Fact-checked by Grok 2 weeks ago

Multi-access edge computing

Multi-access edge computing (MEC) is a network architecture concept that enables the deployment of cloud-computing capabilities and an IT service environment at the edge of telecommunications networks, proximate to end users and devices. Defined by the European Telecommunications Standards Institute (ETSI), MEC provides ultra-low latency, high bandwidth, and real-time access to radio network information, allowing applications to process data closer to the source rather than relying on distant centralized cloud servers. This paradigm supports multiple access types, including mobile, fixed, and wireless local area network (WLAN) connections, facilitating efficient data handling in diverse environments. Originally termed mobile edge computing when introduced by in the mid-2010s, the framework evolved and was renamed multi-access edge computing in September 2017 to broaden its scope beyond cellular networks to include fixed and other access technologies. Standardization efforts are led by 's MEC Industry Specification Group (ISG), which has advanced through phased developments: Phase 1 focused on foundational architecture, Phase 2 on integration with and other networks, Phase 3 (completed in April 2024) on deployment enablers, and Phase 4 (2024–2026) emphasizing security, federation, and preparation for systems, with initial specifications released in November 2025. The group's outputs include technical specifications for , platforms, and , available through 's forge repository. MEC integrates seamlessly with 5G infrastructure as defined by the 3rd Generation Partnership Project (), where capabilities are natively supported in the 5G Core (5GC) and Next Generation Radio Access Network (NG-RAN). Initial support emerged in 3GPP Release 15 with features like User Plane Function (UPF) reselection and Local Area Data Network (LADN), while Release 17 introduced enhancements such as Edge Application Server (EAS) discovery, edge relocation procedures, and a dedicated edge enabler layer outlined in TS 23.558. Release 18 further refines aspects like and federation in alignment with and guidelines. This integration enables connectivity models including distributed anchors, session breakouts, and multiple (PDU) sessions, optimizing data flows for low-latency applications. Key benefits of MEC include significant reductions (up to 2–10 times lower than centralized processing), alleviation of core through local content caching, and enhanced security and privacy by minimizing data transit distances. It also unlocks new revenue streams for network operators, vendors, and developers by enabling rapid deployment of innovative services. Prominent use cases span vehicle-to-everything (V2X) communications for autonomous driving, industrial IoT for real-time automation, augmented and virtual reality (AR/VR) for immersive experiences, video analytics for smart cities, and operations requiring precise control. The architecture typically hosts MEC applications on edge nodes above the network layer, leveraging standardized interfaces for seamless orchestration across multi-vendor environments.

Overview

Definition and Core Principles

Multi-access edge computing (MEC) is a defined by the that provides capabilities and an IT service environment at the edge of multi-access networks, enabling application developers and content providers to deliver services in close proximity to end users across cellular, , and fixed access technologies. This architecture supports the deployment of applications directly within the (RAN) or nearby edge locations, allowing for efficient processing of data generated by user devices without the need to route it to distant central data centers. The core principles of MEC revolve around proximity to end users to minimize , distributed integrated with the RAN for real-time decision-making, and enabling support for latency-sensitive applications such as , autonomous vehicles, and industrial . By leveraging infrastructure, MEC hosts run applications that can access real-time information through standardized , ensuring high and ultra-low —often achieving response times in the range of a few milliseconds. This distributed approach also promotes deployment flexibility, from on-premise installations to deeper integration at the network edge, facilitating context-aware services that respond dynamically to user location and network conditions. In comparison to centralized cloud computing, MEC significantly reduces end-to-end latency by processing data locally; for instance, edge deployments can achieve latency fluctuations as low as 0.5 ms and up to an 84.1% reduction relative to central clouds, which typically incur tens of milliseconds due to data traversal over core networks. Unlike fog computing, which is more device-centric and extends processing to end-user equipment or local gateways in diverse networks, MEC is tailored to telecommunications infrastructure, emphasizing seamless integration with the RAN for operator-managed, multi-access environments. Key terminology in MEC includes edge nodes, which are network points at the hosting MEC services, and MEC hosts, virtualized platforms that provide the for running edge applications with access to network resources. The term "multi-access" reflects the evolution from the earlier "mobile edge computing" concept, renamed by in 2017 to encompass not only cellular but also and fixed broadband accesses, broadening its applicability in converged network ecosystems.

History and Evolution

The concept of , which underpins multi-access edge computing (MEC), traces its origins to the late 1990s with the development of content delivery networks (CDNs) designed to distribute web and video content closer to end-users, thereby reducing and improving performance amid growing . Companies like Akamai pioneered these networks by deploying surrogate servers at the network periphery to cache and deliver data efficiently, laying the groundwork for localized processing that would later evolve into more sophisticated edge paradigms. In 2014, the () established the Mobile Edge Computing (MEC) Industry Specification Group (ISG) as a collaborative initiative led by telecom operators to integrate (IT) services directly into the (RAN), enabling cloud-like capabilities at the edge. The group's first meeting occurred in December 2014, hosted by , and focused on standardizing an open ecosystem for edge applications. By 2017, recognizing the need to extend beyond mobile cellular networks, renamed the initiative to Multi-access Edge Computing to incorporate fixed and access technologies, broadening its scope to multi-access environments. Key milestones in MEC's evolution include its integration with standards in 2018, when and the () collaborated to define enablers for within systems, allowing seamless interaction between MEC platforms and cores for low-latency services. In the , MEC expanded to incorporate () and () capabilities at the edge, supporting real-time analytics and decision-making in distributed environments, as outlined in 's ongoing efforts. A notable 2025 update came with the release of Group Report GR MEC 036 (version 4.1.1 in August 2025), which addresses MEC deployment in resource-constrained environments, such as far-edge devices, to enable lightweight cloud services for applications. On November 4, 2025, released the first specifications for Phase 4 (GS MEC 009, GS MEC 010-2, GS MEC 011, GS MEC 012, and GS MEC 013) along with a on integrating open-source technologies and standards for edge clouds, advancing , federation, and interoperability. This progression was driven by the explosive growth of (IoT) devices generating vast data volumes at the network periphery, necessitating localized processing to manage and concerns. Additionally, 5G's ultra-reliable low-latency communication (URLLC) requirements, targeting latencies under 1 ms for mission-critical applications, propelled MEC's adoption to meet these stringent performance needs without relying on distant centralized clouds. The convergence of these factors transformed MEC from a mobile-centric concept into a versatile, multi-access framework essential for next-generation networks.

