ELASTIC experts from the Barcelona Supercomputing Center (BSC) attended the Smart City Expo 2021 held from 15 - 17th November 2021 with the organization of a small booth at the Catalan Government, Generalitat de Catalunya. Around 200 interested attendees were interesting looking at the demonstration where a real-case scenario was displayed to show the software developed at the city of Florence in Italy. This occasion let the ELASTIC project network with other interested stakeholders from the smart cities in Europe as well as other related potential providers. 

Written by David Faura, Advanced Studies Engineer, THALES Research & Technology

The H2020 ELASTIC project addresses the entire compute continuum from the datacenter to the geo-distributed computing nodes, from the cloud to the edge. The purpose of the project is to develop the basic technological building blocks enabling the use of Information Technology (IT) domain software and hardware components in order to control the correct execution of applications and the correct management of operations in a geo-distributed infrastructure located in a smart city context.

To illustrate our point, we can take as an example one of the ELASTIC use cases: the edge/fog-enabled "Remote Sensing/Advanced Driver Assistance Systems (ADAS) from infrastructure" application for the city of Florence. This use case considers a distributed infrastructure of smart cameras and computing and communication networks placed along the tramway track. The objective is to be able to detect vehicle accidents beyond the capacity of the tramway sensors/radars and to prevent accidents in case of no visibility due to urban objects occulting vehicles or pedestrians. This new technology will have a major impact on the safety of the tramway by preventing future accidents through proactive braking of the tramway in case of an upcoming hazard detection while avoiding dangerous emergency braking. This will ensure a high level of protection for tramway passengers and the environment (pedestrians, bikes, cars, etc.).

Figure 1: ELASTIC Remote Sensing Workflows

ELASTIC is the first step toward an industrial compute continuum infrastructure, which has the ambition to manage industrial critical systems, a concept known as Operational Technology (OT). An industrial compute continuum is based on a computing infrastructure that can handle the IT/OT convergence while ensuring at the same time the high level of dependability required by OT systems, the flexibility of IT systems, and the resilience of fault-tolerant systems.

In order to address real-time constraints imposed by industrial systems, it is necessary for the underlying computing infrastructure to be able to process data and perform analyses as close as possible to the data sources while ensuring data privacy and data security. In addition, the computing infrastructure must be able to orchestrate and distribute micro-services, data-pipeline, cloud applications (legacy and new generation) and critical applications across the continuum, without interfering with the critical environment and fulfilling both Functional and Non-Functional Requirements (NFRs) required by critical applications.

Figure 2 : Next generation of Compute Continuum dedicated to the industrial domain

An essential step towards the management of an industrial critical system is to enhance IT infrastructure with distributed monitoring capabilities allowing real-time measurements of the computing environment and deployment decisions based on non-functional properties identified as critical. Work on this technical issue has already started within ELASTIC through the development of the NFR tools associated with the compute continuum resource management solution, which in ELASTIC is supported by the Global Resource Manage and the COMPs scheduler. These enhanced monitoring capabilities ensure that applications and hardware elements forming the compute continuum respect all the measurement points defined "at design time" by the non-functional requirements (time, energy, communication quality, data protection, safety and security). In the event of non-compliance with the usage domain the local monitoring components record and report any deviations to the global resources manager to provide safe and secure recovery decisions at the system level.

In conclusion, we consider that the industrial compute continuum must be composed of safe and secure edge/fog computing devices connected by efficient and resilient hybrid wired/wireless data networks. This can be achieved using hardware and software components which follow the tenets used in the development of critical systems (or at least for some parts of its infrastructure). Furthermore, the continuum must be able to guarantee different levels of confidence and isolation to ensure concurrent hosting of both critical and non-critical applications (mixed-critical properties). Finally, the continuum must be able to provide assurance evidences that the geo-distributed infrastructure is operating in a safe and secure state while ensuring that it does not create any unacceptable risks to humans, the environment or its mission (business impact).

Industrial compute continuum take away:

• Therefore the IOTIoT, EDGE/FOGedge/fog devices and their software components need to be Industrial domain grade. Who pays?
• NFR (incl. Safety and Security) must be handled at the platform level and not only at the application level.
• A joint safety and security approach is strongly recommended while remaining compatible with systems ranging from semi- to fully autonomous.
• Control and command will spread along the continuum transforming legacy embedded systems into communication hubs.

Pandora’s box is open!
 

Written by Cesar Marin, Information Catalyst

Big data is a topic widely regarded as important because of the common problems faced by organisations that see their data increase over time. However, just “applying big data analytics” out of the box is not so simple to implement even with existing tools available these days. Common tool providers assume data owners want to apply traditional data analytics such as adjusting predefined models seen in other cases but applied to new ones. However, this is not always the case.

