In this section we describe the techniques for automated UAV-based inspection with edge nodes relying on the FL method (section Background). Initially, we focus on describing the MEC platform running on the edge nodes and afterwards we illustrate the automated interaction for the UAV-based inspection using the FL method.
Mobile edge platform overview
UAVs have resource constraints at processing and storage level. Nevertheless, often the data that are gathered from the sensors require processing, before an actual verdict is reached that will lead to autonomous actuation actions. Additionally, storing the data locally at the device level may lead into overflow in memory or storage resources. A common solution to these issues that was followed till recently was the presence of a Cloud environment deployed in a virtualized or physical server of the operation center (Lekidis et al. 2022). However, industrial applications are characterized by real-time and critical operation that requires low latency, which cannot be provided when communicating with Cloud platforms. Hence, a gradual shift is currently observed towards edge platforms, in order to provide a computational and storage layer to this architecture.
The MEC initiative (ETSI: GR MEC 017 2018) allows to extend the network slices by edge resources and services, such as MEC platform, and applications, the UPF, the RAN or even Cloud-native compute, network or storage functions. MEC ensures network scalability by distributing the processing from the centralized architecture of the Cloud platform to the edge that is located closer to the user. This allows faster response to user requests, since computations, data aggregation and analytics are handled within user proximity. A scheme that is currently followed is the presence of a dedicated management entity on the edge for the resource lifecycle management, which includes instantiation, decommissioning and other functionalities. Such entity is called Mobile Edge Platform (MEP) and provides distributed processing and storage capabilities that reduce the network management complexity.
The MEP architecture is illustrated in Fig. 3 and it is deployed in each edge node. The architecture follows the standardized interfaces and components that are defined by the ETSI MEC Industry Specification Group (ISG) (ETSI: GR MEC 017 2018). Additionally, it also includes a Virtualization Infrastructure Manager (VIM) (ETSI: GR MEC 017 2018), which interacts with the NFV MANO to receive instructions for the configuration of the VNFs and virtual links in each edge node. It allows to extend the 5G network slice for providing latency and performance improvements in UAV-based inspection as well as collision avoidance capabilities in UAV missions.
Initially, a resource interface component is used for data exchange with the UAVs. The interface is based on an extension of the Linux Foundation Fledge framework,Footnote 1 which is also offered as VNF using the virtualization environment offered by Linux EVE.Footnote 2 This environment offers isolation for the execution of applications in the mobile edge. The containers are managed by a lightweight version of Kubernetes, namely K3S,Footnote 3 that is used both as a Mobile Edge Orchestrator (MEO) and as a VIM. Moreover, the entire processing and data exchange services are running on the MEP platform, which also contains the UPF. Additionally, the MEP platform also provides accurate geolocation and trajectory data for the UAVs, using constant communication with GPS satellites.
The communication with UAV-based protocols is facilitated through the Fledge resource interface that is included in each MEP (Fig. 3). Finally, every MEP is programmed to regulate the data exchange frequency, in order to maintain minimal edge resource utilization that leads to an extended battery lifetime for autonomous operation.
Federated learning incorporated in infrastructure inspection
The FL method provides a high-level of automation since each MEP is able to interact with the NFV MANO to provide autonomous operation for the system, as depicted in Fig. 4. The MANO that is employed the Open-Source NFV MANO (OSM)Footnote 4 for the orchestration of 5G network slices. Moreover, FL ensures privacy, since the data remain at the edge level and are not stored in cloud platforms. Moreover, a potential failure to the Cloud environment leads to a loss of data, processing and management capabilities and hence a degradation of UE services and applications. A cause of failure is a potential overload or even a targeted cyber-attack. With a decentralized architecture a failure in the Cloud environment of the operation center can avoid such degradation as UE’s services and applications may be served by the nearest edge entity. Through the integration of 5G networks and MEC the FL models are updated and re-trained seamlessly with local data and footage from the infrastructure that is obtained from the UAVs.
Each MEP is also able to interact with the NFV MANO to provide autonomous operation for the system, as depicted in Fig. 4.
The interaction is enabled by the LSTM models that are using the FL method, in order to be trained and executed on the edge level. The reasoning behind the choice of FL lies in the presence of multiple edge Points-of-Presence (PoP) in different distributed locations, each one including a MEP platform. The MEP platforms use FL to train locally the LSTM models and receive the parameters and configurations from the Cloud environment where the NFV MANO is deployed, in order to perform infrastructure inspection closer to the UAV’s using their local data. Furthermore, the FL method allows to improve the efficiency and provide a high-level of network automation for the UAV-based infrastructure inspection method. This is accomplished by performing data processing and caching in each edge PoP.
Moreover, the FL method provides automation in the formation and management of 5G network slices. In this case, the MEP receives configuration instructions from the NFV MANO for network slice instantiation or extension on the edge level. Then, supervised training techniques are used to translate high-level intents from NFV MANO into concrete instructions on how to deploy and instantiate FL LSTM models in each edge PoP. Overall, the procedure that is followed is divided into three individual steps.
Initially intent-based policies are specified, in order to receive the parameters and configurations based on which the LSTM models that will be deployed and executed in each edge PoP for infrastructure asset identification and fault detection using the UAV video data. The use of intents allows to hide complexity, technology- and vendor-specific details. Intents are described in natural language and are translated into configurations through Natural Language Processing algorithms (Chowdhary 2020). Specifically, these algorithms are trained to receive input in form of textual description of the desired service characteristics and then produce a domain specific encoding corresponding to the original intent. This process follows a step sequence: (1) the intents are pre-processed, (2) keywords are extracted and translated into meaningful actions and then (3) aggregated and validated for lack of conflicts.
As a second step, appropriate Application Programming Interfaces (APIs) on the MEP are used to receive the intents and the LSTM model configuration and parameters that are used for the inspection of the specific infrastructure (Fig. 4). To this end, the ETSI MEC ISG has provided an initial set of API’sFootnote 5 to facilitate this interaction. The APIs are registered and discovered over the Mp1 reference point defined in ETSI MEC architecture (ETSI: GR MEC 017 2018). Then, the associated LSTM models and VNFs are instantiated and connected using virtual links based on the intent translation of the previous step.the intents are translated into edge configurations for deploying and instantiating the LSTM models.
The third step concerns the training of the LSTM models. This is accomplished by each MEP using local data from the UAVs and deployed models based on the intents that originate from the NFV MANO. Then, the LSTM models are executed to identify electricity infrastructures and respective assets. During the inspection and if the envisaged inspection accuracy is not achieved, the FL update service synchronizes with the MEC FL API (Fig. 4) to re-calibrate the LSTM models with different parameters and configurations that will provide accuracy improvements.