Placing workloads across the Edge and Cloud can be performed through containerization. However, conditions in Edge nodes are very different from Cloud, as environmental factors such as temperature, humidity, voltage provisioning or dust, heavily affect execution performance and device health. Reliability is an important factor when deciding to deploy such load onto Edge devices, indicating their capability to achieve a desired Quality of Service (QoS). For this, research on Edge computing must focus on how to monitor, estimate and manage devices, in a distributed, autonomous and reliable manner. Our current efforts on performance analysis for devices under "wild conditions" are moving towards integrating reliability into orchestrator systems. Here we present our vision and roadmap for expanding Cloud orchestration towards the Edge with technologies capable of providing knowledge about node environmental conditions and mitigate its impact. Through Out-Of-Band telemetry, we can retrieve temperature and power consumption variables from node components, indicating its health and estimating its reliability given external stress factors. In particular, using intent-based orchestration for containerized platforms, such factors can be used for enforcing reliability as a key-performance indicator. The current work in progress focuses on industrial and commercial scenarios, e.g., Edge computing for urban mobility, with road-side nodes performing AI-based Video-Analytics, exposed to uncontrolled weather conditions. The principal objective is to achieve an Edge network orchestration that takes into account node health in an automatic manner for reducing operational costs such as energy consumption, device repair and replacement, while maintaining QoS in the Edge.

Modern distributed applications increasingly span an edge-cloud continuum, where services may need to dynamically migrate between far-edge, near-edge, and cloud environments to meet latency, resource, or policy constraints. Ensuring seamless service continuity during such migrations remains a significant challenge, particularly due to delays in DNS resolution and inconsistent client routing. This paper presents a modular DNS-driven orchestration framework designed for Kubernetes-based infrastructures. Our approach integrates service orchestration logic with a shared DNS component to enable fast and autonomous service redirection without relying on public DNS providers or complex service meshes. By coupling orchestrator-triggered migrations with DNS record updates, the system ensures immediate service reachability after migration. An optional analytics and decision engine complements the framework by triggering orchestrator actions based on monitoring insights. We evaluate the DNS reconfiguration performance of our solution compared to traditional ExternalDNS-based architectures, demonstrating significantly lower propagation latency and higher determinism during service migrations across the edge–cloud continuum.

As industries increasingly embrace digital and intelligent transformation, enterprises face significant challenges in the containerization upgrades for their Artificial Intelligence(AI) applications and dynamic service migration and management in Cloud-Edge continuum(CEC). This paper presents CloudSkin, an innovative platform designed to realize streamlined, seamless and intelligent service migration in Cloud-Edge Continuum by integrating advanced containerization techniques and AI-driven orchestration capabilities. Our approach, leveraging intelligent algorithms for service migration, can seamlessly transit services between cloud and edge environments, ensuring optimised resource allocation and reducing service latency to assure quality of service(QoS). CloudSkin has been enabled in a Mobility Usecase, empowering Cellnex businesses undergoing digital transformation to achieve higher operational efficiency. The experimental results show that compared to traditional reactive service migration, using intelligent proactive service migration can provide better migration detection, improving F1-Score up to 23.5%, and reducing 28.9% the service running time where the service latency violates SLA.

Effective resource management in edge-cloud networks requires accurate prediction of resource utilization across diverse workloads. The dynamic nature of user demands necessitates prediction models with strong generalization capabilities, i.e., able to achieve high performance in sudden traffic changes or even unseen patterns. Existing works struggle with long-term dependencies and varied temporal patterns. This paper proposes OmniFORE (Framework for Optimization of Resource forecasts in Edge-cloud networks). In particular, we combine attention-based time-series models with temporal clustering to achieve robust generalization and efficiently consume and predict diverse workloads in volatile environments. By training on representative subsets from extensive datasets, OmniFORE captures both short-term stability and long-term changes in resource usage patterns. Experiments with real-world data show that our approach outperforms state-of-the-art methods, particularly in new environments, improving prediction accuracy by 83.87% and achieving 17.92% faster inference.

