Fixed Figures numbers and captions added

By integrating oneM2M’s standardized data management with MEC's edge computing capabilities, this use case shows real-time, low-latency processing, while also enabling the migration of applications across MEC zones without compromising service quality. As the AV moves, the system adjusts dynamically, ensuring continuous service delivery through the seamless orchestration between the cloud, MEC, and edge devices. This use case not only supports static services but also mobility services, ensuring that the AV receives timely updates and services as it traverses multiple zones.

![Figure 5.2.1-1: Autonomous Vehicle Edge Computing service continuity.](/media/usecase_autonomouse_vehicles.png)

![Figure 5.1.1-1: Autonomous Vehicle Edge Computing service continuity.](/media/usecase_autonomouse_vehicles.png)

**Figure 5.1.1-1: Autonomous Vehicle Edge Computing service continuity.**

### 5.1.2	Vulnerable Road Users

The integration of oneM2M’s standardized IoT data management with MEC’s edge computing capabilities creates a responsive and location-aware safety system. For instance, when an HV prepares to make a left turn, the MN-CSE on the nearest MEC node evaluates real-time VRU positions and sends instant collision-risk alerts directly to the vehicle. The MEC platform further enhances this process by providing network-aware optimizations, such as selecting the best available connection (5G, LTE-V2X) for data exchange. As the HV moves across different zones, the system seamlessly transitions tasks between edge nodes or back to the cloud, ensuring uninterrupted service. By combining cloud scalability with edge-level agility, this approach not only improves road safety but also demonstrates how IoT and edge computing can converge to support latency-critical automotive applications. 

An offloading concept locating tasks and resources to a place where close to users can be applied to this VRU detection service. In this case, a service can be provided to users with very short delay.

![Figure 5.2.2-1: Scenario of resource and tasks offloading to support VRU application.](/media/usecase_vru.png)

![Figure 5.1.2-1: Scenario of resource and tasks offloading to support VRU application.](/media/usecase_vru.png)

**Figure 5.1.2-1: Scenario of resource and tasks offloading to support VRU application.**

## 5.2	Industrial & Robotics

Together, the integration of Swarm Computing, edge-based IoT platforms, and MEC infrastructure demonstrates a robust solution for complex delivery optimization challenges. The system’s ability to self-organize, adapt to obstacles, and continuously refine its operations highlights the transformative potential of combining decentralized intelligence with real-time edge computing.

![Figure 5.3.1-1: Data flow in a hybrid swarm system where robots share pheromone maps and report routes and obstacles to the MN-CSE, which uses MEC-provided real-time data to generate virtual pheromones and optimize swarm behavior.](/media/usecase_swarm_robots.png)

![Figure 5.2.1-1: Data flow in a hybrid swarm system where robots share pheromone maps and report routes and obstacles to the MN-CSE, which uses MEC-provided real-time data to generate virtual pheromones and optimize swarm behavior.](/media/usecase_swarm_robots.png)

**Figure 5.2.1-1: Data flow in a hybrid swarm system where robots share pheromone maps and report routes and obstacles to the MN-CSE, which uses MEC-provided real-time data to generate virtual pheromones and optimize swarm behavior.**

### 5.2.2	Smart Warehouse Automation

As warehouse operations span large areas, AGVs may move between MEC zones. The MEC platform supports service continuity by transferring control logic and task context across MEC nodes, ensuring uninterrupted automation and safety.

![Figure 5.3.2-1: High-level overview of the warehouse automation scenario.](/media/usecase_smart_warehouse.png)

![Figure 5.2.2-1: High-level overview of the warehouse automation scenario.](/media/usecase_smart_warehouse.png)

**Figure 5.2.2-1: High-level overview of the warehouse automation scenario.**

### 5.2.3	Industrial Digital Twins

This use case illustrates the integration of the oneM2M IoT platform with the ETSI MEC edge computing framework to support Industrial Digital Twins (IDTs) in smart manufacturing environments. The goal is to enable continuous monitoring, analysis, and optimization of industrial processes by deploying synchronized digital representations of physical assets across both cloud and edge infrastructures. This approach is fundamental to enabling real-time decision-making, predictive maintenance, and autonomous control in dynamic and distributed industrial settings. 

