Public Repository of Experiments

One of the most important key advances of 5G is the virtualization of computing and network functions. These advances have enabled the possibility to implement Multi-access Edge Computing (MEC) capabilities that aim to offload tasks performed by mobile devices and ensure low latencies due to the proximity of the computing facilities to the points of attachment.

In order to integrate all the required virtual resources for each OBU, the novel idea of instantiating virtual substitutes for these OBUs (vOBUs) has been proved to be beneficial in terms of device’s access delay, reliability against wireless disconnections or data cache, as the SURROGATES 5GinFIRE open call project has showed. OdinS introduces this solution that provides the necessary vOBUs that are instantiated at the edge of the access network with the purpose of offloading high computational cost tasks to the network following the MEC approach.

RoadSide Units (RSU) are devices installed on the side of the road to detect hazardous situations and inform vehicles. They can be stand-alone equipment or integrated in traffic lights, toll gates, variable message signboards (VMS), etc. They can be connected to traffic control centres by many means, including cellular, LoRA, and optic fibber. They could also integrate localized communications to communicate with passing by vehicles, using for instance ITS-G5 as in the pilot deployment of C-ITS services. RSUs deployed for C-ITS services are referred to as roadside ITS station (R-ITSS), as these services are developed in conformance with the ITS station architecture [ISO-21217] [ETSI-EN-302-665].

Depending on vehicle density, decision choices of each region, cellular coverage and particularly on the use cases, it is in some situations required to install physical units at specific locations of the road (e.g. black ice detectors, contextual speed limits, traffic lights providing time and phase information, etc.). However, such deployments cannot be envisioned on all roads. Moreover, not all services would be feasible to be deploys as this would be too expensive, particularly in areas with scarce density of vehicles and already covered by cellular networks. In such situations, virtual roadside ITS stations deployed in the cloud would allow the collection and transmission of data alike physical ITS stations along the road.

This is actually what has been implemented by YoGoKo in Nordic Way, the Scandinavian pilot deployment of C-ITS services. Virtual roadside ITS stations allow the transmission of C-ITS services to vehicle using LTE-based networked communications as much as it would be transmitted by roadside ITS stations to vehicles using ITS-G5 localized communications.

Further extending this idea, 5GASP will develop a virtual RSU Network Application acting as a roadside ITS station implementing C-ITS services such as CAM, DENM, SPaT, MAP, amongst others.

C-ITS standards are based on the ITS station architecture. This layered architecture (OSI-type) comprises the ITS station access technologies layer, the ITS station networking & transport layer, the ITS station facilities layer, the ITS station application entity, the ITS station management entity and the ITS station security entity. Each layer comprises a diversity of functionalities that can be chosen from according to the environment of exploitation.

For instance, an ITS station deployed in a vehicle that only provides V2X localized communication would implement ITS-G5, GeoNetworking, BTP, CAM, DENM, SPaT/MAP, LDM; while an ITS station deployed in a vehicle to provide connectivity to the cloud would implement 5G, IPv6 and services similar to CAM, DENM, SPaT/MAP, LDM.

This Network Application by YoGoKo will provide at the minimum facilities layer services independently of the communication protocols so that it would allow users to develop new applications and services for the Automotive and PPDR verticals and ensure that the developments are compatible with C-ITS standards.

Solutions that provide virtual counterparts in a MEC are suitable for vehicular scenarios. The main problem that arises is the high mobility of vehicles and how to maintain the connectivity of their OBUs with their correspondent virtual surrogates when switching between different network domains. This is the case of cross-border areas where vehicles change between operators and domains.

The MIGRATE experimental deployment solved this issue betting on the dynamic instantiation of new virtual counterparts on demand in the new domain using the same configuration parameters of the former virtual surrogate, transferring state to the new instantiation and finally updating data paths using Software-Defined Networking (SDN) functions.

