ETSI GR MEC-DEC 050 V4.1.1 (2025-11)

Multi-access Edge Computing (MEC); ESTIMED Use Cases & Recommendations

DGR/MEC-DEC050EstimedRec

General Information

• Status: Not Published
• Technical Committee: MEC DECODE - Deployment and ECOsystem DEvelopment
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 28-Nov-2025
• Completion Date: 27-Nov-2025

Standards Content (Sample)

GROUP REPORT
Multi-access Edge Computing (MEC);
ESTIMED Use Cases & Recommendations
Disclaimer
The present document has been produced and approved by the Multi-access Edge Computing (MEC) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)

Reference
DGR/MEC-DEC050EstimedRec
Keywords
M2M, MEC, oneM2M
ETSI
650 Route des Lucioles
F-06921 Sophia Antipolis Cedex - FRANCE

Tel.: [phone]  Fax: [phone]

Siret N° 348 623 562 00017 - APE 7112B
Association à but non lucratif enregistrée à la
Sous Préfecture de Grasse (06) N° w061004871

Important notice
The present document can be downloaded from the
ETSI Search & Browse Standards application.
The present document may be made available in electronic versions and/or in print. The content of any electronic and/or
print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any
existing or perceived difference in contents between such versions and/or in print, the prevailing version of an ETSI
deliverable is the one made publicly available in PDF format on ETSI deliver repository.
Users should be aware that the present document may be revised or have its status changed,
this information is available in the Milestones listing.
If you find errors in the present document, please send your comments to
the relevant service listed under Committee Support Staff.
If you find a security vulnerability in the present document, please report it through our
Coordinated Vulnerability Disclosure (CVD) program.
Notice of disclaimer & limitation of liability
The information provided in the present deliverable is directed solely to professionals who have the appropriate degree of
experience to understand and interpret its content in accordance with generally accepted engineering or
other professional standard and applicable regulations.
No recommendation as to products and services or vendors is made or should be implied.
No representation or warranty is made that this deliverable is technically accurate or sufficient or conforms to any law
and/or governmental rule and/or regulation and further, no representation or warranty is made of merchantability or fitness
for any particular purpose or against infringement of intellectual property rights.
In no event shall ETSI be held liable for loss of profits or any other incidental or consequential damages.

Any software contained in this deliverable is provided "AS IS" with no warranties, express or implied, including but not
limited to, the warranties of merchantability, fitness for a particular purpose and non-infringement of intellectual property
rights and ETSI shall not be held liable in any event for any damages whatsoever (including, without limitation, damages
for loss of profits, business interruption, loss of information, or any other pecuniary loss) arising out of or related to the use
of or inability to use the software.
Copyright Notification
No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and
microfilm except as authorized by written permission of ETSI.
The content of the PDF version shall not be modified without the written authorization of ETSI.
The copyright and the foregoing restriction extend to reproduction in all media.

