Lero is the Irish software research centre. It brings together leading software research teams from Universities and Institutes of Technology in a coordinated centre of research excellence with a strong industry focus. Lero has raised the level and profile of Irish software research with such effect that it is now one of the best known and highly regarded software-related research centres in the world.
The centre has the proven capacity to attract and retain global research leaders and to make a substantial contribution both to software-related research and to the Irish economy.
The Lero Centre is supported by a Research Centre grant from SFI, by other state grants, by industry contributions and by external funding (particularly the EU’s research programmes).
Lero interfaces with a wide range of industry, state agencies, educational bodies and international collaborators to deliver on its twin goals of research excellence and social and economic relevance
Lero and DSG are engaged in several projects notably LAMP and DYSARM
LAMP (Learning-Based Autonomic Management Of Smart Grids) began in September 2011. It’s aim was to investigate the application of self-organising architectures to electrical supply and demand management. As society demands more renewable forms of energy and the management of energy grids must take place in the face of the emergence of multiple micro-producers, there is a growing need for autonomic management of power distribution. LAMP will apply reinforcement learning techniques to the coordination of agent-managed micro-grids. In particular, LAMP will apply autonomic management techniques for inter-agent collaboration at multiple levels of the power distribution hierarchy. A specific research focus of LAMP is the understanding of the limits of self-organising and learning based-techniques in an application domain subject to widely varying supply and demand and in which guaranteed quality-of-service agreements must still be maintained. As such, LAMP will draw significantly on our expertise in both self-organising systems and formal analysis.
The principal objective of this work programme is to investigate the design of algorithms to support effective autonomic/self management of community-based micro grids that are capable of offering specific quality-of-service guarantees.
LAMP will investigate the use of decentralised, agent-based autonomic management techniques for optimisation of community-based micro grids. In particular, LAMP takes as its starting point the use of Distributed W-Learning (DWL) (previously studied in urban traffic control scenarios) for the management of and undertake a simulation study of a community micro-grid. LAMP will design extensions or adaptations to DWL to address issues arising from the study of DWL in community-based micro grids. For example, extensions may be needed to improve scalability or responsiveness, to react to changes in the available sensor/actuator infrastructure, or the network topology and, more generally, to ensure that bounds on performance can be achieved.
Model-Driven Development of Smart Vehicles (MDDSV) investigates smart vehicles that can communicate with other vehicles in real time have the potential to support more sustainable transportation. The big challenge today is to enable efficient coordination among smart vehicles to further increase the safety and efficiency of the traffic. Distributed coordination of smart vehicles is a non-trivial problem because vehicles are in a highly dynamic environment, moving at a high speed, and communicating with dynamic participants over an unreliable wireless network.
The key challenge addressed by the MDDSV project is the derivation of safe and efficient algorithms for coordination between smart vehicles
modelled as mobile real-time systems. One objective is to investigate model-driven techniques for the development of software
to support future smart vehicles modelled as semi-autonomous, mobile, real-time systems. Another objective is to investigate mathematical techniques
to coordinate autonomous vehicles in mixed traffic environments, i.e. when they share the road with conventional vehicles.
Dynamic Service Adaptation using Model-Driven Engineering (DYSARM) envisions a Smart City that will adapt the services it provides to the needs of its citizens, and to conserve and preserve its resources. Such adaptation must occur in real-time, and may not be anticipated and designed for in advance. New software models are required to support reasoning about the run-time behaviour of the City’s services, assist in the adaptation decision-making process, and affect the required adaptation in real-time. Run-time models that reflect the state of execution of running systems enable the specification of required adaptations. These models can reduce the time taken to comprehend system behaviour, identify problems, propose changes and verify the results of proposed changes.
DYSARM will develop a runtime model environment and toolkit that will improve the understandability and, consequently, the reliability of adaptations in dynamic service-oriented computing applications through support of, at the model level: adaptation specification; monitoring and validation of the executing system; and automated adaptation of services at runtime. With models providing views of the executing system, its impact on the behaviour of the city structures and services and its continued adaptation, the designer can reason over the semantics and current state of the system, and also the adaptation, at an appropriate level of abstraction
Personal Cities (Self-Organising Architectures For Autonomic Management Of Smart Cities) focuses on the fact that an increasing proportion of the world’s population now lives in cities placing an increasing strain on their transportation, security, business, water-management, and energy-supply systems. To improve quality of life and ensure sustainability, future “smart cities” will increasingly rely on information and communications technology to optimize the performance of these systems. Our research is investigating the design of the middleware needed to support such smart cities focusing on the exploitation of data from very diverse sensors, especially those contributed by individual citizens, to enable autonomic management of city-scale services while respecting the privacy of individuals. In particular, we are investigating the design of new middleware architectures, communication infrastructures, and advanced analytics to allow sensor data to be gathered, transported, and interpreted effectively to optimize urban resource usage and service delivery. This work will contribute to Ireland’s economy by stimulating a pipeline of new products, services, and companies to service what is a growing international market.
SURF: : Service-centric network for urban-scale feedback systems(2014-2018) is a 4-year SFI project on Service-centric network for urban-scale feedback systems. This project aims to answer is how can we provide a platform for effective, demand-driven composition of embedded sensing, communications and computing resources in urban environments that supports urban-scale dynamic composition, monitoring and control of services to optimise the use of urban infrastructure and resources? The underlying assumption is that demand-driven composition of sensing, communication and computing requires a virtual infrastructure capable of scalable, dynamic management of the increasingly large numbers of embedded sensors, smart phones, small-scale wireless city hotspots, and larger urban mobile network cells.