Architecture and Components

Key Architectural Elements

Multi-access edge computing (MEC) systems are built upon a distributed architecture that positions computational resources close to the network edge. At the core of this architecture are MEC hosts, which are physical or virtual entities comprising one or more compute nodes, along with associated networking and storage resources. These hosts are typically deployed at aggregation points or co-located with radio access network (RAN) elements, such as base stations, to enable low-latency processing for end-user applications. The virtualization infrastructure within a MEC host manages resource allocation, including the data plane for traffic routing and forwarding, ensuring efficient handling of user data flows. The MEC platform serves as the foundational software layer running atop the virtualization infrastructure of a MEC host, facilitating the deployment and operation of MEC applications. It leverages (NFV) and (SDN) principles to virtualize resources, allowing applications to run in virtual machines (VMs) or containers. Key functions of the MEC platform include traffic steering and rule enforcement, DNS handling, and an gateway for secure exposure. This platform enables multi-tenancy by isolating resources and services for multiple tenants or operators on the same infrastructure, promoting efficient resource sharing across diverse applications. MEC applications are virtualized software instances that execute on the MEC platform, consuming or providing services through standardized interfaces like the Mp1 reference point, which has been enhanced for improved service registration and discovery. These applications can include edge-specific functions, such as content caching or analytics, tailored to leverage proximity to the (UE). Service discovery is supported via on the MEC platform, allowing applications to register and locate services dynamically within the edge . Additionally, the information service (RNIS) provides applications with access to radio network status, such as UE location and signal conditions, enabling location-aware processing and optimized service delivery. Orchestration in MEC involves centralized and distributed management to handle across multiple MEC hosts. The MEC orchestrator oversees system-wide operations, including , host selection for deployment, and coordination with external systems like the NFV orchestrator in NFV-based variants; in NFV contexts, the MEC Application Orchestrator (MEAO) supports these functions. This ensures seamless scaling and mobility support for applications migrating between edge locations. is further enhanced through technologies, which allow for lightweight, dynamic resource provisioning; for instance, container orchestration platforms like are commonly employed to manage containerized MEC applications in distributed edge environments. The MEC reference architecture delineates a functional split between the user plane and to optimize and signaling, with variants including support for MEC across domains and Security Monitoring and Management (SMM) for enhanced protection. The user plane, handled primarily by the data plane in the infrastructure, processes and routes application traffic at , minimizing transit delays. In contrast, the , managed by the MEC platform and orchestrator, focuses on centralized signaling, policy enforcement, and service coordination, integrating briefly with RAN elements for enhanced edge intelligence. This supports the overall ETSI-defined framework for interoperable MEC deployments.

Integration with Access Networks

Multi-access edge computing (MEC) integrates closely with various technologies to enable low-latency processing and efficient data handling at the network periphery. A primary integration point involves co-locating MEC servers with 4G evolved Node B (eNB) base stations in a "bump in the wire" deployment model, where MEC acts as an intermediary for and GTP-U packet , supporting local breakout to local area networks (LANs) via the S1 . In 5G networks, this extends to co-location with next-generation Node B (gNB) base stations, mapping MEC functionality to the User Plane Function (UPF) for seamless traffic steering and reduced core network dependency. Similarly, MEC supports integration with Wi-Fi access points and wireline edge routers, deploying servers alongside (RAN) elements or fixed broadband infrastructure to handle diverse traffic flows without assuming a specific radio technology. These co-location strategies minimize transmission delays by processing data closer to the , leveraging standard interfaces for across access types. Key protocols and interfaces facilitate this integration, drawing from and standards. The N6 interface, aligned with 3GPP's system architecture, connects the MEC user plane to the UPF, enabling efficient data plane routing for user traffic between edge applications and the core network. The MP2 interface exposes RAN capabilities to the MEC platform, allowing programmable data plane instructions for traffic steering among applications and external networks, such as directing flows based on application needs. -defined , via the Mp1 reference point, ensure service continuity by supporting application session state relocation, registration, and during mobility events, thus maintaining uninterrupted edge services. These mechanisms, combined with the Network Exposure Function (NEF) in , provide northbound for capability exposure, bridging MEC with core functions like (QoS) management. MEC's multi-access support ensures seamless connectivity across heterogeneous networks, including cellular, Wi-Fi, and fixed access. It handles handovers by reallocating application instances to target MEC hosts, distinguishing between intra-host mobility (no reallocation needed) and inter-host mobility (requiring service continuity via session relocation). The Access and Mobility Management Function (AMF) in 5G coordinates these transitions, minimizing disruptions across network types while supporting non-3GPP access integration into the 5G core for unified authentication and policy enforcement. This capability addresses challenges like interference and packet loss in multi-access environments, enhancing overall quality of experience (QoE) through ETSI-defined networking layers that abstract underlying access technologies. In 5G deployments, MEC enhances network slicing by provisioning dedicated edge resources tailored to specific service types. For ultra-reliable low-latency communications (URLLC), MEC allocates computational and storage resources at the to process time-sensitive data, ensuring high reliability and minimal delays in industrial automation scenarios. Enhanced (eMBB) benefits from MEC's caching and offloading mechanisms, which boost efficiency by localizing content delivery and reducing backhaul load. For massive machine-type communications (mMTC), MEC scales to manage high volumes of data through distributed processing, integrating with (SDN) and (NFV) for virtualized per-slice resource isolation. This slicing-aware integration allows operators to dynamically allocate edge capabilities per slice, optimizing performance across diverse requirements without compromising isolation. The -to- continuum in MEC forms a hierarchical structure that extends processing capabilities across layers, from the device to the central . At the device , local computation on or sensors handles immediate tasks, such as initial data filtering in applications. Intermediate layers, often co-located with access nodes, aggregate and analyze data for enhanced , bridging immediacy with broader coordination. The central layer manages complex, resource-intensive operations like large-scale , enabling dynamic task offloading and across the continuum to achieve reductions of 40-60% while preserving data privacy. This layered approach, supported by connectivity, ensures seamless resource orchestration and in MEC ecosystems.

Benefits and Advantages

Technical Benefits

One of the primary technical benefits of multi-access edge computing (MEC) is significant latency reduction, achieved by processing closer to the end-user rather than transmitting it to distant centralized clouds. In MEC deployments, end-to-end latencies can reach 1-10 ms for latency-sensitive applications, such as smart factories and automotive systems, compared to over 100 ms typically experienced in central cloud environments. Such reductions enable responsiveness critical for applications requiring sub-20 ms delays. MEC also optimizes bandwidth usage through local caching and on-site , which substantially decreases the volume of routed over backhaul links to the core network. These mechanisms can alleviate and improve overall network efficiency. Additionally, offloading computational tasks from end devices to nearby MEC servers extends device battery life by minimizing local processing demands, particularly beneficial for resource-constrained endpoints. In terms of reliability and scalability, MEC's distributed architecture provides fault isolation, where failures in one edge node do not propagate across the entire system, achieving availability levels up to 99.999% for critical services. This design supports massive connectivity, aligning with capabilities to handle up to 1 million devices per square kilometer, enabling scalable deployment in dense environments without overwhelming central infrastructure. Furthermore, MEC enhances (QoS) by integrating real-time analytics from (RAN) data, facilitating dynamic such as bandwidth slicing and traffic prioritization based on location and demand. This RAN awareness allows for predictive QoS adjustments, ensuring low delay variation and high throughput (e.g., up to 200 Mbps) in varying network conditions.