© ETSI 2025.
All rights reserved.
ETSI
3 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 7
3.1 Terms . 7
3.2 Symbols . 7
3.3 Abbreviations . 7
4 Overview . 8
5 Edge & IoT Domains and Use Cases . 9
5.1 Introduction . 9
5.2 Smart City & Mobility . 9
5.2.1 Description . 9
5.2.2 Autonomous Vehicle with Continuous Edge Computing . 10
5.2.3 Vulnerable Road Users . 11
5.3 Industrial & Robotics . 12
5.3.1 Description . 12
5.3.2 Swarm-based Autonomous Ant Delivery Optimization . 12
5.3.3 Smart Warehouse Automation . 13
5.3.4 Industrial Digital Twins . 14
5.4 Maritime . 15
5.4.1 Description . 15
5.4.2 Assisted Manoeuvring for Autonomous Ship . 16
5.5 Metaverse . 17
5.5.1 Description . 17
5.5.2 Smart Shopping with Edge-AI and Cloud IoT Integration . 17
5.6 Future Home . 18
5.6.1 Description . 18
5.6.2 User Premises Edge and oneM2M Integration . 18
6 MEC-oneM2M Architectural & Use Case Mapping . 19
6.1 Introduction . 19
6.2 MEC Frameworks . 19
6.3 oneM2M Components . 21
6.3.1 Introduction to oneM2M . 21
6.3.2 oneM2M Architecture . 21
6.3.3 oneM2M Architecture Common Service Layer Functions . 23
6.3.4 Mapping between MEC and oneM2M . 23
6.3.5 Virtualisation of oneM2M Common Service Layer functions . 24
6.4 Use Cases & Frameworks Mapping . 24
6.4.1 Introduction to Use Cases & Frameworks Mapping . 24
6.4.2 Deployment Options . 24
6.4.2.1 Option A: deploy the oneM2M as a cloud, MEC as an edge . 24
6.4.2.2 Option B: oneM2M and MEC as an edge with the different physical node . 25
6.4.2.3 Option C: oneM2M and MEC in the same physical edge node . 26
6.4.2.4 Option D: oneM2M and MEC are tightly coupled in the same edge node . 27
6.4.3 Autonomous Vehicle with Continuous Edge Computing . 28
6.4.3.1 Use Case Driving Deployment . 28
6.4.4 Vulnerable Road Users . 29
6.4.4.1 Use Case Driving Deployment . 29
6.4.5 Swarm-based Autonomous Ant Delivery Optimization . 30
ETSI
4 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
6.4.5.1 Use Case Driving Deployment . 30
6.4.6 Smart Warehouse Automation . 31
6.4.6.1 Use case Driven Deployment . 31
6.4.7 Industrial Digital Twins . 32
6.4.7.1 Use case Driven Deployment . 32
6.4.8 Assisted Manoeuvring for Autonomous Ships . 33
6.4.8.1 Use case Driven Deployment . 33
6.4.9 Smart Metaverse Shopping with Edge-AI and Cloud-IoT Integration . 34
6.4.9.1 Use case Driven Deployment . 34
6.4.10 Future Homes. 35
6.4.10.1 Use case Driven Deployment . 35
7 Proposed Recommendations for Federation and Orchestration Mechanisms . 37
7.1 Introduction . 37
7.2 Handover . 38
7.3 Swarm Computing . 40
7.4 oneM2M and MEC support for federated Learning . 43
History . 46

ETSI
5 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members , and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI IPR online database.
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
Trademarks
The present document may include trademarks and/or tradenames which are asserted and/or registered by their owners.
ETSI claims no ownership of these except for any which are indicated as being the property of ETSI, and conveys no
right to use or reproduce any trademark and/or tradename. Mention of those trademarks in the present document does
not constitute an endorsement by ETSI of products, services or organizations associated with those trademarks.
DECT™, PLUGTESTS™, UMTS™ and the ETSI logo are trademarks of ETSI registered for the benefit of its
Members. 3GPP™, LTE™ and 5G™ logo are trademarks of ETSI registered for the benefit of its Members and of the
3GPP Organizational Partners. oneM2M™ logo is a trademark of ETSI registered for the benefit of its Members and of
the oneM2M Partners. GSM® and the GSM logo are trademarks registered and owned by the GSM Association.
Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Multi-access Edge Computing
(MEC).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