The sixth generation (6G) of mobile networks seeks to leverage Integrated Terrestrial and Non-Terrestrial Networks (ITNTN) to support next-generation applications. However, despite increased network densification, meeting the stringent latency requirements—particularly for delay-sensitive applications—remains a significant challenge due to the high latency overhead introduced by non-terrestrial nodes. Content caching offers a potential solution to reduce content access delays and backhaul bandwidth costs by pre-storing content closer to the end users. However, the heterogenity of nodes, the varying content popularity and the distinct Quality of Service (QoS) requirements introduced by network slicing complicate caching decisions in ITNTNs. For instance, prioritizing content related to critical services (e.g., emergency services) over popular content (e.g., social media files) may better address QoS needs, albeit with lower hit ratios, an aspect not explored in existing works. This paper introduces SaCCA-RL (Reinforcement Learning-based Slice-aware Content Caching Algorithm) for selecting which content to cache and its placement under constrained ITNTN node capacities, considering different and varying content popularity and priority. Simulations show that SaCCA-RL results in more than 10% improvement in terms of cache gain compared to baseline algorithms, while guaranteeing 100% hit-ratio for critical content.

The virtualization and softwarization of 5G/6G mobile networks have enabled the deployment and orchestration of cloud-native network and application functions. The deployment of these functions is crucial, as the placement of data plane elements (i.e., User Plane Function (UPF)) and vertical services can significantly impact the overall user latency. However, in multi-slice edge scenarios, characterized by users with distinct levels of criticality, the problem of UPF and application placement is becoming increasingly complex due to i) the various costs involved and ii) the limited computational resources at the edge. In this paper, the problem of joint UPF and application placement for a multi-slice user scenario is studied, taking into account multiple cost components that influence the placement decision, including service migration, traffic forwarding, server activation and processing costs. To tackle this problem, we introduce a Joint UPF and Application Reinforcement Learning-based (JUAP-RL) algorithm, which decides the UPF and application deployment location and coordinates the placement stages. Extensive experiments have shown that JUAP-RL demonstrates up to 17% gain in terms of user acceptance ratio and up to 23.4% reduction in provisioning cost compared to baseline schemes.

In this paper, we jointly study the real-time user association, traffic routing and x-Network Function (xNF) placement problem in an integrated Terrestrial and Satellite 6G Network (TN-SN), with the aim of maximizing the network energy efficiency and user acceptance ratio. We formulate the aforementioned problem as a Mixed Integer Linear Program (MILP), considering various capacity, power and flow conservation constraints for the integrated network, while also meeting the specific service requirements of each user. To tackle the increased complexity of the optimal solution, we also develop an efficient heuristic (named as TERA). Through extensive simulations, TERA demonstrates a notable superiority in energy efficiency compared to the current State-of-the-Art (SoA), achieving up to 85 % of the optimal with up to 87 % lower execution time even under challenging traffic load conditions.

Due to the fact that large swathes on Earth still lack broadband communication coverage, especially in remote/rural areas and developing countries, there have been several attempts, starting from 3GPP Release 17, to lay out the architectural amendments needed for the integration of terrestrial networks with their non-terrestrial counterparts. Such attempts have led to recent projects regarding such integration that consider either 5G/5G-Advanced networks or more revolutionary approaches for the forthcoming 6G networks. In this manuscript, we give an overview of the architectural framework, technical innovations, and considered use cases of the Horizon Europe ETHER project.

The fifth generation (5G) networks are designed to support a large range of diverse services with strict performance requirements. Studies suggest that, 5G uses machine learning technologies for variety of tasks ranging from network management, and resource optimization to automated services. The successful integration of 5G with machine learning has also led to the basis for 6G networks. However, the use of machine learning makes the 5G networks susceptible to adversarial attacks. A few works study the effect of differential privacy and adversarial attacks in the 5G systems let alone to provide the proposal of effective defense mechanism. This study proposes Denoising and Adversarial attack-based STacked AutoeNcoder (DASTAN) convolutional neural networks (CNN) to provide defense against a specific differential privacy attack, i.e. membership inference, optimized to detect the device or data distribution potentially used in the training process. DASTAN initiates an intentional attack to camouflage the characteristics of an authorized user from an adversary and uses a de noising stacked autoencoder to recover the information at service provider's end for RF fingerprinting. The aim of RF fingerprinting is to validate the authenticity and identity of the device to preserve the privacy of wireless network. Experimental results demonstrate the efficacy of DASTAN-CNN, which reduces the attack success rate by up to 52.69% in comparison to the case where no defense strategy is employed. The DASTAN-CNN also achieves 75.29% authorized user recognition rate for RF fingerprinting while reducing the attack success rate to 39.23%, which shows the effectiveness in terms of trade-off efficiency.