Business and Economic Impacts

Multi-access edge computing (MEC) enables operators (telcos) to transform their networks into platforms for innovative services, driving substantial economic value through diversified models and operational efficiencies. By decentralizing compute resources to the network edge, MEC facilitates real-time data processing that aligns with enterprise demands for low-latency applications, thereby enhancing competitive positioning in the era. These impacts extend beyond technical enhancements, fostering a where telcos can monetize infrastructure assets previously underutilized for core connectivity alone. A primary business opportunity lies in new revenue streams, such as edge-as-a-service (EaaS) offerings, where telcos provide on-demand compute, storage, and orchestration at the edge via models like infrastructure-as-a-service (IaaS) or . This allows operators to lease edge resources to enterprises for applications like or industrial automation, generating recurring income from otherwise idle and sites. Complementing this, monetization enables telcos to expose network functions—such as location accuracy or quality-of-service guarantees—to third-party developers, creating ecosystems for app innovation and slicing-based services. The global MEC market, reflecting these opportunities, is estimated at USD 5–8.5 billion as of 2025 and projected to expand to USD 34–259 billion by 2030–2035, with compound annual growth rates (CAGRs) ranging from 18.9% to 47.6% across analyses. Ecosystem partnerships amplify these revenue potentials by combining telco network proximity with hyperscaler cloud expertise. Collaborations, such as , embed into telco radio access networks, enabling seamless deployment of edge applications for partners like and in sectors including and healthcare. These alliances, often involving app developers, standardize interfaces for and accelerate market entry, with hyperscalers contributing while telcos provide assured . On the cost side, MEC reduces data transport expenses by processing information locally, minimizing backhaul traffic to centralized and associated fees. further drives operational expenditure (OPEX) savings—estimated at up to 38% in architectures—through automated and shared resources, lowering and energy demands compared to traditional deployments. These efficiencies are particularly pronounced in environments, where nodes consolidate functions previously siloed in proprietary equipment. Market drivers for MEC adoption center on monetization, where telcos differentiate offerings beyond commoditized by bundling edge compute with advanced slices. Enterprise private networks, increasingly deployed for secure and , rely on MEC to deliver tailored performance, supporting economic models like pay-per-use billing for compute cycles or API calls. This shift enables operators to capture higher margins from verticals such as and , where predictable latency translates to measurable productivity gains.

Deployment and Implementation

Deployment Models

Multi-access edge computing (MEC) deployment models vary based on the balance between latency requirements, resource availability, and operational complexity, typically categorized into centralized, distributed, and hybrid approaches. In the centralized model, MEC infrastructure is consolidated in regional data centers or telco edges, enabling efficient resource pooling and management for applications that can tolerate moderate latency, such as aggregated data processing in industrial settings. This approach leverages robust compute and storage at network aggregation points, often integrated with core network functions, to support high-capacity workloads while minimizing the number of deployment sites. Distributed models, by contrast, position MEC hosts directly at cell sites or far-edge locations, such as base stations, to achieve ultra-low latency for real-time applications like local inference on constrained devices, though this requires lightweight virtualization to handle limited resources at each site. Hybrid models combine elements of both, facilitating cloud-edge federation where centralized orchestration coordinates distributed processing, allowing seamless workload migration across tiers for dynamic scenarios like extended reality services. Infrastructure options for MEC deployments include on-premises telco for full control over dedicated facilities, colocation with hosts to share costs and connectivity in carrier- data centers, and extensions of services to leverage scalable, elastic resources at . On-premises setups deploy MEC platforms on operator-owned at edge locations, ensuring compliance and customization but demanding significant capital investment. involves partnering with hosts to place MEC servers in shared facilities, optimizing for interconnectivity with multiple networks while reducing deployment footprint. extensions, such as operator-integrated zones, enable -edge operations by extending centralized capabilities to proximity points, supporting bursty workloads without full on-site . Orchestration and management in MEC rely on frameworks like Management and Orchestration (MANO), which handle lifecycle management of MEC applications and hosts through components such as the MEC Orchestrator (MEO) and MEC Platform Manager (MEPM). MANO integrates with NFV environments to automate deployment, scaling, and fault recovery via standardized interfaces like Mm1 to Mm9, ensuring consistent operation across models. Automation is further enhanced by intent-based networking, where high-level service intents are translated into automated configurations, reducing manual intervention and enabling dynamic in multi-domain setups. Scalability in MEC deployments emphasizes phased rollouts, beginning with high-density areas to prioritize hotspots and validate before expanding to broader regions. This approach allows incremental integration, starting with basic connectivity and evolving to full , while incorporating measures such as resource sleep states and offloading to minimize power consumption in distributed sites. Energy-efficient designs optimize utilization and dynamic to balance and , particularly in resource-constrained far-edge deployments.

Real-World Case Studies

In 2019, partnered with (AWS) to launch Edge, integrating AWS Wavelength zones into Verizon's network to enable low-latency applications such as (AR) and (VR) experiences in stadiums. This deployment allowed developers to build interactive apps with single-digit latencies, reducing round-trip data travel and enhancing user immersion for live events, such as sports broadcasts where AR overlays provide statistics without . By embedding compute and storage at the network edge, the solution minimized transit delays that could exceed 100 milliseconds over public internet paths, supporting seamless AR/VR for thousands of attendees. In 2020, and collaborated on private networks using CBRS spectrum to advance Industry 4.0 applications, including multi-access edge computing (MEC) for smart factories that enable real-time control. 's on-premises MEC portfolio was expanded through this partnership to deliver low-latency edge processing, allowing industrial robots to perform precise, automated tasks with minimal delay, such as coordinating assembly lines or in environments. The initiative focused on reliable connectivity for devices and , reducing operational disruptions and improving efficiency in factory settings by processing data closer to the equipment. European trials in 2022 demonstrated MEC's role in automotive (V2X) communication through Deutsche Telekom's involvement in the 5G-MOBIX project, where standalone networks integrated with MobiledgeX MEC supported cooperative intelligent transport systems. In these tests along highways, MEC enabled low-latency data exchange between vehicles and roadside units, facilitating real-time hazard warnings and traffic optimization with low latencies, such as around 24 milliseconds for key platooning flows. The deployment connected roadside units to MEC cloudlets in , ensuring reliable V2X messaging for automated driving scenarios while maintaining network slicing for prioritized safety communications. By 2025, the Recommendation X.1648 provided updated guidelines on data security (as of April 2025), outlining frameworks for integrating MEC with networks to address threats like illegal in distributed systems. These guidelines emphasized secure architectures for MEC hosts, including platforms and applications. In November 2025, partnered with AWS to build high-capacity fiber routes using Verizon's AI Connect portfolio, accelerating AI applications with resilient, low-latency network connectivity at the edge. Across these deployments, key lessons learned highlight challenges in MEC interoperability, such as data format inconsistencies and resource orchestration across vendors, which were addressed through standardized open APIs to ensure seamless integration. Open APIs from and industry forums facilitated "write once, run everywhere" development, reducing deployment times and enabling cross-border service continuity in trials. This approach mitigated federation issues in multi-operator environments, promoting scalable MEC ecosystems while prioritizing security and low-latency performance.

