The evolution toward sixth-generation (6G) networks necessitates integrated resource management solutions to address the interdependencies between network segments, such as Radio Access Network (RAN) and Edge Cloud (EC) infrastructures. Unified management of network and compute fabrics is crucial for achieving seamless service delivery, end-to-end power efficiency, and delay guarantees, while resiliency becomes a key enabler for adapting to various application demands and diverse network segment conditions. In this context, this paper proposes a unified framework for dependable wireless EC networks that jointly addresses the problems of RAN selection and Service Function Chain (SFC) embedding to minimize the total power consumption across network segments under end-to-end delay SFC deployment constraints. The framework iteratively solves these problems, considering the interdependencies between RAN ingress points and the EC network resource constraints. To deal with the high dimensionality of the considered parameters and achieve timely and scalable decision-making, a coalition formation game optimizes RAN selection, while a delay-aware heuristic approach undertakes the power-efficient embedding of multiple SFCs within the EC network. Simulation results demonstrate the framework’s efficiency in reducing power consumption compared to segment-specific approaches, highlighting the importance of cross-segment dependencies. Also, the adaptability of the proposed unified modeling and the framework’s scalability are demonstrated, ensuring resilient performance under varying network parameter settings.

Approximate and delayed computing have emerged as promising paradigms to offer flexibility in computational accuracy and strategically differentiate tasks between edge and cloud execution to enhance resource utilization. However, these approaches introduce tradeoffs, potentially compromising accuracy on one hand and increasing latency on the other. In this paper, we explore the integration of approximate and delayed computing paradigms within the edge-cloud computing continuum. Users can either offload tasks for approximate computing at the edge or opt for exact but potentially delayed computing at the cloud. In this context, the joint problem of computation task offloading and data compression is formulated and solved as a non-cooperative Game in Satisfaction Form. Each user autonomously determines the amount of task to offload for either computing option and the percentage of data compression for approximate computing, aiming to achieve an acceptable accuracy-latency tradeoff. The formulated game admits a Satisfaction Equilibrium (SE) point, which is concluded using a Reinforcement Learning (RL)-based algorithm. Simulation results demonstrate the performance of the proposed task offloading framework in the achieved accuracylatency tradeoff compared against different offloading strategies.

Collaborative Machine Learning (ML) paradigms, such as Federated Learning (FL), suffer from unequal client contributions and adversarial behavior, where clients deliberately degrade global model accuracy via outdated or poisoned updates. In this paper, we address fair client collaboration and adversarial behavior detection and mitigation using a combined reputation-aware incentive and robust aggregation approach. First, the long-term client reputation across FL epochs is estimated using a variant of the Shapley value, which offers polynomial complexity, contrariwise to the latter. Client reputation is then used to weight model updates during global model generation, effectively mitigating the impact of model poisoning and replay attacks. Moreover, it is used to determine informed monetary rewards for the clients on the server side, which, in turn, guide their efforts in the number of model iterations employed during local training. The interplay between server and clients in their reward and effort decisions is modeled as a Stackelberg game, which concludes fair client participation. Numerical results via modeling and simulation validate the effectiveness of both mechanisms against state-of-the-art and baseline alternatives.

The evolution toward AI-native networks necessitates seamless Artificial Intelligence (AI) integration, which demands privacy-preserving data management, scalable model deployment, and optimized resource allocation. To address these challenges, we introduce key AI enablers as Data Operations (DataOps), Machine Learning Operations (MLOps), and AI as a Service (AIaaS). DataOps enables secure, privacy-preserving data collection through techniques such as Differential Privacy (DP) and secure aggregation, facilitating AI-driven insights without exposing sensitive user data. MLOps enhances the AI life cycle by leveraging distributed learning paradigms, including horizontal and Vertical Federated Learning (VFL), supported by Split Learning (SL). AIaaS extends these capabilities by exposing AI models and services through standardized APIs, enabling on-demand training, inference, and automation. By integrating AIaaS with DataOps and MLOps, networks can achieve greater intelligence, adaptability, and compliance with privacy and interoperability standards. This paper introduces a coherent architectural framework and operational strategy for embedding AI-driven intelligence into 6G networks, with a focus on key innovations in data governance, AI model coordination, and service exposure.

The efficient operation of the unified Integrated Sensing and Communication (ISAC) – Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices’ externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices’ externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia’s operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability.

Multi-provider multi-user multi-access edge computing provides a recent market-driven networking paradigm facilitating the user data offloading process. In this paper we introduce the AGORA framework, which employs a sophisticated multi-leader multi-follower Stackelberg game that jointly optimizes the data offloading, computing resource allocation, and computing resource pricing, all facilitated through a non-cooperative game-theoretic approach. In order to support the aforementioned modeling and approach, a novel utility function that quantifies the users satisfaction, factoring in the computing service cost, and an innovative profit function for the MEC providers is introduced, emphasizing the market penetration and the computing service provision costs. Numerical results, obtained via modeling and simulation, demonstrate AGORA’s remarkable adaptability, accommodating homogeneous and heterogeneous user computing demands, while simultaneously outperforming proportional fairness resource allocation approaches, and significantly enhancing the MEC providers’ profitability and the users’ satisfaction from the edge computing services.