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates. Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks. We assume that the design of defense mechanism and attacks are two sides of the same coin. Designing a method to reduce vulnerability requires the attack to be effective and challenging with real-world implications. In this work, we propose simulated poisoning and inversion network (SPIN) that leverages the optimization approach for reconstructing data from a differential model trained by a vehicular node and intercepted when transmitted to roadside unit (RSU). We then train a generative adversarial network (GAN) to improve the generation of data with each passing round and global update from the RSU, accordingly. Evaluation results show the qualitative and quantitative effectiveness of the proposed approach. The attack initiated by SPIN can reduce up to 22% accuracy on publicly available datasets while just using a single attacker. We assume that revealing the simulation of such attacks would help us find its defense mechanism in an effective manner.

Service providers highly rely on network softwarization for addressing the demanding use cases of the 5G verticals. In order for the 5G vertical scenarios to be seamlessly executed, where the coverage should be provided in large areas, the required infrastructure capacity and availability can only be supported by cross-operator cooperation. In this context, we employ a novel federated core-edge 5G architecture, where 5G vertical services are represented as service function chains (SFCs) and can be offered on the infrastructure owned either by the local operator or a foreign one. Furthermore, we propose two online SFC placement methods that aim to efficiently place the SFCs across the architecture, taking into account the deployment cost. A simulation of the optimal solution, based on an integer linear programming (ILP) approach, is evaluated against a hop-based heuristic placement method that runs on our experimental 5G platform. Our experimental results demonstrate that the optimized placement produces the most cost-effective solution. Our heuristic algorithm introduces a near-optimal solution, but with a higher deployment cost. Compared with the ILP approach, the heuristic solution provides lower complexity and execution time. Finally, in order to further augment the placement techniques, we propose enhanced lifecycle management methods that use live migration and scaling actions to further decrease the execution cost and adapt to real-time incoming traffic, respectively.

The deployment of fifth generation (5G) mobile networks along with the massive penetration of multimedia applications render the edge cache capacity a valuable but limited network resource. Edge caching has been lately widely adopted, as it can bring to the network important benefits, such as better network utilization and higher Quality of Experience (QoE). However the cache sharing among different Content Service Providers (CSPs) is a non-trivial problem due to: i) the limited nature of cache resources, and ii) the network neutrality concept that requires equal treatment of the CSPs. In this paper, we study the cache sharing problem in the presence of multiple CSPs, focusing both on the fairness and the network performance. Taking into account the importance of network neutrality and the scarcity of the cache resources, we formulate the cache sharing as a bankruptcy problem and we propose a set of different approaches for its solution. The performance of the proposed approaches is assessed through a series of simulation experiments in different scenarios (i.e., assuming different popularity distributions and market shares), identifying the potential trade-offs.

As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, we evaluate the proposed mobility orchestration combination and we find gains in low latency in all examined scenarios.

The increasing demand for fast, reliable, and robust network services has driven the telecommunications industry to design novel network architectures that employ Network Functions Virtualization and Software Defined Networking. Despite the advancements in cellular networks, there is a need for an automatic, self-adapting orchestrating mechanism that can manage the placement of resources. Deep Reinforcement Learning can perform such tasks dynamically, without any prior knowledge. In this work, we leverage a Deep Deterministic Policy Gradient Reinforcement Learning algorithm, to fully automate the Virtual Network Functions deployment process between edge and cloud network nodes. We evaluate the performance of our implementation and compare it with alternative solutions to prove its superiority while demonstrating results that pave the way for Experiential Network Intelligence and fully automated, Zero touch network Service Management.

Content caching has been considered by both academia and industry as an efficient solution to tackle the problem of the back-haul becoming the bottleneck in the service of users in future heterogeneous cellular networks. Most of the related caching-oriented studies are based on the content popularity, overlooking the impact of content size on their analysis. In this context, this work studies content caching in an environment where cellular users are equipped with cache memories. In particular, we formulate the content caching as an optimization problem, where the objective is to minimize the average download latency of popular videos through self-caching and device-to-device (D2D) caching and, consequently, increase the network throughput. In addition, in order to solve this problem in real-time scenarios, we introduce a low-complexity utility-based algorithm, which accounts for parameters such as the size and the popularity of the requested contents, as well as the density of the end users. Finally, we provide extensive simulation results that validate our analysis and prove that our innovative scheme outperforms other existing solutions.