Applications and Use Cases

Latency-Sensitive Applications

Multi-access edge computing (MEC) plays a pivotal role in enabling latency-sensitive applications by processing data at the network edge, thereby minimizing round-trip times and supporting essential for immersive, safety-critical, and operational systems. These applications leverage MEC's proximity to end-users and devices to achieve low latencies on the order of 1 ms, which are unattainable through centralized architectures. In (AR) and (VR) experiences, as well as gaming, MEC facilitates edge rendering to deliver immersive interactions without perceptible delays. For instance, in , MEC servers handle rendering and streaming to ensure responsive gameplay and prevent in users. This edge-based approach supports ultra-reliable low-latency communication (URLLC) in networks, enabling multiplayer AR/VR games where synchronized actions require consistent low-latency processing across distributed participants. For autonomous vehicles, MEC enhances (V2X) communications by processing sensor data at the edge to enable rapid collision avoidance. Edge nodes analyze incoming data from , , and cameras in , generating alerts or control signals within milliseconds to mitigate risks such as pedestrian or vehicle intersections. This localized computation reduces dependency on distant servers, allowing for proactive maneuvers like emergency braking based on shared V2X messages, thereby improving in dynamic environments. In industrial (IIoT), MEC supports real-time control and through edge , optimizing processes by analyzing on-site. This allows for instantaneous responses in robotic lines or conveyor systems, where delays could lead to production halts. Video analytics for benefits from MEC's live edge processing, which performs and event recognition directly on camera feeds to enable immediate alerts. By extracting and analyzing features at —such as motion tracking or facial recognition—MEC substantially reduces upload volumes, with techniques like feature forwarding achieving over 99% savings compared to full video transmission. This approach ensures timely threat identification in security scenarios, such as detecting intrusions in public spaces, while conserving network resources. MEC's alignment with 5G URLLC provides the foundational performance metrics for these applications, delivering end-to-end latencies as low as 1 ms and reliability up to 99.999%, which is critical for mission-critical operations. This support stems from MEC's integration with standards, ensuring robust handling of intermittent connectivity and high-priority traffic in edge environments. As of 2025, recent advancements include AI-enhanced edge processing for more accurate real-time analytics in these applications.

Industry-Specific Implementations

In healthcare, multi-access edge computing (MEC) facilitates edge processing for remote and by enabling low-latency at the network . This three-tier involves edge devices for initial and preprocessing from sensors physiological parameters such as and , with MEC servers handling decision-making to reduce to the . For remote , MEC integrates with networks to support tactile internet applications, allowing surgeons to receive haptic feedback and streams with low latency under 20 ms, as demonstrated in frameworks for robotic-assisted procedures. benefits from MEC's ability to process locally, enabling immediate alerts for anomalies in wearable device outputs, thereby improving response times in smart hospital environments. In , MEC tailors personalized in-store experiences through edge-based recommendation engines that leverage . By deploying MEC appliances near store locations, retailers can location-based and behavioral data from mobile apps or in-store sensors to deliver context-aware product suggestions, reducing for and inventory updates. For instance, integration with (AR) applications allows customers to visualize products via low-latency edge rendering, enhancing engagement while caching content locally to cut network traffic by up to 59%. Verizon's Edge platform with partners like AiFi exemplifies this by using MEC for instant shopper tracking and machine learning-driven recommendations, optimizing inventory and personalizing offers based on activity. In transportation, MEC supports traffic management via edge-optimized , processing data from vehicle-to-infrastructure communications in . MEC-enabled vehicular networks aggregate inputs from cameras, , and roadside units to fuse sensor data at , enabling predictive traffic flow adjustments and collision avoidance with reduced compared to centralized processing. This approach alleviates bandwidth constraints on backhaul networks while providing localized services like updates, as seen in Internet of Vehicles (IoV) applications where MEC servers at base stations handle fusion for autonomous driving scenarios. In the energy sector, MEC aids optimization for renewable integration by performing localized analytics on distributed energy resources. Edge nodes process data from inverters and turbines in proximity, enabling forecasting and load balancing to accommodate variable renewable outputs without relying on distant cloud infrastructure. For example, models like TGNet utilize MEC for energy prediction on datasets such as GEFCom2014, supporting stability through immediate and demand-response adjustments. MEC implementations incorporate sector-specific APIs to address customization needs, particularly in healthcare for compliance with regulations. ETSI-defined Health Data APIs allow secure access to patient records while enforcing localization rules, ensuring sensitive information remains within jurisdictional boundaries during edge processing. enforcement APIs further integrate with MEC frameworks to apply granular controls, such as and access logging, aligning with standards like HIPAA for and applications.

Standards and Ecosystem

ETSI and Core Standards

The (ETSI) established the Multi-access Edge Computing Industry Specification Group (MEC ISG) in December 2014 to standardize solutions that enable low-latency services across multi-access networks. By 2025, the MEC ISG has produced over 50 specifications, encompassing reference architectures, service enablers, and deployment guidelines to foster an open, multi-vendor . Core standards form the backbone of MEC implementations, with GS MEC 003 defining the overall and that outlines system components, interfaces, and interactions for deploying MEC platforms at the network edge. GS MEC 012 establishes the Information , allowing MEC applications to access contextual data from the to enhance application performance and resource optimization. Similarly, GR MEC 022 details use cases and requirements for (V2X) communications, specifying how MEC supports latency-critical automotive applications through edge-based processing of sensor data and traffic information. In 2025, significant updates include GR MEC 036 V4.1.1, which extends MEC capabilities to resource-constrained devices, such as endpoints and fixed or mobile terminals, by addressing deployment challenges in limited-power environments. The ISG also advanced API principles for multi-tenancy, providing guidelines for secure resource partitioning and isolation to enable multiple operators and service providers to share MEC infrastructure without compromising performance or privacy. To promote , ETSI offers compliance testing frameworks that validate MEC systems against specifications, ensuring seamless integration across diverse vendors and access technologies.