Odins’ Network Application will present a supporting solution that provides interdomain mobility capabilities to the vOBUs introduced before, and in a more generic view, to any Network Application that requires such capabilities. This Network Application shall enable the vOBUs to be migrated to the closest MEC to the real vehicle, meeting the low latencies requirements of vehicular applications. The migration procedure will ensure that the OBU will maintain connectivity with its virtual counterpart in the former domain while the new virtual instantiation is getting ready with the same configuration and state. Once it is ready, the data paths are updated to start using the new one without any packet loss.

Autonomous vehicles (especially those involved in PPDR) are usually equipped with multiple 4K cameras and sensors (e.g. LiDAR and RADAR) that generate tens of Megabit of data per second. Moreover, as autonomous vehicle technologies continue to evolve, the data generated inside cars continues to grow exponentially. To enable the vehicle to be highly operational, there are remote/cloud services such as Teleoperation, remote driving and vehicle remote assistance that use the generated data to both estimate the status of the vehicle and build an image of its surrounding. Such services usually send back the vehicle control commands and instructions to be executed instantaneously. Naturally, the success of these services depends on the communication latency and the reliability of network messages.

The V2C communication Network Application enables the vehicle to send and receive data in real-time. The Network Application is application-aware to ensure optimized data transmission based on the content being sent (e.g. real-time video or voice have higher priority than telemetry data). Since the transmission of the HD video generated by the cameras must be in realtime (i.e. buffering and retransmission mechanisms cannot be used) the Network Application uses techniques such as Forward-Error-Correction (FEC), channel bonding and dynamic encoding bitrate to assure fast video delivery while maintaining maximum video quality. In addition, when multiple channels exist, the Network Application will prioritize the data delivered to the vehicle based on the performance of the channels.

There is an industry consensus today that autonomous vehicles will need help in making decisions, especially in unusual, dangerous situations that can happen on the road and may require violating the traffic laws (e.g. crossing double yellow lines). For these cases, the industry has started to adopt teleoperation/remote driving solutions that enable a remote human operator to monitor and take control over the car if needed.

DriveU and BLB will work together within the 5GASP project in order to develop a Network Application that enables a remote operator to take full/partial control over an autonomous vehicle in unusual/dangerous situations that can happen in emergency situations (PPDR). Ensuring safe Teleoperation and human remote assistance entails reliable transmission of high-quality real-time video with minimum latency. As such we will equip a virtual vehicle instance with one or more 5G modems and a SW module that enables the transmission of HD video to the teleoperation centre.

UNIVBRIS will develop Network Application(s) in support of efficient MEC handover between MEC points of presence (POPs) in the context of the Automotive Use Case. This may refer to, e.g., strict latency, reliability, network and PoP resource availability restrictions.

Thus, MEC handovers may not be necessarily coupled to wireless handovers and be triggered by, e.g., edge service performance predictions. To this end, analysis and identification of suitable MEC architectures at the UNIVBRIS testbed site may involve hybrid solutions: e.g., by adopting distributed POPs collocated with RAN access points or/and including more centralised POP schemes that cover a wider area in order to avoid user handovers and proactive actions.

The goal of UNIVBRIS Network Application is to explore the suitability of Machine Learning (ML) in order to facilitate strict requirements, as stated above, after handovers.

PrivacyAnalyzer is a software tool implemented by Modio catering for the identification of faulty or malicious behaviour either due to software bugs that lead to excessive Personal Identifiable Information (PII) leaks, or due to privacy leaks that might derive from the fact that a device has been hacked. In these scenarios, PII may be disseminated at any stage.

The Value Proposition of PrivacyAnalyzer is formed around its capability to alert end-users as well as service providers in situations where PII leaks are detected. In turn, this helps them to undertake the relevant decisions (e.g. a user stopping use of certain devices until they are replaced by privacy-aware alternatives/upgraded devices or a service provider to fix the software problems which cause the privacy leaks). A snapshot of a PrivacyAnalyzer web User Interface (UI) that summarizes detected privacy vulnerabilities is depicted in the radar chart at https://modio.io/Privacy/.