The upcoming fifth generation (5G) mobile communications urge software-defined networks (SDNs) and network function virtualization (NFV) to join forces with the multiaccess edge computing (MEC) cause. Thus, reduced latency and increased capacity at the edge of the network can be achieved, to satisfy the requirements of the Internet of Things (IoT) ecosystem. If not properly orchestrated, the flexibility of the virtual network functions (VNFs) incorporation, in terms of deployment and lifecycle management, may cause serious issues in the NFV scheme. As the service level agreements (SLAs) of the 5G applications compete in an environment with traffic variations and VNF placement options with diverse computing or networking resources, an online placement approach is needed. In this article, we discuss the VNF lifecycle management challenges that arise from such heterogeneous architecture, in terms of VNF onboarding and scheduling. In particular, we enhance the intelligence of the NFV orchestrator (NFVO) by providing: 1) a latency-based embedding mechanism, where the VNFs are initially allocated to the appropriate tier and 2) an online scheduling algorithm, where the VNFs are instantiated, scaled, migrated, and destroyed based on the actual traffic. Finally, we design and implement an MEC-enabled 5G platform to evaluate our proposed mechanisms in real-life scenarios. The experimental results demonstrate that our proposed scheme maximizes the number of served users in the system by taking advantage of the online allocation of edge and core resources, without violating the application SLAs.

As industries are under pressure for shorter business and product life cycles, there is an extensive effort from the research community for novel and profitable automation processes. This effort has given rise to the fifth-generation (5G) Tactile Internet, which is characterized by extremely low latency communication in combination with high availability, reliability, and security. In this paper, we discuss the key technologies to support the Tactile Internet characteristics in industrial environments and then showcase the implementation of a novel 5G network function virtualization enabled experimental platform. Given that ultra-reliable low-latency communication is crucial for the manufacturing process, we demonstrate that, in our setup, submillisecond end-to-end communication is attainable, proving the suitability of our platform for Tactile Internet industrial applications.

Considering that cities are typically divided in residential and business districts, massive population migrations between different areas introduce considerable discrepancies in the traffic distribution. However, although novel technologies are constantly introduced to fulfill the 5G requirements, the current cellular networks raise some limitations due to their static deployment. Aerial networks with unmanned aerial vehicles (UAVs) that carry access points can flexibly overcome this issue by controlling this traffic effectively in an ad hoc manner, but their limited power storage is their major drawback. In this paper, we propose the use of solar-powered charging stations to satisfy the energy needs of UAVs and formulate a theoretic matching problem to ensure the best performance in terms of energy, communication, and safety. Then, we evaluate the network performance by comparing the coverage and utilization between the two scenarios: i) the static infrastructure, and ii) the aerial network, and we present useful insights regarding the benefits of aerial networks.

Fifth Generation (5G) Cloud-Radio Access Networks (C-RANs) are about to exploit both optical and Millimeter Wave (mmWave) technology to meet the ever-increasing traffic demands. In this new type of converged Fiber-Wireless (FiWi) systems efficient Medium Transparent-Medium Access Control (MT-MAC) protocols should be designed, able to satisfy the very strict 5G service requirements. To this end, in this paper, we propose an MT-MAC protocol for mmWave Analog Radio-over-Fiber (A-RoF) C-RANs, which employs gated service, according to which users are granted transmission windows equal to the number of bytes contained in their buffer. An analytical model is also proposed for the mean packet delay, which is verified by means of simulation for different fiber length values, network load conditions and optical capacity values. Our results not only prove the accuracy of the proposed model but also the suitability of the proposed MT-MAC protocol to meet the sub-ms delay challenge of latency-critical 5G network requirements.

The unprecedented surge in mobile data traffic, along with the wide range of services and the corresponding different performance requirements, has raised the need for a new mobile network architecture. In that sense, 5G has been conceived as a software defined network able to provide service-tailored connectivity. Network slicing is a key mechanism to serve efficiently the diversified service requirements. In this paper, we formulate the joint RAN slicing and functional split with optimization of centralization degree (CD) and throughput. Our results show that even though in terms of CD we have more costs, we can better meet the service requirements and also have more throughput in the network.