Complementary Standards and Interoperability

The has significantly contributed to multi-access edge computing (MEC) through its specification releases, enabling seamless integration with networks. In Releases 15 and 16, 3GPP introduced foundational support for , including mechanisms for User Plane Function (UPF) relocation to optimize by anchoring traffic closer to the edge. These releases allow for dynamic UPF insertion and relocation based on location and , facilitating efficient in MEC environments. Building on this, Release 17 provides enhancements for MEC, including architecture for native application operation and interchange over MEC networks, with specific improvements for non-3GPP access to support diverse connectivity scenarios like integration. Beyond , other standards bodies have developed complementary frameworks to advance MEC adoption. The Open Edge Computing Initiative has outlined reference architectures that promote interoperable edge solutions, emphasizing modular components for deployment across varied environments. Similarly, the Recommendation X.1648, approved in April 2025, provides guidelines for , including a reference architecture integrated with networks and focused on frameworks to address deployment challenges. Interoperability efforts in MEC are bolstered by platforms and standardization initiatives. The Open Network Automation Platform (ONAP) supports MEC by enabling automated lifecycle management of edge services, including provisioning and scaling across distributed environments. The GSMA's Open Gateway initiative drives harmonization, standardizing interfaces like Edge Site Selection to ensure consistent access to MEC capabilities across operators. These open mitigate challenges such as by promoting portable, vendor-agnostic integrations that reduce dependency on proprietary systems. In the MEC ecosystem, proofs of concept have demonstrated practical , particularly in cross-operator scenarios. At (MWC) events, trials such as the GSMA's Telco Edge Cloud (TEC) pre-commercial initiative have showcased homogeneous availability and services across regions and operators, enabling seamless edge application delivery.

Challenges and Future Directions

Technical and Security Challenges

Multi-access edge computing (MEC) faces significant technical challenges due to the inherent limitations of edge nodes compared to centralized infrastructures. Edge servers typically operate with constrained computational resources, including limited CPU, , and , which complicates efficient task offloading and for multiple users sharing the same coverage area. This scarcity often leads to suboptimal performance in high-demand scenarios, such as processing, where balancing load across heterogeneous edge devices becomes critical. Additionally, poses hurdles during user handovers between edge nodes, requiring seamless service migration to maintain low latency without disrupting ongoing computations or connections. Security challenges in MEC are amplified by the distributed nature of edge deployments, exposing nodes to physical risks at remote locations like cell sites, which can facilitate tampering or unauthorized intrusions. Data privacy concerns arise from distributed processing, where sensitive information is handled closer to the user, increasing the potential for breaches if or controls fail across the network. Common threats include distributed denial-of-service (DDoS) attacks targeting , which can overwhelm limited resources and degrade service availability, particularly in multi-tenant environments. To mitigate these issues, zero-trust models have been proposed, emphasizing continuous of entities and least-privilege to reduce the attack surface in MEC architectures. specifications, such as GR MEC 041, outline paradigms like mutual-TLS for secure communications and cryptographic attestation for verifying application integrity, addressing vulnerabilities like stolen tokens and compromised apps. In AI-driven edge applications, challenges from model poisoning—where adversaries inject malicious data to corrupt processes—require robust detection mechanisms, such as adaptive trust management to isolate tainted contributions. Interoperability gaps further complicate MEC adoption, as vendor-specific implementations of and protocols lead to fragmentation, hindering seamless integration across multi-vendor ecosystems. standards aim to bridge these gaps by defining open interfaces for edge orchestration, but inconsistent adherence persists, affecting service continuity in federated deployments.

Emerging Trends and Market Outlook

One prominent emerging trend in multi-access edge computing (MEC) is the integration of (AI) and (ML) directly at the edge, enabling decision-making and reduced latency for distributed systems. , a decentralized ML approach, allows edge devices to collaboratively train models without sharing raw data, enhancing privacy and efficiency in applications like autonomous vehicles and smart cities. This integration is particularly advancing through AI-native architectures that process data closer to the source, minimizing demands on central clouds. Preparations for networks are positioning MEC to support sub-1ms latency requirements, facilitating ultra-reliable low-latency communications (URLLC) for immersive experiences and massive deployments. visions emphasize intelligence to handle terabit-per-second data rates and holographic communications, with MEC serving as a core enabler for in dynamic environments. Additionally, MEC is evolving to underpin applications, where AI processes and rendering to deliver seamless virtual interactions without dependency. The market outlook for MEC indicates robust growth, with projections estimating expansion from USD 5.3 billion in 2025 to USD 124.6 billion by 2035, reflecting a (CAGR) of 37.2%. This surge is driven by increasing demand for low-latency processing in and beyond ecosystems. Private MEC deployments are gaining traction among enterprises, offering customized edge resources for sectors like and healthcare, with the segment forecasted to grow at a CAGR of 23.8% through 2033. Key innovations include quantum-safe encryption protocols tailored for MEC, leveraging standards like ETSI MEC and (QKD) to protect edge computations against quantum threats in distributed networks. Sustainable edge computing initiatives are also emerging, focusing on green practices such as energy-efficient in 6G-enabled MEC to reduce carbon footprints in renewable energy systems. Furthermore, convergence with Open RAN architectures is enhancing MEC flexibility, allowing disaggregated radio access networks to integrate edge processing for improved scalability and security via . In November 2025, released the first Phase 4 specifications and a , focusing on developer-friendly for vertical industries, enhancements to edge platform application enablement, and alignment with open-source projects to improve and support preparations. Research directions post-2025 emphasize ITU-led collaborations to develop global frameworks for MEC within IMT-2030 (), focusing on standardized interfaces for edge orchestration and across international networks. These efforts aim to align MEC with broader capabilities, including AI-driven and sustainable connectivity.