Green operation is of paramount importance in 6G and zero-energy wireless nodes can support it. To fully exploit the potentials of zero-energy wireless multi-hop networks, it is essential to jointly optimize their source data rates, routing and transmission power decisions, which is a significantly complex problem, in particular under the uncertainties introduced by the wireless channel states and the energy harvesting processes on the nodes. In this paper, we tackle the aforementioned problem under the assumption that wireless nodes operate only based on their batteries that charge solely via ambient energy harvesting. A plan for the network operation for a future time horizon is computed using scenario-based optimization techniques to account for stochasticities. The derived problem formulation is non-convex and is solved via a novel heuristic method that iteratively solves appropriately parameterized convex approximations of the original problem. At convergence, the obtained solution is feasible to the original non-convex problem. Numerical results illustrate the effectiveness of the proposed solution compared to the standard non-convex solver Ipopt and showcase the behavior of the network under heterogeneous scenarios.

Rate-Splitting Multiple Access (RSMA) has been recently recognized as a more general multiple access technique that overcomes the limiting factors of its predecessors related to the signal decoding complexity and interference management tradeoff. In this paper, we investigate the application of the RSMA technique to facilitate the users’ concurrent offloading to multiple servers in a multi-server Multi-Access Edge Computing (MEC) system. Each user fully offloads different parts of its computation task at the available MEC servers (or a combination of them) using the same frequency band. We aim to minimize the sum of users’ maximum experienced delay among the different MEC servers, stemming from both the offloading and processing, by jointly optimizing their computation task assignment ratios to the servers, their allocated common-message rates, common and private-message transmission powers, and computing resources related to each server. The formulated min-max-sum problem is non-convex, and its optimization variables are highly coupled. By examining its structure, we equivalently transform the problem and further decompose it into two independent sub-problems that separately provide solutions to the radio and computing resource allocation problems. Numerical results show the effectiveness of the proposed solution in terms of the users’ experienced delay and the proposed algorithm’s real execution time.

Integrated Sensing and Communication (ISAC) has recently emerged as an additional communication service within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) era, through which distributed nodes are able to communicate their sensing information to a Base Station (BS) using integrated signals. In this paper, we study the coexistence of ISAC with other communication types of the nodes, by introducing a software-defined framework to control the nodes' uplink transmission powers related to each service. Each node is simultaneously engaged in two types of communications with different BSs for ISAC and generic data transmission to the cloud via dual connectivity. The uplink power splitting/control problem between the BSs is formulated as a non-cooperative game in satisfaction form, through which each node autonomously concludes to a Satisfaction Equilibrium (SE) point that meets its minimum ISAC and pure communication-oriented requirements. Different achievable SE points are analyzed, while an Reinforcement Learning (RL) and a searching-based algorithm are introduced to conclude to the SE and Minimum Efficient SE (MESE) of the studied problem. Simulation results demonstrate the operation of the algorithms and the overall proposed framework in achieving an efficient share of the resources to the different services.

An effective way to accommodate the computing demands of Internet-of-Things (IoT) end-user devices without the intervention of a remote server, is to motivate the collaboration between them. The latter paradigm, termed as collaborative Mobile Edge Computing (MEC), allows an end-user device to act as service provider, by allocating excess computing resources for the computation of a service requester’s task, in exchange for adequate economic incentives. In this paper, we introduce a contract theory-based one-shot auction to model the computing resource trading between a service requester and the prospective service providers. Unlike existing works, we aim to account for the different types of asymmetric information arising during and after the contracting phase between the trading parties, regarding the service providers’ willingness to collaborate and their offered computing power. The service requester derives a set of optimal economic bids, having statistical knowledge of the providers’ private information, and each service provider autonomously selects the bid and its computing resource allocation that maximize its utility. The economic bid comprises a two-stage payment to secure the provider’s truthful collaboration both prior and after the contractual agreement. The effectiveness of the proposed model is validated by comparison against benchmark contract theory models that unilaterally account for the providers’ private information either prior/during or after the contracting phase.

In this paper, we consider a competitive aerial computation offloading environment, where two edge resource operators provide computing services on a time-slot basis to multiple users, via Unmanned Aerial Vehicles (UAVs), each bearing a mounted edge server. The aim of each UAV is to selfishly maximize the difference between its personal and the opponent UAV's utility, by competitively allocating its energy resources to the different users in the system. The problem is formulated as a Generalized Colonel Blotto (GCB) game, where the UAVs allocate their resources across a number of battlefields, i.e., the users, as competing players, seeking to win the battlefield by increasing the difference of their in-between allocated resources and thus, experienced utility. The overall framework is complemented by a Reinforcement Learning (RL)-empowered algorithm to account for the energy efficient scheduling of the UAVs' overall available energy in the different time slots, where the GCB game is realized. The performance evaluation of the proposed framework is achieved via modeling and simulation. The obtained numerical results demonstrate the operation of the proposed GCB game, under different levels of competitiveness between the UAVs, and assess the effectiveness and efficiency of the proposed RL algorithm against different comparative scenarios.

