-[6.3 Use Cases \& Frameworks Mapping](#63use-cases--frameworks-mapping)
-[6.3.x](#63x)
-[6.3.x.1 Use Case Driving Deployment](#63x1-use-case-driving-deployment)
-[Table 6.3.x.1-1 – Operational Requirements and Platform Support for Autonomous Vehicle with Continuous Edge Computing](#table-63x1-1--operational-requirements-and-platform-support-for-autonomous-vehicle-with-continuous-edge-computing)
-[6.3.x.1 Use case Driven Deployment](#63x1use-case-driven-deployment)
-[Table 6.3.x.1-1 – Operational Requirements and Platform Support for Smart Warehouse Automation](#table-63x1-1--operational-requirements-and-platform-support-for-smart-warehouse-automation)
-[7 New Internetworking Proposed Recommendations Based on Use Cases](#7new-internetworking-proposed-recommendations-based-on-use-cases)
-[7.1 XXX](#71xxx)
-[Annex A: Title of annex](#annex-a-title-of-annex)
-[Annex B: Title of annex](#annex-b-title-of-annex)
-[B.1 First clause of the annex](#b1-first-clause-of-the-annex)
@@ -170,6 +174,7 @@ All rights reserved.<br />
-[Annex: Bibliography](#annex-bibliography)
-[Annex : Change history](#annex--change-history)
-[History](#history)
-[Document history](#document-history)
<br/>
@@ -265,8 +270,23 @@ Clause 7 will define the requirements to support the architectures and use cases
## 5.1 Introduction
This clause presents a structured analysis of selected use cases that demonstrate the application of MEC and oneM2M frameworks in Edge and IoT-based scenarios. Use cases are grouped by domain—such as Mobility, Industrial, and Maritime—to reflect their contextual relevance. Within each domain, individual use cases are examined in detail, covering their context, involved stakeholders, technical requirements, challenges, and the specific roles played by MEC and oneM2M technologies in addressing them.
The selected and identified use cases and scenarios presented in this document have been obtained through a structured exploration process involving key stakeholders. This process was facilitated via a dedicated form designed to systematically capture the description of each use case, the involved actors, technical and operational requirements, as well as preconditions and postconditions. Furthermore, the form guided stakeholders in defining the key triggers, expected interaction flows, and dependencies necessary for successful implementation. By leveraging this structured approach, the document ensures a comprehensive and consistent representation of use cases, aligning them with the MEC and oneM2M frameworks to maximize interoperability and practical applicability.
This clause introduces the application domains and use cases explored in the context of the ESTIMED project, focusing on how the integration of the ETSI MEC and oneM2M frameworks enables next-generation Edge-IoT solutions. The clause builds upon a use case-driven methodology that emphasizes real-world scenarios as the foundation for architectural mapping, technical analysis, and standardization recommendations.
The goal of this clause is to present selected domains where edge computing and IoT convergence are driving tangible benefits across verticals such as mobility, industry, maritime operations, digital experiences, and smart living. For each domain, representative use cases have been identified to demonstrate the practical value of combining MEC's edge capabilities with oneM2M’s standardized IoT platform. These use cases illustrate how the integration supports low-latency data processing, scalable service delivery, and intelligent, context-aware applications.
The table below summarizes the domains and the associated use cases covered in this clause:
| Maritime | - Assisted Manoeuvring for Connected Ships | Remote vessel monitoring, mission-critical edge processing, seamless edge-cloud communication |
| Metaverse | - Smart Virtual Shopping Service | IoT-enhanced virtual environments, edge-hosted AI analytics, immersive low-latency digital experiences |
| Future Home | - Advanced Smart Home Services | Real-time media, education, health, and automation services within personalized, responsive smart environments |
Each use case is examined in detail within its domain context, describing how MEC and oneM2M contribute to solving specific challenges, enabling innovation, and supporting interoperability. The domains and use cases form the analytical foundation for the architectural mappings and technical recommendations that follow in subsequent clauses.
## 5.2 Smart City & Mobility
This clause focuses on the Smart City and Mobility domain, highlighting two key use cases: Autonomous Vehicles and Edge Continuum, and Vulnerable Road User Detection. It examines how MEC and oneM2M frameworks support advanced urban mobility solutions by enabling real-time data processing, vehicle-to-infrastructure communication, and coordinated edge intelligence. These capabilities contribute to improved traffic management, enhanced road safety, and more efficient transportation systems within smart urban environments.
@@ -306,13 +326,13 @@ However, when applied to real-world systems like autonomous delivery or energy m
In this scenario, the edge IoT platform (e.g., MN in oneM2M), which acts as an edge node in a decentralized system, plays a critical role in overcoming these limitations. While robot (agents) in the swarm communicates and collaborates autonomously, the edge IoT platform provides indirect feedback by offering information that robots may not directly perceive, such as road conditions, elevator status, and indoor obstacles. This feedback mechanism enhances the swarm’s performance and helps in optimizing the delivery task. Specifically, the edge computing platform (e.g., MEC) allows for ultra-low latency processing and ensures that robots can receive real-time updates on the environment. By integrating the edge IoT platform for efficient resource management and the edge computing platform for real-time environmental feedback, this hybrid swarm system becomes more capable of optimizing delivery paths and avoiding congestion, while still maintaining decentralized autonomy.