References

  1. [1]
    Multi-access Edge Computing - Standards for MEC - ETSI
    Multi-access Edge Computing (MEC) offers application developers and content providers cloud-computing capabilities and an IT service environment at the edge ...Introduction · Our Role & Activities
  2. [2]
    [PDF] Multi-access Edge Computing - ETSI
    Multi-access Edge Computing (MEC) offers application developers and content providers cloud-computing capabilities and an IT service environment at the edge ...
  3. [3]
    Edge Computing - 3GPP
    Jun 5, 2023 · Edge computing, simply put, is where core network and cloud computing capabilities are moved to the edge of the network closer to the customers.
  4. [4]
    [PDF] ETSI GS MEC 003 V3.1.1 (2022-03)
    Multi-access Edge Computing enables the implementation of MEC applications as software-only entities that run on top of a Virtualisation infrastructure, which ...
  5. [5]
    [PDF] Enabling Multi-access Edge Computing in Internet-of- Things - ETSI
    Multi-access Edge Computing (MEC) provides compute sites at cell towers, while oneM2M is an IoT standard platform for M2M communications.
  6. [6]
    https://www.etsi.org/images/files/ETSIWhitePapers/ETSI-WP59-Enabling-Multi-access-Edge-Computing-in-iot.pdf
  7. [7]
    Multi-access edge computing: open issues, challenges and future ...
    Dec 21, 2017 · Since 2017, the ETSI MEC industry group has renamed “Mobile Edge Computing” to “Multi-Access Edge Computing” to better reflect the growing ...
  8. [8]
    What Is Edge Computing? - NVIDIA Blog
    Oct 22, 2019 · In fact, edge computing can be traced back to the 1990s, when content delivery networks (CDNs) acted as distributed data centers. At the ...
  9. [9]
    Edge Computing is the Evolution of CDNs - Azion
    Evolution of the CDN. CDNs were first introduced in the late 1990s to alleviate bottlenecks from increased Internet traffic and improve reliability to mission- ...
  10. [10]
    New ETSI Mobile-Edge Computing Industry Specification Group ...
    The first meeting of ETSI's newly created Mobile-Edge Computing (MEC) Industry Specification Group (ISG) took place on 2-4 December 2014, hosted by Nokia ...Missing: date | Show results with:date
  11. [11]
    ETSI Multi-access Edge Computing starts second phase and renews ...
    Mar 28, 2017 · ETSI's Mobile Edge Computing Industry Specification Group has been renamed to Multi-access Edge Computing to embrace the challenges in the ...
  12. [12]
    [PDF] MEC in 5G networks - June 2018 - ETSI
    In addition, 3GPP 5G system specifications define the enablers for edge computing, allowing a MEC system and a 5G system to collaboratively interact in traffic ...
  13. [13]
    [PDF] ETSI GR MEC 036 V4.1.1 (2025-08)
    This document is about Multi-access Edge Computing (MEC), specifically MEC in resource constrained terminals, fixed or mobile.
  14. [14]
    [PDF] MEC Deployments in 4G and Evolution Towards 5G - ETSI
    This way, MEC can be not only a technology ready for 4G, but also a driver to motivate 5G adoption, as it can allow operators to retain the investment made ...
  15. [15]
    3GPP enables MEC over a 5G core
    Jul 4, 2018 · The new “MEC in 5G networks” white paper focuses primarily on exploring these edge computing enablers from 3GPP and on how the 3GPP ecosystem ...
  16. [16]
    [PDF] ETSI GS MEC 003 V3.2.1 (2024-04)
    Multi-access Edge Computing enables the implementation of MEC applications as software-only entities that run on top of a Virtualisation infrastructure, which ...
  17. [17]
    [PDF] MEC support towards Edge Native Design - ETSI
    Edge/MEC clusters are characterized by some distinctive features: much larger scale than cloud-based clusters, geographical dispersal, much variation in ...
  18. [18]
    Handover decision with multi-access edge computing in 6G networks
    MEC servers can now be deployed with RAN elements, including base stations for fourth generation (4G), 5G and 6G networks, Wi-Fi access points and fixed links.
  19. [19]
    3GPP Capability Exposure Frameworks and APIs
    Feb 4, 2025 · This article summarizes the work done on protocol and interface aspects for the 3GPP Capability Exposure interfaces, bridging 3rd party entities ...Missing: N6 MP2
  20. [20]
    Multi-Access Edge Computing Handover Strategies, Management, and Challenges: A Review
    ### Summary of MEC Handover Strategies for Multi-Access Networks
  21. [21]
    Integration of Network Slicing and Machine Learning into Edge ...
    They also proved that the edge resources are very critical and crucial for the necessary computing and communication resources of URLLC and mMTC services.
  22. [22]
    The journey to cloud as a continuum: Opportunities, challenges, and ...
    Edge servers, cloud platforms, and end devices are hierarchically distributed over the network, forming the edge-to-cloud continuum. In this model, edge devices ...
  23. [23]
    What is round-trip time (RTT) and how to reduce it? - Gcore
    Dec 8, 2022 · Round-trip time (RTT) is the time it takes for the server to receive a data packet, process it, and send the client an acknowledgement that the request has ...How To Calculate Rtt Using... · Normal Rtt Values · How To Reduce Rtt
  24. [24]
    [PDF] ETSI GR MEC 043 V4.1.1 (2025-08)
    The TOD scenarios can benefit significantly from Multi-access Edge Computing (MEC) to achieve low latency and high reliability communication. TOD becomes ...
  25. [25]
    Hybrid CDN Architecture Integrating Edge Caching, MEC Offloading ...
    The hybrid system thus reduces backhaul content traffic by an additional 8 ... reduction of roughly 70% compared to traditional CDNs. The Q-learning ...
  26. [26]
    [PDF] A Survey on Architecture and Computation Offloading - arXiv
    The authors demonstrate on a real MEC testbed that the reduction of latency up to 88% and energy consumption of the UE up to 93% can be accomplished by the ...
  27. [27]
    [PDF] ETSI TS 122 261 V17.12.0 (2024-01)
    connection density (e.g. 1 million connections per square kilometre) of multiple UEs. The 5G system shall support a timely, efficient, and/or reliable ...
  28. [28]
    https://www.factmr.com/report/multi-access-edge-computing-market
  29. [29]
    Multi-Access Edge Computing Market Analysis - Fact.MR
    The global multi-access edge computing market is projected to grow from USD 8.5 billion in 2025 to USD 48.2 billion by 2035, advancing at a CAGR of 18.9%.
  30. [30]
    [PDF] edge computing: The telco business models - Intel® Network Builders
    Business model 2: Edge IaaS/PaaS/NaaS ... These involve both business models in which the telco acts as an enabler of MEC services for others,.Missing: EaaS monetization
  31. [31]
    What it will take for telcos to unlock value from network APIs
    Feb 21, 2024 · Over the next five to seven years, we estimate the network API market could unlock around $100 billion to $300 billion in connectivity- and edge ...Missing: MEC EaaS
  32. [32]
    Telco edge computing: How to partner with hyperscalers
    Aug 19, 2020 · Telcos and hyperscalers need each other to deliver edge computing. How can operators partner with hyperscalers to strenghten their role ...
  33. [33]
    AI infrastructure: A new growth avenue for telco operators - McKinsey
    Feb 28, 2025 · Verizon's MEC (mobile-edge computing) partnership with AWS uses a hybrid approach to enable enterprises to obtain compute power close to the ...