Each application is unique and predefined models not always apply to all cases. It is becoming more common to find data owner that realise their case does not fit the traditional case and end up telling to themselves “I have the data but I don’t know what it means. Can data analytics be of any help?” This situation requires some thinking: how can we help data owners discover data patterns without using predefined data models?

The ELASTIC project recently faced this situation. Having created predefined data models for the transport domain would have provided a too accurate but rigid model, which could have been sufficient in a world where nothing changes. However, things changed drastically at the beginning of 2020 with the Covid-19 pandemic: transport patterns changed overnight in such a way that nobody could have predicted; suddenly people hugely reduced transport usage; public transport service was reduced to a minimum; and there was no way to tell when or if it would be back to “normal”. How is ELASTIC dealing with this situation?

The ELASTIC project has three ways of supporting a transport infrastructure that has been hardly hit by Covid-19:

1.    Data is generated by a number of real-time analytics distributed across the transport network. This data represents entities such as cars, people, trams, etc. moving in the city. This data is then collected via a distributed data analytics platform (DDAP) that functions as a Content Delivery Network, collecting data from edge devices in trams, passing it through fog computing cabinets at each tram stop, and propagating the data up to the data centre at the tram depot. Data is available for processing both for real-time and historical purposes and completely independent of any transport situation.

2.    At the data centre in the tram depot, data is passed through a series of micro filters and micro aggregators that separate data into smaller units with their timestamp and combine them in meaningful ways, respectively. Once this is done, pattern finding algorithms are applied to the resulting data. These algorithms do not apply any predefined model nor expect any “known pattern” to be found. They discover patterns hidden in the data. If data changes over time, say because of new transport patterns triggered by new Covid-19 responses, these are also discovered as more data accumulates. This increases the reliability of the ELASTIC architecture to support changing transport conditions due to the current pandemic.

3.    Nevertheless, pattern prediction is also present in ELASTIC architecture for detecting and predicting track wear, although not done in a traditional way. Track profiles are obtained via a specialised laser measurement unit under a maintenance vehicle. Then another algorithm processes that data for finding hidden patterns, but rather than fitting data to predefined models, it creates and maintains different data models in parallel. Over time these models learn more, get merged with similar ones, or simply disappear if no longer relevant. This model management mechanism allows the pattern prediction tool to adapt to changes happening in the tram network that are reflected in the track wear data. Therefore, it makes ELASTIC a robust architecture that can stomach unexpected changing data patterns without prior knowledge.

Furthermore, pattern finding algorithms are not sufficient without a variety of visualisation techniques to interpret data. One of the tools provided by ELASTIC, based on Superset, consists of an analytics dashboard. This tool allows the user to choose the data they want to explore and how they want to see it. Users have a variety of nearly 50 different graphs to choose from and create different dashboards according to their needs.

ELASTIC is still an ongoing project and it is currently being applied in the transport infrastructure of the Metropolitan City of Florence in Italy. Florence was one of the first locations hit hard by Covid-19 in Europe. The transport network and all its suppliers, just like many similar networks across Europe, have suffered the effects of partial or total transport shutdown because of the pandemic. But the city has shown its resilience and has been adjusting to many “new normal” situations. It just makes perfect fit to equip the city with a software architecture as resilient and robust as its transport infrastructure.

We at ELASTIC are proud champions of equality, diversity and inclusion and we try to do our little bit to contribute to these values in the community. Along with the mission of the European Commission, we always aim to find new ways of showing our support for fair treatment and opportunities for all.

In this vein, we are launched our Women in STEM interview series, which features female scientists that are vital members of ELASTIC. In these interviews, our researchers talk not only about their role in the project, but also their motivation and experience as women in STEM. Through their words, they deliver their own motivational messages to young girls and women who are interested in pursuing a career in these fields. You can find the interviews below:

• Elli Kartsakli from BSC
• Flavia Gaudio from GEST
• Viola Sorrentino from THALES
• Rita Sousa from ISEP
• Anna Queralt from BSC

Our partners are strong supporters of diversity and inclusion and are aware of the importance of their social responsibility. Thus, they have clear policies on how to fulfill their civic duty in benefiting the whole of society. Have a look at our individual partner policies:

• BSC: Gender and Diversity Equality Plan and Bioinfo 4 Women
• IKERLAN: Committed to our environment and Gender Equality Commission
• THALES: Diversity, Equality and Inclusion
• GEST: Corporate Social Responsibility
• Citta Metropolitana di Firenze: Equal Opportunity and Equality Advisory (in Italian)

Finally, we enjoy being part of community initiatives that raise awareness and celebrate these causes, such as the International Day of Women and Girls in Science on 11 February and the European Diversity Month in May. Recently created, our equality and diversity page summarises all of our related activities. Stay tuned!