Capitalizing on the different available computing options across the network, the concept of computing continuum has recently emerged to efficiently manage the exaggerated computation demands of the numerous Internet-of-Things (IoT) users and applications. Nevertheless, the edge computing’s attractiveness to the users, in terms of its reduced incurred time and energy overhead, acts as an impediment in the realization of the envisioned computing continuum. In this paper, recognizing the potential of forwarding delay-tolerant tasks to upper computing layers, we design an incentivization-based mechanism for the offloading users, aiming to shift their preference from the edge to the upper fog computing layer. The corresponding mechanism comprises two stages, in which different models of Contract Theory are adopted. In the first stage, a users-to-edge server contract is formulated to determine the optimal amount of each user’s initially offloaded task at the edge that is allowed to be further forwarded to the fog, based on the user’s delay tolerance. Subsequently, an edge-to-fog server contract is formulated to account for the edge server’s tradeoff between the local execution and transmission overheads, deriving the most beneficial amount of the users’ tasks that ultimately reaches the fog. The overall mechanism is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

The Minimum Collisions Assignment in an interdependent networked system is the problem of assigning a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. It has been shown in the literature that, when the number of resources is larger than the maximum degree of the underlying graph, there exists a randomized algorithm which converges, with high probability, to an assignment of resources having zero collisions. In this work we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We provide and analyze a distributed, randomized, algorithm that converges in a logarithmic number rounds to an assignment of resources over the network for which every node has at most a certain number of collisions.

The Fifth Generation (5G) mobile communications together with software defined networking (SDN) and network function virtualization (NFV) are expected to enable a wide range of vertical use-cases. Different vertical industries with diverse service streams and sets of requirements should leverage the advanced capabilities of 5G networks through a single infrastructure to support the desired Quality of Service/Experience (QoS/QoE). In this paper, we focus on the Transport vertical and we study four novel service categories, each one consisting of one or more related scenarios, within the framework of the 5G Health, Aquaculture and Transport (5G-HEART) 5G PPP Phase 3 project. The first pass analysis of the envisioned vehicular services and their underlying operation, combined with the mapping of the mostly high-level functional user requirements to quantitative network Key Performance Indicators (KPIs) via a thorough and concise methodology, is essential for future testing with real pilots. Furthermore, our work paves the way towards efficient network slicing by exploring the interrelations between the identified KPIs and the respective target values that must be simultaneously satisfied over the same physical network infrastructure, in the context of the three 5G generic services.

The emergence of Unmanned Aerial Vehicles (UAVs) as part of the safety-critical and traffic alleviation infrastructure in 5G and beyond wireless networks, promotes the rethinking of the conventional resource orchestration management. In this paper, we propose a novel methodology that treats the uplink power allocation problem in UAV-assisted wireless networks, operated under Non-Orthogonal Multiple Access (NOMA), based on the principles of labor economics and Contract Theory (CT). The proposed approach specifically targets the challenge of imperfect Channel State Information (CSI) due to the uncertainties of the wireless links. The users are characterized by types that depend on their experienced channel conditions, which are typically unknown to the UAVs, while the latter probabilistically estimate the users’ types. The users’ transmission powers are iteratively optimized and determined, while an Reinforcement Learning (RL)-empowered user-to-UAV association procedure is realized. The overall framework is evaluated via modeling and simulation regarding its proper operation, effectiveness and efficiency, under different scenarios.

To support the provisioning of modern services in a smart city environment, future communication networks need to be intelligently designed with respect to the city infrastructure and energy efficient utilization of resources. Unmanned Aerial Vehicles (UAVs) are already being utilized as part of a smart city wireless network infrastructure to provide on-demand connectivity and eliminate the network’s coverage holes, especially when communication conditions are unfavorable. Complementary to this, the adoption of Reconfigurable Intelligent Surface (RIS) technology allows for the creation of a more controllable smart wireless communications environment. In this article, a multi-user Non-Orthogonal Multiple Access (NOMA) communications system aided by a RIS and a UAV is studied. Based on a single-leader multiple-followers Stackelberg Game, we aim to jointly optimize the overall received signal strength at the UAV and maximize the users’ achieved energy efficiency. The UAV – acting as a leader - intelligently steers the RIS-reflected signals in order to enhance the corresponding received signal quality, by determining the RIS elements’ effective phase shifts. This, in turn, is exploited by the users (i.e., followers), which through the formulation of a non-cooperative game, where each user aims at maximizing its achieved energy efficiency, they determine their optimal uplink transmission power. The proposed optimization framework is evaluated via modeling and simulation, demonstrating the significant power savings and the ultimate users’ satisfaction occurring by the introduction of the RIS.