ETSI
6 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
1 Scope
The present document provides a set of selected use cases that leverage IoT and Edge computing technologies, with a
particular focus on standard-based implementations where oneM2M and ETSI MEC frameworks can be applied.
Building on the joint MEC-oneM2M White Paper [i.2], it refines and expands the identified synergies between these
frameworks and offers a detailed analysis of architectural evolutions, including necessary components, required
mappings, and potential enhancements to facilitate interoperability and integration between oneM2M and MEC-based
deployments.
By analysing real-world and industry-relevant use cases, the present document highlights how the convergence of IoT
and Edge computing can enable scalable, efficient, and interoperable solutions across different domains. The scope
extends to evaluating the applicability of existing standards, identifying gaps, and proposing recommendations to ensure
alignment with ongoing standardization efforts within ETSI and oneM2M. The present document serves as a reference
for stakeholders to understand the practical implications of integrating MEC and oneM2M technologies in future
IoT-Edge ecosystems.
The scope includes recommendations and proposals for MEC-oneM2M interworking deployment patterns across the
edge-cloud continuum, covering the four deployment options to enable latency‑critical processing and service
continuity, the exposure of oneM2M IoT data and control services through the MEC platform for consumption by
applications that access and actuate IoT resources, and hybrid multi‑tier arrangements that distribute functions across
user equipment or gateways, and centralized infrastructure for hierarchical data management and orchestration. It also
encompasses cross‑edge Artificial Intelligence approaches that are central to the stated goals, including federated
learning for privacy‑preserving collaborative model training and edge inference across MEC nodes and
oneM2M‑managed data spaces, and swarm computing for distributed, real‑time coordination of agents and devices
supported by edge‑hosted IoT services and MEC compute. The resulting recommendations address the functional
enablers, mappings, and enhancements required to operationalize these deployment patterns and intelligent capabilities
across representative use cases.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document.
[i.1] ETSI GS MEC 003: "Multi-access Edge Computing (MEC); Framework and Reference
Architecture".
[i.2] ETSI White Paper No. #59: "Enabling Multi-access Edge Computing in Internet-of- Things: how
to deploy ETSI MEC and oneM2M".
[i.3] ESTIMED Project Official Website.
[i.4] ETSI GS MEC 009: "Multi-access Edge Computing (MEC); General principles, patterns and
common aspects of MEC Service APIs".
[i.5] ETSI GS MEC 010-2: "Multi-access Edge Computing (MEC); MEC Management; Part 2:
Application lifecycle, rules and requirements management".
ETSI
7 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
[i.6] ETSI GS MEC 011: "Multi-access Edge Computing (MEC); Edge Platform Application
Enablement".
[i.7] ETSI GS MEC 013: "Multi-access Edge Computing (MEC); Location API".
[i.8] ETSI GS MEC 012: "Multi-access Edge Computing (MEC); Radio Network Information API".
[i.9] ETSI GS MEC 015: "Multi-access Edge Computing (MEC); Traffic Management APIs".
[i.10] ETSI GS MEC 016: "Multi-access Edge Computing (MEC); Device application interface".
[i.11] ETSI GS MEC 021: "Multi-access Edge Computing (MEC); Application Mobility Service API".
[i.12] ETSI GS MEC 028: "Multi-access Edge Computing (MEC); WLAN Access Information API".
[i.13] ETSI GS MEC 029: "Multi-access Edge Computing (MEC); Fixed Access Information API".
[i.14] ETSI GS MEC 030: "Multi-access Edge Computing (MEC); V2X Information Services API".
[i.15] ETSI GS MEC 033: "Multi-access Edge Computing (MEC); IoT API".
[i.16] ETSI GS MEC 040: "Multi-access Edge Computing (MEC); Federation enablement APIs".
[i.17] ETSI GS MEC 045: "Multi-access Edge Computing (MEC); QoS Measurement API".
[i.18] ETSI GS MEC 046: "Multi-access Edge Computing (MEC); Sensor-sharing API".
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
3D Three-Dimensional
3GPP 3rd Generation Partnership Project
5G Fifth Generation mobile networks
ADN Application Dedicated Node
ADN-AE Application Dedicated Node - Application Entity
AE Application Entity
AGV Autonomous Guided Vehicles
AIS Automatic Identification System
AI Artificial Intelligence
API Application Programming Interface
API GW API GateWay
AR Augmented Reality
ASN Application Service Node
ASN-CSE Application Service Node - Common Services Entity
AV Autonomous Vehicle
CNC Computer Numerical Control
CoAP Constrained Application Protocol
CPE Customer Premises Equipment
CPU Central Processing Unit
ETSI
8 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
CSE Common Services Entity
CSF Common Service Function
DT Digital Twin
FL Federated Learning
GPU Graphics Processing Unit
HTTP Hypertext Transfer Protocol
HV Host Vehicle
IDT Industrial Digital Twin
IN Infrastructure Node
IN-AE Infrastructure Node - Application Entity
IN-CSE Infrastructure Node - Common Services Entity
IoT Internet of Things
IPE Interworking Proxy application Entity
LIDAR LIght Detection And Ranging
MEP MEC Platform
MN-AE Middle Node AE
MN-CSE Middle Node - Common Services Entity
NaaS Network as a Service
NFV Network Function Virtualisation
NoDN Non-oneM2M Node
NSE Network Services Entity
OAS OpenAPI Specification
OCF Open Connectivity Foundation
ROC Remote Operation Centre
SAREF Smart Appliances REFerence
VIM Virtualisation Infrastructure Manager
VRU Vulnerable Road User
4 Overview
This clause introduces the present document by clearly defining its purpose, scope, and key objectives. It lays the
groundwork for the integration of the ETSI MEC and oneM2M frameworks, highlighting their critical role in enabling
advanced Edge and IoT deployments across a wide range of application domains.
The ESTIMED project [i.3] takes a use case-driven approach to steer the integration of ETSI MEC and oneM2M. This
strategy effectively demonstrates the tangible benefits of merging edge computing capabilities with standardized IoT
architectures. By anchoring the analysis in real-world scenarios, the approach not only guides integration efforts but
also informs future standardization initiatives to better meet operational demands. The project emphasizes identifying
new functional requirements and potential extensions to existing specifications, thereby contributing to the continuous
refinement and evolution of both MEC and oneM2M frameworks in response to emerging IoT and edge computing
challenges. Following this methodology, the present document is organized into three main clauses, each addressing a
core aspect of the analysis.
Clause 5 delivers a domain-centric analysis of carefully selected use cases, illustrating the practical application of MEC
and oneM2M in Edge-IoT environments. Use cases are categorized by relevant domains-such as Mobility, Industrial,
and Maritime to reflect their specific operational contexts. Within each domain, use cases are thoroughly examined,
