Enhancing critical ecosystems
Using data analysis to enhance critical ecosystems like cities and farms, and the digital-physical systems that support them
Algorithms Mathematical modelling Dynamic/static (Mathematical modelling) Privacy & trust
This work aims to explore how combinations of sensing instrumentation, actuation, and a spectrum of data analytics can improve and protect the assets within ecosystems such as a cities and farms. The work also aims for 'joined-up thinking' about how such assets interact and impact upon each other in an ecosystem. The work has the potential to improve the long-term safety of such systems and the safety of people and property.
Explaining the science
An example of how critical ecosystems are considered is how water companies want to optimise customer quality of service and pipe lifetimes, but are not really concerned with transport infrastructures, and vice versa. Imagine a future where when one network detects a leak and the road traffic is automatically re-routed to avoid this. Better still the water network predicts a leak and schedules maintenance activities. This would enable more efficient ecosystem operation which in turn can make resources more sustainable.
Such a 'joined-up' system has the potential to improve the long-term safety of such systems (i.e. water delivery and food production) and the safety of people and property.
Project aims
This multi-disciplinary project brings together researchers and user partners with expertise across sensor networks, food production (farming), and water networks. It addresses key priorities, end user needs, and challenges, with a diversity that means the solutions produced have the potential to be truly transformative and impactful.
The work is scheduled to run over five years and aims to answer the following overarching research question: Through data analysis, how can we enhance critical ecosystems and the cyber-physical systems that support them continuously?
The main research objectives are:
Understanding sensor selection and placement to optimise observation
Understanding edge device analytics algorithms to improve the efficiency of IoT-based systems, maintaining usefulness of the data from sensors
Deriving alternative control methods (valve closure, fertilizer release etc) that incorporate advanced event-based control, aperiodic control, and protocols/algorithms to support this efficiently
Experimenting with data-driven modelling of critical infrastructures and dynamic model update
Exploring how the physical world, self-monitoring, privacy, and security impacts the trustworthiness of data for such systems
Taking water distribution networks and precision agriculture as two examples of critical infrastructure, we will initially instrument a sensing computing infrastructure to obtain data from each type of critical infrastructure. Using data-driven modelling, the operation of these computing infrastructures will be mapped to a control model and the control devices will then be deployed to close the loop.
Water distribution networks are large scale and topologically complex with many constraints that impact their operation (customer demand, weather, pipe lifetime etc). The main barriers for such systems are cost and resilience. In these kinds of systems, the data required to operate the control loop, guarantee computer system and water systems lifetimes, and keep both types of system secure, is not necessarily all the same, therefore understanding these trade-offs is very important.
Agriculture also brings large scale and a diversity of data (soil, foliage, weather etc), but also the need to convince farmers to trust in precision agriculture systems. However, farmers do acknowledge the benefits that can be obtained from being part of a network of farms (e.g. early warning of infestations etc.).
Dr Din-Houn Lau
Lecturer in Statistics, Imperial College London
Professor Julie A McCann
Data-Centric Engineering Strategic Leader