at scale and their perceived value by the farming community. By targeting the intersection of energy efficiency, cost-effectiveness and digital literacy, use case demonstrates a practical pathway for applying advanced AI tools in extensive livestock systems—without compromising simplicity or affordability.
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Main outcomes: The evaluation of use case demonstrates the technical feasibility, environmental sustainability and practical relevance of deploying a camera-based monitoring system for pasture-based cattle farming. The system, based on edge AI and low-bandwidth connectivity, successfully operated under real field conditions, achieving high levels of behaviour classification accuracy (97%) and localisation precision (0.89 m), while maintaining low power consumption compatible with solar setups. These results confirm the suitability of the proposed technology mixture for remote livestock monitoring in low-infrastructure contexts. Beyond technical performance, use case also highlighted broader impacts on skill development, farmer engagement and sustainability awareness. The system proved especially valuable for farms with pastures located far from daily operations, offering a reliable method for remote visual check-ins. While farmers appreciated the system’s potential, their feedback also revealed important concerns around privacy, integration with existing herd management systems and the risk of misuse of visual data. Addressing these concerns will be critical for increasing user trust and ensuring wider adoption.
In terms of social acceptance, the use case was generally well received by stakeholders, especially due to its non-invasive nature and the reduced need for wearable devices on animals. Farmers expressed a strong interest in digital tools that support animal welfare and pasture efficiency. The participatory design process helped build trust and ownership, fostering a positive attitude towards adoption. Continued transparency, training and support will be key for scaling the system in socially acceptable and responsible ways. The insights gathered here inform not only replication efforts in other European regions but also broader recommendations for smart farming policies.