  34. [34]
    Mobile Edge Computing (MEC) | Concepts - Couchbase
    Reduced costs: By processing data at the edge, mobile edge computing reduces the need to transmit large amounts of data over long distances, lowering the costs ...
  35. [35]
    [PDF] economic benefits of the vmware telco cloud automation and ...
    Our economic model shows an operations expense. (OpEx) savings of 38% and network total cost of ownership (TCO) savings of 29% for a horizontal architecture ...
  36. [36]
    Multi-Access Edge Computing - Devopedia
    Mar 17, 2021 · MEC helps network operators reduce their CAPEX by purchasing general-purpose equipment rather than specialized telecom equipment. By reducing ...<|separator|>
  37. [37]
    5G monetization with differentiated connectivity - Ericsson
    Jul 17, 2025 · Monetizing the capabilities of 5G standalone (5G SA) is both a challenge and an opportunity for communications service providers to break free from ARPU ...Missing: per- | Show results with:per-
  38. [38]
    5G Monetization: Strategies for Communication Service Providers
    Sep 12, 2024 · Flexible pricing models: Implementing tiered pricing structures and pay-per-use models to accommodate varying customer needs and usage patterns.
  39. [39]
    5G Private Network Market Size, Growth, Share & Industry Report ...
    Jun 28, 2025 · The 5G Private Network Market is expected to reach USD 3.06 billion in 2025 and grow at a CAGR of 43.60% to reach USD 18.68 billion by 2030.
  40. [40]
    [PDF] AWS and Verizon team up to deliver 5G edge cloud computing
    Dec 3, 2019 · By utilizing AWS Wavelength and Verizon 5G Edge, developers will be able to deliver a wide range of transformative, latency-sensitive use cases ...
  41. [41]
    [PDF] 5G Edge Partner Program YBVR - Verizon
    Sep 8, 2021 · This shortens the roundtrip data needs to travel, reducing lag time, or latency. By moving AWS compute and storage services to the edge of ...
  42. [42]
    Verizon 5G Edge with AWS Wavelength
    Low latency. Avoid unnecessary internet hops and transit delays of 100+ milliseconds when building apps with super-fast response requirements—such as games, ...
  43. [43]
    AT&T and Nokia Drive Industry 4.0 with Private Networks Enabled ...
    Oct 19, 2020 · AT&T's on-premises edge portfolio, which already includes 5G-capable AT&T Multi-Access Edge Computing (MEC), is expanding to offer these ...Missing: factories | Show results with:factories
  44. [44]
    [PDF] How 5G is Transforming the Manufacturing Landscape
    Reliable and resilient characteristics of the 5G network will enable driverless vehicles, the ability to control mobile robotics in real time and wider ...
  45. [45]
    [PDF] DE Trial Results - 5G-MOBIX
    Jun 22, 2022 · • 5G RSU and MobiledgeX MEC are connected within Deutsche Telekom 5G NSA network, MEC cloudlet location is Berlin. • TUB MEC is in the same ...
  46. [46]
    https://www.nokia.com/newsroom/att-and-nokia-drive-industry-40-with-private-networks--enabled-by-cbrs/
  47. [47]
    Guideline on edge computing data security - ITU
    This document outlines a framework and reference architecture for edge computing integrated with 5G networks, emphasizing data security challenges and ...Missing: guidelines MEC Asian telco pilots collaborative systems
  48. [48]
    MEC APIs: opportunities and challenges - STL Partners
    MEC APIs are a type of APIs that allow app developers to access the benefits of multi-access edge computing and integrate them into applications and services.Missing: lessons | Show results with:lessons
  49. [49]
    [PDF] MEC application developer guidelines for universal access to ... - ETSI
    Model watermarking can embed digital watermarks in AI models to detect unauthorized modifications. Secure API integration should utilize CAMARA APIs with DPoP ...Missing: 2020s | Show results with:2020s
  50. [50]
    Mobility-aware Multi-Access Edge Computing for Multiplayer ...
    Jan 16, 2023 · Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency ...
  51. [51]
    Collision avoidance in 5G using MEC and NFV: The vulnerable road ...
    May 8, 2020 · The second contribution of this paper is a novel V2X service and algorithm, namely VRU-safe, that operates on top of the proposed architecture.
  52. [52]
    An Inter-operable and Multi-protocol V2X Collision Avoidance ...
    In this paper, we propose a Collision Avoidance service based on Vanetza, an open-source ETSI ITS protocol stack, and demonstrate its operation.
  53. [53]
    [PDF] Bandwidth-efficient Live Video Analytics for Drones via Edge ...
    In this paper, we show how edge computing can greatly reduce the per-drone bandwidth demand for video analytics, without compromising the timeliness or accuracy ...
  54. [54]
    [PDF] Edge-Based Video Analytics: A Survey - arXiv
    Mar 25, 2023 · Edge-based video analytics processes video data using edge servers to overcome high bandwidth and latency issues of cloud computing.
  55. [55]
    What are eMBB, URLLC and mMTC? - 5G technology - Verizon
    Mar 30, 2023 · Guidelines for 5G URLLC address these needs with reliability of up to 99.999% and latency in low single-digit milliseconds. As described by ...
  56. [56]
    [PDF] Ultra-Reliable Low-Latency Communication - 5G Americas
    Market Drivers & Use Cases for 5G URLLC ... 5G systems are driven by the use of software and IT virtualization technology toward the development of.
  57. [57]
    Edge Intelligence and Internet of Things in Healthcare: A Survey
    ### Summary of Edge-Based IoT Frameworks for Remote Patient Monitoring in Healthcare
  58. [58]
    [PDF] 5G-Americas-EDGE-White-Paper-FINAL.pdf
    Multi-Access Edge Computing (MEC) platform or as a programmable Application ... • Remote surgery and examination. The Tactile Internet has been ...
  59. [59]
    [PDF] The Business Case for MEC in Retail - Intel
    1 Specifically, this means moving processing closer to the edge of network with standards such as Multi-access Edge Computing (MEC) developed by ETSI ISG MEC.2, ...
  60. [60]
    [PDF] Attract and retain customers with contactless, autonomous shopping .
    Multi-access edge computing (MEC) provides both an IT service environment and cloud-computing capabilities to help enable the real-time enterprise. Shopping ...
  61. [61]
    A Survey of Multi-Access Edge Computing and Vehicular Networking
    ### Summary of MEC Applications in Vehicular Networking for Smart City Traffic Management and Sensor Fusion
  62. [62]
    Leveraging Edge Intelligence for Solar Energy Management in Smart Grids
    **Summary of Edge Intelligence and MEC for Solar Energy Management in Smart Grids:**
  63. [63]
    Our group on Multi-access Edge Computing (MEC) - ETSI
    Industry Specification Group (ISG) on Multi-access Edge Computing (MEC). We create a standardized, open environment allowing the efficient and seamless ...
  64. [64]
    https://www.verizon.com/business/5g-edge-portal/content/dam/mec/partners/documents/5G-Edge-Partner-Program-Aifi-SB.pdf
  65. [65]
    [PDF] ETSI GS MEC 009 V3.1.1 (2021-06)
    ETSI GS MEC 012: "Multi-access Edge Computing (MEC); Radio Network Information API". [i.8]. IANA: "Hypertext Transfer Protocol (HTTP) Status Code Registry ...
  66. [66]
    [PDF] ETSI GS MEC 021 V2.2.1 (2022-02)