covering the scenario context, involved stakeholders, technical and operational requirements, and key challenges. The
clause also explores how MEC and oneM2M technologies contribute to overcoming these challenges. The use cases
were identified through a structured engagement with stakeholders, facilitated by a dedicated form designed to capture
all critical details consistently, including descriptions, actors, requirements, preconditions and postconditions, triggers,
and interaction flows.
Building on this foundation, clause 6 analyses the principal architectures of MEC and oneM2M frameworks,
emphasizing their core functionalities and complementarity in supporting Edge-IoT deployments. For each use case, the
clause maps relevant architectural components and capabilities from both frameworks, highlighting overlaps as well as
identifying new features that could enhance support for specific scenarios. This mapping lays the groundwork for a
comprehensive architectural reflection and technical assessment.
Clause 7 includes recommendations that enable a framework for the federation and orchestration of MEC and oneM2M
instances. It also discusses how these capabilities can be used in Swarm Computing and Federated Learning
applications.
ETSI
9 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
Overall, the present document establishes a structured and cohesive foundation to understand and advance how MEC
and oneM2M can jointly enable scalable, interoperable, and forward-looking Edge-IoT solutions.
5 Edge & IoT Domains and Use Cases
5.1 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.
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.
Table 5.1-1 summarizes the domains and the associated use cases covered in this clause.
Table 5.1-1: Domains covered
Domain Use Cases Focus
• Autonomous Vehicles and Edge Real-time data processing, V2X
Smart City & Mobility Continuum communication, urban mobility
• Vulnerable Road User Detection optimization, traffic safety
• Swarm-based Autonomous Ant
Delivery Optimization Intelligent coordination, low-latency control,
Industrial & Robotics
• Smart Warehouse Automation industrial automation, edge-based robotics
• Industrial Digital Twins
Remote vessel monitoring, mission-critical
• Assisted Manoeuvring for Connected
Maritime edge processing, seamless edge-cloud
Ships
communication
IoT-enhanced virtual environments, edge-
Metaverse • Smart Virtual Shopping Service hosted AI analytics, immersive low-latency
digital experiences
Real-time media, education, health, and
Future Home • Advanced Smart Home Services 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
5.2.1 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.
ETSI
10 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
5.2.2 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 (schematically reported in
Figure 5.2.2-1) 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.
Service Continuity is a critical aspect of this use case. Initially, the IN-CSE in the cloud manages and stores all data
related to the AV, including location, sensor data (e.g. LIDAR, radar), and external conditions such as road
infrastructure. When an AV enters a zone covered by a nearby MEC node, the IN-CSE dynamically offloads data
processing tasks (such as real-time object detection, collision risk analysis, and navigation updates) to the MN-CSE
instances deployed at the edge. These instances not only handle locally generated data but also share additional
real-time data from other MEC applications to enhance decision-making. The MN-CSE processes the data locally,
providing immediate feedback and decision-making (e.g. braking, turning, lane-changing) with minimal latency,
ensuring that the vehicle operates efficiently.
As the AV moves across different zones, it will seamlessly transition from one MEC node to another. The system
continuously monitors the AV's location and detects when the vehicle approaches a new MEC zone. Upon detection, the
system initiates the migration of the MN-CSE's offloaded data and applications to the new MEC node. This migration
ensures that all real-time data and applications are immediately available at the new location, guaranteeing that the AV
experiences uninterrupted service as it moves. The oneM2M system synchronizes between the cloud and edge, ensuring
that all data is consistent and up-to-date. This edge-to-edge service continuity is critical for maintaining performance
and safety in dynamic environments.
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 5.2.2-1: Autonomous Vehicle Edge Computing service continuity
ETSI
11 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
5.2.3 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. As illustrated in
Figure 5.2.3-1, 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.
Meanwhile, edge-located MN-CSE instances, deployed as MEC applications on distributed edge nodes, act as localized
IoT platforms that process time-sensitive data near the vehicles. When a Host Vehicle (HV) enters a zone covered by a
nearby MEC node, the cloud IN-CSE dynamically offloads VRU detection tasks - such as real-time analytics and safety
alert generation-to the edge-resident MN-CSE. This offloading ensures ultra-low-latency processing, as the MN-CSE
can immediately analyse VRU data (e.g. from smartphones, roadside sensors) and issue warnings to the HV without
relying on distant cloud servers.
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 5.2.3-1: Scenario of resource and tasks offloading to support VRU application
ETSI
12 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
5.3 Industrial & Robotics
5.3.1 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.
5.3.2 Swarm-based Autonomous Ant Delivery Optimization
This scenario explores a hybrid swarm-based autonomous delivery system inspired by ant colony behaviour. Swarm
computing is a computational model based on the collective behaviour 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 behaviours that solve complex tasks, such as pathfinding, resource allocation, or
optimization.
However, when applied to real-world systems like autonomous delivery or energy management, fully decentralized
swarm systems face several limitations. For instance, without any centralized coordination or additional guidance, the
swarm may face challenges in optimizing complex tasks or adapting to dynamic, large-scale environments. Swarm
systems can struggle with task allocation, resource optimization, and dealing with environmental changes when
working entirely autonomously without external help. Thus, this use case explores how swarm agents, integrated with a
cloud IoT platform and assisted by edge nodes, can more effectively solve given problems.
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.
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 (depicted also in Figure 5.3.2-1), 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.
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.
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.
ETSI
13 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)

