-[5.2.3 Industrial Digital Twins](#523industrial-digital-twins)
-[5.3 Maritime](#53maritime)
-[5.3.0 Description](#530description)
-[5.3.1 Assisted Manoeuvring for Autonomous Ship](#531-assisted-manoeuvring-for-autonomous-ship)
-[5.4 Metaverse](#54metaverse)
-[5.4.0 Description](#540description)
-[5.4.1 Smart Shopping with Edge-AI and Cloud IoT Integration](#541smart-shopping-with-edge-ai-and-cloud-iot-integration)
-[5.5 Future Home](#55future-home)
-[5.5.0 Description](#550description)
-[5.6.1 User Premises Edge and oneM2M Integration](#561user-premises-edge-and-onem2m-integration)
-[6 MEC-oneM2M Architectural \& Use Case Mapping](#6mec-onem2m-architectural--use-case-mapping)
-[6.0 Introduction](#60introduction)
@@ -268,7 +273,7 @@ Clause 7 will define the requirements to support the architectures and use cases
# 5 Edge & IoT Domains and Use Cases
## 5.1 Introduction
## 5.0 Introduction
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.
@@ -288,10 +293,13 @@ The table below summarizes the domains and the associated use cases covered in t
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
## 5.1 Smart City & Mobility
### 5.1.0 Description
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.
### 5.2.1 Autonomous Vehicle with Continuous Edge Computing
### 5.1.1 Autonomous Vehicle with Continuous Edge Computing
This use case demonstrates the integration of oneM2M's IoT platform with ETSI MEC's edge computing framework to enable continuous real-time processing for autonomous vehicles (AVs). The core system consists of a cloud-based oneM2M IN-CSE (centralized IoT hub) for managing vehicle and environmental data, and MN-CSE instances deployed on MEC nodes that process time-sensitive data such as vehicle sensor inputs, road conditions, and traffic signals with ultra-low latency.
@@ -303,7 +311,7 @@ By integrating oneM2M’s standardized data management with MEC's edge computing
### 5.2.2 Vulnerable Road Users
### 5.1.2 Vulnerable Road Users
This scenario illustrates a collaborative architecture between oneM2M’s IoT platform and ETSI MEC’s edge computing framework to enable real-time Vulnerable Road User (VRU) detection for connected vehicles. At the core of the system, a cloud-based oneM2M IN-CSE serves as the central IoT hub, storing and managing VRU-related data such as pedestrian locations, cyclist trajectories, and road conditions.
@@ -314,11 +322,13 @@ An offloading concept locating tasks and resources to a place where close to use
## 5.3 Industrial & Robotics
## 5.2 Industrial & Robotics
### 5.2.0 Description
This clause covers the industrial and robotics domain with a focus on use cases such as Swarm-based Autonomous Ant Delivery Optimization, Smart Warehouse Automation, and Industrial Digital Twins. It explores how MEC and oneM2M frameworks enable intelligent coordination, real-time control, and seamless IoT integration in complex industrial and logistics environments. By leveraging edge capabilities, these use cases benefit from low-latency processing, enhanced automation, and improved operational efficiency across urban and industrial ecosystems.
### 5.3.1 Swarm-based Autonomous Ant Delivery Optimization
### 5.2.1 Swarm-based Autonomous Ant Delivery Optimization
This scenario explores a hybrid swarm-based autonomous delivery system inspired by ant colony behavior. Swarm computing is a computational model based on the collective behavior of decentralized systems, often inspired by nature (e.g., ants, bees, or fish schools). In traditional swarm computing, multiple objects (agents) collaborate autonomously to solve problems or perform tasks by sharing information and interacting with each other without the need for centralized control. Each agent in the swarm makes decisions based on local information and interactions, leading to emergent global behaviors that solve complex tasks, such as pathfinding, resource allocation, or optimization.
@@ -336,7 +346,7 @@ Together, the integration of Swarm Computing, edge-based IoT platforms, and MEC
### 5.3.2 Smart Warehouse Automation
### 5.2.2 Smart Warehouse Automation
This use case describes how ETSI MEC and oneM2M platforms can work together to enable real-time warehouse automation, including coordination of autonomous guided vehicles (AGVs), environmental monitoring, and asset tracking. The system architecture integrates a centralized oneM2M IN-CSE for managing warehouse infrastructure data and multiple MN-CSE instances deployed as MEC applications to provide low-latency control and decision-making.
@@ -346,7 +356,7 @@ As warehouse operations span large areas, AGVs may move between MEC zones. The M
### 5.3.3 Industrial Digital Twins
### 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.
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.
@@ -359,11 +369,13 @@ As mobile industrial assets like AGVs or modular production units relocate withi
## 5.4 Maritime
## 5.3 Maritime
### 5.3.0 Description
The maritime sector is undergoing significant transformation through the adoption of automation and advanced sensing technologies that enable vessels to navigate and perform tasks autonomously. These connected ships must maintain continuous communication with operation centers across the edge-cloud continuum to ensure effective monitoring and decision-making. This section presents a use case that integrates the oneM2M IoT platform with the ETSI MEC edge computing framework to deliver real-time assisted manoeuvring for unmanned vessels in seaport areas, supporting low-latency data processing, seamless service continuity, and mission-critical functionalities.
### 5.4.1 Assisted Manoeuvring for Autonomous Ship
### 5.3.1 Assisted Manoeuvring for Autonomous Ship
Maritime Autonomous Surface Ships (MASS) technologies have been developing rapidly during last decade. Unmanned ships should be able to navigate without colliding with other vessels, considering the naval traffic and unexpected situations. These ships are usually equipped with advanced technologies like integrated automation systems based on AI-models and sensing capabilities which allow them to navigate, make decisions and perform tasks autonomously. They usually rely on the communication with the Remote Operation Centers (ROCs) serving as command hubs for monitoring, controlling and managing ships from the shore. ROCs serve as a pivotal hub for overseeing and controlling unmanned vessels remotely from onshore locations so that operators can utilize advanced sensor technologies and data telemetry to monitor vessel position, speed, course, environmental conditions, and other operational parameters in real-time.
@@ -375,11 +387,13 @@ As the vessel transits through different MASS zones, the proposed oneM2M/MEC arc
## 5.5 Metaverse
## 5.4 Metaverse
### 5.4.0 Description
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
### 5.4.1 Smart Shopping with Edge-AI and Cloud IoT Integration
This use case describes a smart virtual shopping service within a metaverse environment, where users can experience immersive, interactive shopping that is tightly integrated with real-world data and edge intelligence. A user enters a virtual shopping mall through a metaverse interface—either via VR/AR devices or standard 3D web platforms. Inside the virtual store, the layout, available products, and store conditions reflect those of an actual physical store in real time. This synchronization is made possible through IoT devices deployed in the physical store, which are directly connected to a cloud-based oneM2M platform (IN-CSE). These devices stream data such as shelf inventory levels, product locations, environmental conditions (e.g., temperature), and user presence to the clould IoT platform.
@@ -389,7 +403,9 @@ If needed, IoT actuators in the real store—such as digital signage or voice as
## 5.6 Future Home
## 5.5 Future Home
### 5.5.0 Description
The Smart Home domain is rapidly evolving toward immersive, responsive, and personalized services powered by real-time data and intelligent automation. This clause presents a use case that integrates the oneM2M IoT platform with the ETSI MEC framework to enable advanced home experiences such as real-time media interactions, remote education, elderly care, and intelligent automation.