    To complement the definitions for each method and resource defined in the interface clauses of the present document,. ETSI MEC ISG is providing for the ...
  67. [67]
    [PDF] ETSI MEC Overview - Standardisation update on Multi-access Edge ...
    1) Open standard → allowing multiple implementations and ensuring interoperability. 2) MEC exploiting ETSI NFV framework and definitions → enabling MEC in ...Missing: 2020s | Show results with:2020s
  68. [68]
    [PDF] ETSI GR MEC-DEC 025 V2.1.1 (2019-06)
    Jun 1, 2019 · The MEC interoperability testing methodology follows the NFV interoperability testing methodology (defined in ETSI. GS NFV-TST 002 [i.2]) where ...
  69. [69]
    [PDF] The 5G Evolution:3GPP Releases 16-17
    Jan 16, 2020 · Release-15 NR supports basic handover based on UE measurement reporting. • The source gNB triggers handover by sending a handover request to ...Missing: relocation | Show results with:relocation
  70. [70]
    [PDF] Strategies for UPF Placement in 5G and Beyond Networks
    MEC brings computing, storage, and networking capabilities to the network edge, reducing network response time and backhaul traffic as network functions and.
  71. [71]
    How does 3GPP R17 enable MEC applications?
    Oct 4, 2022 · 3GPP R17 defines an architecture for native application operation and data interchange over Multi-Access Edge Compute (MEC) networks.
  72. [72]
    [PDF] 5G Releases 16 and 17 in 3GPP - Nokia
    Multi-access edge computing (MEC) enhancements: Optimizing edge networking support (mobility, discovery and provisioning). • Extension to 71 GHz: Covered ...
  73. [73]
    Industry initiatives across edge computing - ScienceDirect.com
    This chapter goes through various initiatives across the world that have major traction in terms of collaboration, collateral produced and industry impact.
  74. [74]
    X.1648 : Guideline on edge computing data security - ITU
    X.1648 : Guideline on edge computing data security ; Recommendation X.1648 (04/25). Approved in 2025-04-17. Status : In force. Table of Contents.
  75. [75]
    [PDF] ONAP Orchestration for Multi-Access Edge Cloud
    Globally, IP traffic (consumer & business, mobile. & fixed) will reach 278.1 Exabytes per month in. 2021, up from 96.1 Exabytes per month in 2016.Missing: computing | Show results with:computing
  76. [76]
    GSMA | Mobile Industry Deploys Open Network APIs and Prepares ...
    Feb 27, 2023 · Musicians from around the world will jam over 5G, using Mobile Edge Compute (MEC) and the GSMA Open Gateway's Edge Site Selection API. See ...Missing: harmonization | Show results with:harmonization
  77. [77]
    Navigating the Future of Multi-Access Edge Computing (MEC)
    Apr 26, 2024 · Adopting open standards – To avoid vendor lock-in and ensure flexibility, businesses should utilize open standards for developing edge ...
  78. [78]
    TEC Pre-Commercial Trial - Bloom City Trial - GSMA Foundry
    Objective of the trial. Prove the ability to achieve cross-region, cross-operator availability of homogeneous enabler APIs and services powered by telco edge ...
  79. [79]
    Mobility-Aware Offloading Decision for Multi-Access Edge ...
    In 2017, the European Telecommunications Standards Institute (ETSI) changed the name 'mobile edge computing' to 'multi-access edge computing' for addressing ...
  80. [80]
    Computation Offloading in Resource-Constrained Multi-Access ...
    Mar 29, 2024 · In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this ...
  81. [81]
    A survey of mobility-aware Multi-access Edge Computing
    Mar 1, 2023 · In this paper, we focus on four functional components (task/service offloading, resource allocation, content/task caching, and service/task migration) of MEC.
  82. [82]
    [PDF] MEC security; Status of standards support and future evolutions - ETSI
    • Implementing Zero-Trust strategies - Zero Trust is a paradigm shift towards security and eliminates ... Security; Isolation and trust domain specification - ...Missing: 004 | Show results with:004
  83. [83]
    Privacy and security vulnerabilities in edge intelligence: An analysis ...
    This paper reviews Edge Intelligence with a focus on privacy and security issues, identifying critical challenges and vulnerabilities in edge and cloud ...
  84. [84]
    A Survey of DDoS Attack and Defense Technologies in Multiaccess ...
    Nov 4, 2024 · MEC faces numerous security challenges, with Distributed Denial of Service (DDoS) attacks being one of the primary threats. On one hand, the ...
  85. [85]
    [PDF] ETSI GR MEC 041 V3.1.1 (2024-03)
    Jan 6, 2024 · The present document outlines security topics and paradigms that apply to MEC deployments across the realms of application/platform security and ...Missing: 004 | Show results with:004
  86. [86]
    [PDF] THE 2025 EDGE AI TECHNOLOGY REPORT | Ceva's IP
    5 Emerging Trends in. Edge AI. 1. Federated Learning: Decentralized Intelligence at the Edge. Federated learning (FL) is evolving beyond privacy preservation ...
  87. [87]
    A Comprehensive Survey on Emerging AI Technologies for 6G ...
    This paper presents the use of AI in 6G communication networks, technologies, techniques, trends, and future research directions.
  88. [88]
    Overview of AI and Communication for 6G Network - arXiv
    This paper presents a comprehensive overview of AI and communication for 6G networks, emphasizing their foundational principles, inherent challenges, and ...
  89. [89]
    6G-Enabled Edge AI for Metaverse: Challenges, Methods, and ...
    Oct 24, 2025 · This survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence (AI) and Metaverse.
  90. [90]
    Multi-Access Edge Computing Market | Global Market Analysis Report
    Aug 21, 2025 · The multi-access edge computing market is projected to grow from USD 5.3 billion in 2025 to USD 124.6 billion by 2035, at a CAGR of 37.2%.
  91. [91]
    Private MEC Market Research Report 2033 - Dataintelo
    According to our latest research, the market is projected to expand at a CAGR of 23.8% from 2025 to 2033, reaching an estimated USD 16.8 billion by the end of ...
  92. [92]
    Quantum-safe Edge Applications: How to Secure Computation in ...
    May 27, 2024 · In this paper, we propose a technical solution to achieve this goal, building on standard specifications, namely ETSI MEC and ETSI QKD, and discussing the gaps ...
  93. [93]
    Review on 6G Communication Network Technology for Sustainable ...
    Oct 30, 2025 · Enabling 6G wireless connections and blockchain-based encrypted information connectivity enhances the safety of the RES-based electricity system ...
  94. [94]
    Open RAN for 6G Networks: Architecture, Use Cases and Open Issues
    Quantum Cryptography for Enhanced Security: As O-RAN introduces more open interfaces, security risks increase. Quantum. Key Distribution (QKD) offers ...
  95. [95]
    Shaping the 6G Revolution: How the Americas Can Lead the Charge
    Sep 17, 2024 · At 5G Americas, we're exploring the implications of the IMT-2030 (6G) framework in our latest white paper, “ITU's IMT-2030 Vision: Navigating ...
  96. [96]
    The ITU Vision and Framework for 6G: Scenarios, Capabilities and ...
    Jan 30, 2025 · This paper describes the progresses moving towards 6G, which is officially termed as “international mobile telecommunications (IMT) for 2030 and beyond”Missing: MEC | Show results with:MEC