Figure 5.3.2-1: Data flow and interaction in a hybrid robot swarm system
5.3.3 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 reported in Figure 5.3.3-1 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.
The oneM2M IN-CSE collects and stores data from IoT devices such as temperature and humidity sensors, RFID tags,
and AGV telemetry. When time-critical operations occur (e.g. routing AGVs, responding to a gas leak, or dynamic
reallocation of assets), relevant tasks are offloaded to MEC-based MN-CSEs. The MEC platform processes this data
locally, allowing immediate decisions such as rerouting AGVs, sending alerts to personnel, or controlling air filtration
systems.
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.
ETSI
14 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)

Figure 5.3.3-1: High-level overview of the warehouse automation scenario
5.3.4 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 illustrated in Figure 5.3.4-1 relies on a cloud-based oneM2M Infrastructure Node
Common Service Entity (IN-CSE), which functions as a centralized orchestrator and digital repository, and multiple
Middle Node Common Service Entities (MN-CSEs) 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.
Real-time insight is achieved through the ability of MN-CSEs to execute localized, time-critical functions including
anomaly detection, in-line quality control, and safety compliance checks. The system ensures seamless service
continuity across the edge-cloud continuum by dynamically migrating services and synchronizing digital twin data as
assets move across operational zones. This continuity is crucial for uninterrupted performance of digital twins as the
physical counterparts transition between factory cells or sites. The solution is grounded in oneM2M's standardized
resource and data models, enabling interoperability across diverse devices and platforms.
ETSI
15 ETSI GR MEC-DEC 050 V4.1.1 (2025-11)
Initially, the IN-CSE in the cloud hosts the master representations of industrial assets - such as CNC machines,
collaborative robots (cobots), or Automated Guided Vehicles (AGVs) - maintaining records of their operational states,
historical data, and environmental contexts. When local operations are initiated in a specific factory cell that is served
by a MEC node, the IN-CSE delegates processing to the nearest MN-CSE deployed on the edge. This MN-CSE ingests
live data streams including vibration patterns, temperature levels, system workload, and diagnostic logs, and performs
localized analytics such as early fault detection, micro-adjustments to optimize yield, and predictive maintenance alerts.
Additionally, the MN-CSE may interact with other applications hosted on the MEC platform, such as AI-based vision
systems for quality inspection or real-time control systems for robotic motion, by leveraging MEC-provided APIs.
As mobile industrial assets like AGVs or modular production units
...