Science of Sensor Systems
Sensor systems are embedded everywhere: from transportation and lighting, to smart tags and flooded fields, providing information and facilitating real-time decision-making and actuation. Smart cities, internet of things, big data and autonomous vehicles all depend on robust sensor systems that can be trusted to deliver useful, timely and more reliable information.
Extracting information is far from straightforward: sensors are noisy, they decalibrate or may be misplaced, moved, compromised, and generally degraded over time, both individually and as a collective network. Uncertainty pervades the physical and digital environments in which the systems operate. There are increasing requirements to add more autonomy and intelligence, yet we understand very little about programming in the face of such pervasive uncertainty that cannot be engineered away. How can we be assured that a sensor system does what we intend, in a range of dynamic environments? How can we make such a system “smarter”? How can we connect the stochastic nature of environments, the continuous nature of physical systems, and discrete nature of software? Currently we cannot answer these questions because we are missing a science of sensor system software. The S4 programme will develop a unifying science, across the breadth of mathematics, computer science and engineering, that will let developers engineer for the uncertainty and ensure that their systems and the information they provide is resilient, responsive, reliable, statistically sound and robust. The vision is smarter sensor based systems in which scientists and policy makers can ask deeper questions and be confident in obtaining reliable answers, so the programme will deliver new principles and techniques for the development and deployment of verifiable, reliable, autonomous sensor systems that operate in uncertain, multiple and multi-scale environments.
What are the fundamental questions about how to design, deploy and reason about sensor-based systems. How can we develop new principles, techniques and tools, alongside simulations and physical sensor test beds for experimentation to answer these questions?
The UK-wide programme of research is led by Prof Muffy Calder of the University of Glasgow alongside senior staff from St Andrews and Liverpool universities and Imperial College London, the Science of Sensor Systems Software (S4) project brings together expertise across computing, engineering, and mathematics. It aims to develop new principles and techniques for sensor system software which is hoped will help development in a range of sectors, including the development of more robust water networks, air quality monitoring, reliable autonomous driving and precision manufacturing.