Over time, the swarm self-organizes into an efficient delivery network with real-time obstacle avoidance and path adaptation, demonstrating how Swarm Computing, when combined with IoT platform running on Edge infrastructure, can solve complex problems in dynamic environments.
As the swarm operates, it gradually evolves into a highly efficient delivery network, capable of real-time obstacle avoidance and dynamic path adaptation. This process exemplifies the power of Swarm Computing when integrated with an IoT platform deployed on Edge infrastructure. In this context, Swarm Computing refers to a decentralized approach in which multiple autonomous agents—represented as Application Entities (AEs) within the oneM2M framework—interact and collaborate using locally available information. These agents are able to solve tasks independently, without relying on centralized control, resulting in emergent collective intelligence.
The IoT platform, implemented as an edge node (such as a Middle Node, MN, in oneM2M), plays a pivotal role by providing indirect feedback to the swarm. It supplies real-time environmental data that individual agents may not directly perceive, thereby enhancing the swarm’s overall performance and adaptability. This feedback mechanism enables agents to make informed decisions, optimize delivery routes, and respond effectively to changing conditions.
Explanation of Key Concepts:
Complementing the IoT platform, the Edge infrastructure—exemplified by the MEC platform—processes data locally to deliver ultra-low-latency feedback. By hosting both IoT services and computational resources at the edge, the MEC platform ensures that swarm agents receive timely updates and actionable insights. This local processing capability is crucial for improving coordination among agents, optimizing task execution, and maintaining operational efficiency in dynamic environments.
- Swarm Computing: A decentralized model where multiple agents (AEs in oneM2M) interact and collaborate based on local information, solving tasks autonomously without needing a central authority.
- IoT platform running on Edge infrastructure (e.g., MN in oneM2M): An edge IoT node that provides indirect feedback to enhance the swarm’s performance by offering real-time environmental data.
- Edge infrastructure (e.g., MEC platform): A platform that processes data locally to provide low-latency feedback, improving swarm coordination and task optimization.
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.
@@ -327,8 +347,7 @@ As warehouse operations span large areas, AGVs may move between MEC zones. The M
### 5.3.3 Industrial Digital Twins
T
his 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.
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.
The overall system architecture relies on a cloud-based oneM2M IN-CSE (Infrastructure Node Common Service Entity), which functions as a centralized orchestrator and digital repository, and multiple MN-CSEs (Middle Node Common Service Entities) deployed at the edge on MEC nodes physically co-located with manufacturing equipment or microfactories. These edge-based MN-CSEs are responsible for processing time-sensitive data streams such as sensor readings from production lines, robotic cell statuses, energy usage metrics, and environmental conditions with ultra-low latency.
@@ -358,7 +377,7 @@ As the vessel transits through different MASS zones, the proposed oneM2M/MEC arc
## 5.5 Metaverse
The Metaverse is emerging as a dynamic space for immersive, interactive experiences that seamlessly bridge the digital and physical worlds. In this context, virtual environments are increasingly enhanced by real-time data from physical spaces and intelligent processing at the edge. This clause introduces a use case that integrates the oneM2M IoT platform with the ETSI MEC edge computing framework to enable a smart virtual shopping service. By combining live data from IoT-enabled physical stores with edge-hosted AI analytics, the system delivers personalized, low-latency interactions within the metaverse, ensuring a synchronized, context-aware user experience that connects virtual behaviour with real-world actions.
The metaverse refers to a persistent, shared, and immersive digital environment where users interact with each other, digital objects, and services through avatars or virtual representations. It encompasses a range of technologies—including Virtual Reality (VR), Augmented Reality (AR), 3D web platforms, and real-time data integration—that collectively enable seamless blending of digital and physical experiences. In the context of IoT and edge computing, the metaverse can extend traditional retail, education, entertainment, and social interaction by connecting virtual spaces with live data and intelligent services from the real world. For example, a virtual shopping mall in the metaverse can mirror the inventory and layout of a physical store, allowing users to browse, interact, and purchase items as if they were present on-site. Similarly, virtual classrooms can leverage real-time sensor data and edge analytics to create adaptive, engaging learning environments. This clause introduces a use case that integrates the oneM2M IoT platform with the ETSI MEC edge computing framework to enable a smart virtual shopping service. By combining live data from IoT-enabled physical stores with edge-hosted AI analytics, the system delivers personalized, low-latency interactions within the metaverse, ensuring a synchronized, context-aware user experience that connects virtual behaviour with real-world actions.
### 5.5.1 Smart Shopping with Edge-AI and Cloud IoT Integration