PrivacyAnalyser offers the following features:

1. automatic detection and classification (via selected machine learning techniques) of confidential data from streaming data collected from IoT devices
2. evaluation of the level of privacy protection offered by encryption, de-identification, and anonymization
3. option for users to express their preferences, regarding information that is deemed as confidential, through privacy policies
4. visualization of the outcomes of the privacy tests using intuitive and informative web User Interfaces (UIs)

Within 5GASP, Modio’s shall adapt the architecture of PrivacyAnalyzer in the context of the 5GASP architecture, so that its components are implemented as a chain of Virtual Network Functions (VNFs), in other words, as a Network Application deployed within the 5GASP platform.

PrivacyAnalyzer was chosen given its potential to become an important service in both 5GASP’s use cases as well as a service that could serve other verticals apart from Automotive and PPDR. For the latter verticals, the PrivacyAnalyzer Network Application shall cater for detecting privacy vulnerability of relevant network devices, which are involved in automotive and/or PPDR communications. As an example, our service might enhance the physical protection of the personnel of the involved authorities (e.g. First Responders) in PPDR operations and overall help the success of ongoing disaster recovery operations.

ININ will develop a Network Application targeted for ensuring a continued ability of Public Safety users to communicate also within the most demanding mission critical situations. 5G Isolated Operation for Public Safety Network Application (5G IOPS Network Application) aims at maintaining a level of communication between public safety users, offering them local mission critical services even when the backhaul connectivity to the core network is not fully functional or is disrupted. This operation mode is typically needed in PPDR disaster situations, when the infrastructure is damaged or destroyed, and in the out of coverage emergency cases operated in the rural areas.

5G IOPS Network Application will explore novel connectivity modes between UE, RAN and core network elements, functions of MANO orchestration and cloud-native network function approach to assure automated deployment and self-healing capabilities of the Network Application as proposed in 3GPP Release 15 specifications for the Public Safety services. This Network Application will also be used to showcase international cross-border PPDR operation in multidomain 5G environments.

The Neo.Bus Network Application developed by Neobility will be a 5G cloud native application that uses the 5G infrastructure to calculate optimal shared routes on the fly. Using predictions (duration of stay and next location/travel prediction), the shared pool of potential passenger can be increased by a huge number. The algorithms will predict the probability, time, and destination of the next travel, and use this data to contact potential future travellers ahead of their travel-time and convince them to join the route.

5G networks with their increased capacity, security, elasticity, and adaptability will be a key asset for Neo.Bus. Using the 5G infrastructure MEC, Neo.Bus will deliver better services to its customers by building a real-time distributed vehicle route problem (VRP) optimizer engine that runs on MEC servers closer to users and buses, thus proving near instantaneous and 100% incremental processing. A real time planning engine takes a constant feed of data (GPS from users and buses) and the schedule must constantly adapt to stay efficient.

Using a real-time distributed solution, that runs closer to the user in edge computing, will recalculate the route continuously (continuous re-optimisation) and providing a near instantaneous response to the user’s request.

Wildfires are one of the costliest and deadliest natural disasters across the world, especially in the Mediterranean region. The immediate impacts include damage to millions of hectares of forest resources, evacuation of thousands of people, burning of homes and devastation of infrastructure, and most importantly, threatening the lives of people.

This Network Application will give to teams a first assessment of buildings and forests on fire. The Network Application is onboarded to an air drone as a cloud native application together with services on the Edge ensuring low latency. Telemetry as well as information from infrared sensors, speakers, conventional video and thermal vision are transmitted to the 5G System and from there to the teams on the ground. The Network Application not only handles the drone live images streaming and processing, but also allows the teams on ground to adjust the altitude at which flyover takes place according to the height of the flames. Apart from the service creation time, it shall also be critical the handover between 5G stations.