As mobile industrial assets like AGVs or modular production units relocate within or between factory environments, the system ensures that all associated digital twin data and computational contexts are seamlessly migrated to a new MN-CSE at the appropriate MEC node. This handover is coordinated by the IN-CSE and utilizes MEC’s Application Mobility and Platform Services to maintain uninterrupted functionality. Throughout this process, the oneM2M system keeps cloud and edge representations synchronized, enabling both high-level analytics—such as key performance indicators and production benchmarking—and immediate operational control. The benefits of this integrated approach are significant. Sub-second response times are achieved by executing control loops directly at the edge, reducing latency to a minimum. The architecture supports uninterrupted service delivery even during the movement of assets between operational zones, enhancing system reliability. Its modular and scalable nature allows deployment across a wide range of industrial environments. Finally, adherence to oneM2M standards and MEC orchestration capabilities guarantees compatibility and future-proof integration across heterogeneous platforms.

![Figure 5.3.3-1: Industrial Digital Twins supported and enabled for both IoT/IIoT interactions and edge-cloud computation by the integrated capabilities of oneM2M and ETSI MEC.](/media/usecase_digital_twins.png)

![Figure 5.2.3-1: Industrial Digital Twins supported and enabled for both IoT/IIoT interactions and edge-cloud computation by the integrated capabilities of oneM2M and ETSI MEC.](/media/usecase_digital_twins.png)

**Figure 5.2.3-1: Industrial Digital Twins supported and enabled for both IoT/IIoT interactions and edge-cloud computation by the integrated capabilities of oneM2M and ETSI MEC.**

## 5.3	Maritime 

As the vessel transits through different MASS zones, the proposed oneM2M/MEC architecture allows to seamlessly move tasks between edge nodes, ensuring service continuity and supporting mission-critical services for the unmanned vessels (e.g., assisted maneuvering, collision avoidance and situational awareness).

![Figure 5.4.1-1: Scenario for assisted manoeuvring exploiting oneM2M and ETSI MEC.](/media/usecase_ship_maritime.png)

![Figure 5.3.1-1: Scenario for assisted manoeuvring exploiting oneM2M and ETSI MEC.](/media/usecase_ship_maritime.png)

**Figure 5.3.1-1: Scenario for assisted manoeuvring exploiting oneM2M and ETSI MEC.**

## 5.4	Metaverse 

If needed, IoT actuators in the real store—such as digital signage or voice assistants—can respond with contextual actions (e.g., displaying pickup instructions). The entire experience feels seamless to the user: every virtual interaction is grounded in live, physical-world data, while AI-driven recommendations create a personalized, context-aware shopping journey. The user benefits from smart, immersive shopping, while the system ensures real-time linkage between the digital and physical layers, powered by oneM2M and MEC.

![Figure 5.5.1-1: Smart Shopping with Edge-AI and Cloud IoT Integration.](/media/usecase_smart_shopping.png)

![Figure 5.4.1-1: Smart Shopping with Edge-AI and Cloud IoT Integration.](/media/usecase_smart_shopping.png)

**Figure 5.4.1-1: Smart Shopping with Edge-AI and Cloud IoT Integration.**

## 5.5	Future Home

In this extension, the non-MEC node is realized as a Customer Premises Edge device  (have support of both computational as well as Multi-access network communication capabilities) deployed locally at the user's home (e.g., home gateway or dedicated edge server). This edge device hosts MEC applications or MN-CSE components and serves as the key enabler for local, low-latency processing of Smart Home functions, reducing dependency on centralized cloud services.

![Figure 5.6.1-1: Future Home user premises scenario with integrated Edge and IoT functionalities.](/media/use_case_future_home.png)

![Figure 5.5.1-1: Future Home user premises scenario with integrated Edge and IoT functionalities.](/media/use_case_future_home.png)

**Figure 5.5.1-1: Future Home user premises scenario with integrated Edge and IoT functionalities.**

# 6	MEC-oneM2M Architectural & Use Case Mapping