Mesh network for public response and prediction of critical scenario

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Proposed by:

Entity Logo:

Summary of the entity:

CONNECT5 – DIH for Connectivity, CPS, IoT, Cloud/Edge, and Data Analytics is a national and European DIH supporting the digital and green transformation of SMEs and public organizations. CONNECT5 is a collaborative network, in the form of a Consortium of 12 entities (RTO, polytechnic institutes, universities), with a high level of expertise in digital technologies, state-of-the-art infrastructures and a deep nationwide network of contacts (companies, business associations, public entities, etc.) bringing together their complementary expertise and assets, to create a unique value platform. 

CONNECT5’s will leverage its capabilities based on Cyber-Physical Systems (CPS), Internet of Things (IoT), Communications networks (5G and 6G), infrastructures (Cloud and Edge Computing), Big Data platforms, and analytical data processing, supported by Artificial Intelligence (AI), Cybersecurity and High-performance computing (HPC) when required, enabling the target entities to evaluate its impacts and the feasibility to their business before further investing in it. 

The services provided by CONNECT5 will be based on 4 main domains:  

                           i.Test before investing;  

                          ii. Skills and training; 

                         iii. Support to find investment; 

                        iv. Innovation ecosystem and networking.  

Included in the action plan are also the Digital Maturity Assessment, transformation proposal, and acceleration with the DTA. 

CONNECT5 will provide a wide range of services that will enable any beneficiary to access the latest knowledge, expertise, and technology with a special focus on the connectivity dimension for testing and experimenting with digital innovation relevant to their activity, matching their needs with digital solutions already available or in an advanced phase of deployment.

Summary of the challenge:

Mesh network for communication and interoperability of services in a critical environment, providing support for real-time decisions at the edge

Description:

Stakeholders:  Municipalities, Emergency Responders, Healthcare Services, Civil Protection Agents; Police; Traffic Managers; Environmental Agencies; Disease Control Agencies; 

Description: 

The presented challenge must propose using a deterministic model to develop a mesh network. Through this type of network, it must be possible to minimize communication latency between devices, guaranteeing the prediction of an event in a specific time window. For example, data available in national agencies like historical aggregated and classified data including weather conditions, terrain models, previously identified risks, critical assets, infrastructures, and hazards (for example, current ignition in industrial facilities or forests), among others.  

Within the network, it will be possible to use the devices allocated in the nodes to detect risk situations and/or critical scenarios. In these nodes, real-time edge analytical solutions must be applied, namely, to forecast risk changes and asset positioning, and alerts sent when an event is predicted and/or detected, ensuring data interoperability and sharing. It must be efficient in collecting and processing data to provide smooth real-time reporting, critical updates, and actionable intelligence. 

Each node must also host, run and reinforce learning models used for pre-processing in the platform. Each node will host and publish relevant datasets and update the central ones with observed facts and decisions taken.

For accountability purposes, both ML.-learning model versions and data used for training must be saved and updated both at the nodes and in the central instance.

Data:

DIH Data Provider:

The data needed to implement the aforementioned scenario should cover all the aspects related to crisis management. Such datasets should include the actors of the emergency scenario, risk management, the hazard itself, nearby points of interest that can mitigate the hazard, and any other relevant models for the use case. Data will be facilitated by relevant stakeholders for the scenario and will include Points of Interest, Assets (e.g. equipment or infrastructures), Pre-identified Risks, and hazards or Failures detected, among others. An initial set of data will be supplied and published in the REACH repository.

• Asset
• Hazard
• Measure

These datasets will be according to SmartDataModels (https://smartdatamodels.org/index.php/10-risk-management-data-models-published/), namely:

• https://github.com/smart-data-models/dataModel.PointOfInterest

• https://github.com/smart-data-models/dataModel.RiskManagement/blob/master/Risk/README.md

• https://github.com/smart-data-models/dataModel.RiskManagement/blob/master/Hazard/README.md

• https://github.com/smart-data-models/dataModel.RiskManagement/blob/master/Asset/README.md

Other relevant data can be found in publicly available Open Source Datasets or in existing REACH DVC Themes. (Energy, Health, and others). This can be further detailed during the implementation phase but data related to weather conditions, for example, may be relevant and can be obtained from Open Data Sources, which can be chosen by participants in cooperation with the DIH.

Expected outcomes:

Creating a mesh network for communication and interoperability of services in a critical environment, providing support for real-time decisions at the edge