The AI-Driven Heritage Building Conservation and Retrofitting (AI4HERITAGE) project is a ground-breaking initiative aimed at preserving historical structures while advancing sustainability. This innovation is a comprehensive solution that leverages artificial intelligence (AI), the Internet of Things (IoT), and advanced sensor technology to optimise the conservation and retrofitting of heritage buildings. What sets this project apart is its holistic approach, seamlessly combining heritage preservation with energy efficiency and sustainability. In summary, AI4HERITAGE solutions improves business productivity and efficiency in the construction sector by streamlining documentation, enhancing structural analysis, preserving materials, providing restoration guidance, enabling efficient virtual reconstruction, offering data-driven decision support, optimising planning and monitoring, facilitating remote detection, enhancing engagement and education, and reducing costs. These improvements not only benefit conservationists but also contribute to the long-term preservation and sustainability of our cultural heritage.

Connected and Autonomous Vehicle applications are being designed with the expectation of ubiquitous communication to the cloud and will be severely affected by inadequate network coverage. The data needed to make critical decisions have to be accessed via the cloud, but the inevitable unpredictability and unreliability of any mobile network may render that data irrelevant at best and potentially hazardous at worst and data cannot make a round trip to the cloud in time to process the large amount of information requiring a large amount of algorithmic processing that essential to be executed in real-time for decision making demands capable of sensing and navigating the environment around the vehicle. Transferring such massive amounts of data has proved to be prohibitively expensive. While more complex Radio Frequency (RF) modulation can increase data rates and software multiplexing can increase the number of simultaneous connections, the basic user-device-to-base-station Air-Interface remains unchanged from the earliest days of mobile phone networks. Due to the lack of bandwidth at the lower end of the RF spectrum higher frequencies are needed to propagate these faster data rate carriers. Antenna diversity techniques, such as MIMO, can improve RF path loss and decrease interference but the fundamental need for a user to stay within range of a base station / access point is still paramount. Unfortunately, overall path attenuation at the higher frequency is far greater and so there will be a need to substantially increase the number of base stations in order to provide coverage, due to the cell tower densification issue. The complexity of Autonomous Driving Systems (ADS) is increasing at an unprecedented rate, with increased sensor and software algorithms. The computational processing required is equivalent to server performance than traditional automotive embedded processing requiring centralised gateways managing multiple cross-domain and processing capability. This consolidation requires a dramatic change in software architecture for the data to be processed and distributed securely and safely, at very low latencies, between various domains. Current estimates predicts fully-autonomous vehicles will require one billion lines of code for the processing required to provide the situational awareness to replace and enhance the human driver behaviour. The requirement for communication as currently preventing us from reaching and producing safe Level 4 and Level 5 autonomous vehicles. The Above the Cloud Project will develop a Connected Car Software Defined Vehicle architecture capable of handling the vast data flows and processing it in near real time.