The upcoming generation of mobile networks is expected to serve numerous mobile users with high quality-of-service (QoS) demands, requiring high-capacity fronthaul. As the provision of fiber connections directly to the end users is not cost-efficient, the integrated fiber wireless (FiWi) fronthaul design based on wireless networking and passive optical networks (PONs) has been proposed. The FiWi design involves modern networking technologies that can accommodate the need for data rates in the Gb/s scale and low delay, such as the wavelength division multiplexing (WDM) in the optical domain and the multiple input multiple output (MIMO) communication over millimeter wave (mmWave) spectrum in the wireless domain. The co-existence of two network types requires resource management in a medium transparent manner, i.e., the sharing of the bandwidth in the wireless domain should allow the organization of the data packets in optical frames. As the traffic circulating in the FiWi fronthaul involves packets of different priorities, i.e., different QoS classes, the resource management scheme should support QoS differentiation. To this end, we propose a resource management scheme for FiWi fronthaul and we extensively study its performance in terms of experienced delay and throughput. Our simulation results demonstrate that the proposed scheme significantly reduces the delay of the high priority class.

Nowadays, over-the-top (OTT) applications can be accessed via Internet connections over cellular networks. Mobile network operators (MNOs) strive to accommodate mobile traffic through energy efficient shared networks. The OTT service providers (OSPs) need to interact with MNOs and require resources for offering desired quality- of-service (QoS) levels to users of different categories, with different QoS requirements, which imply prioritization of certain OTT flows. Resource scheduling should respect network neutrality, which forbids OSP prioritization. Furthermore, OSPs request resources periodically, according to their performance goals, causing delay in flows' accommodation due to i) the time required for information exchange between OSPs and MNOs, affected by network congestion, and ii) the time required for flows to receive resources, affected by the number of active flows. The intervention of OSPs may induce additional energy cost for the MNOs and affect the mobile network energy efficiency. Acknowledging the lack of OSP- oriented resource management approaches, we introduce a matching theoretic flow prioritization (MTFP) algorithm and investigate delay and energy efficiency through extensive simulations. Our study shows that MTFP improves both metrics comparing to the best effort approach, whereas its performance is affected by the OTT traffic level and the frequency of resource allocation process.

In the forthcoming Smart City scenario, Service Providers will require users to authenticate themselves and authorize their mobile applications to access their remote accounts. In this scenario, OAuth 2.0 has been widely adopted as a de facto authentication and authorization protocol. However, the current OAuth 2.0 protocol specification does not consider the user privacy issue and presents several vulnerabilities that can jeopardize users’ privacy rights. Therefore, in this paper we propose an OAuth 2.0 based protocol for Smart City mobile applications that addresses the user privacy issue by integrating a pseudonym-based signature scheme and a signature delegation scheme into the OAuth 2.0 protocol flow. The proposed solution allows users to self-generate user-specific and app-specific pseudonyms on-demand and ensure privacy-enhanced user authentication at the Service Provider side. The proposed protocol has been validated with Proverif and its performance has been evaluated in terms of time and space complexity. Results show that the proposed protocol can provide users with efficient and effective means to authenticate towards service providers while preventing user tracking and impersonation from malicious entities located in the network side or in the users’ mobile device.

The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the widespread use of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a cooperative nonparametric statistical framework, namely COPS, for the mitigation of user misbehavior in network coding scenarios. Given that the behavior of adversaries cannot be characterized by certain probability distributions, the proposed scheme exploits two well-known nonparametric statistical methods, i.e., Kruskal-Wallis analysis and Conover-Iman multiple comparisons, for the detection and identification, respectively, of malicious users in the network. It is worth noting that the COPS framework does not require monitoring of the wireless channel and additional overhead, as its operation is based on the processing of the existing control packets. We assess the performance of the proposed scheme in various scenarios, showing that COPS is able to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.

Modern over-the-top (OTT) applications can be accessed via Internet connections over cellular networks, possibly shared and managed by multiple mobile network operators (MNOs). The OTT service providers (OSPs) need to interact with MNOs, requesting resources for serving users of different categories and with different quality-of-service requirements. For this purpose, OSPs need OTT application flow prioritization in resource allocation, while the network resource scheduling should respect network neutrality that forbids OSP prioritization. OSPs also need to request resources periodically, according to their performance goals, i.e., grade-of-service (GoS) level (blocking probability), causing delay in flows' accommodation due to: 1) the time required for information exchange between OSPs and MNOs, affected by network congestion, and 2) the time required for flows to receive resources, affected by the number of concurrently active flows. Acknowledging the lack of OSP-oriented resource management approaches, we: 1) introduce a novel matching theoretic flow prioritization (MTFP) algorithm that respects network neutrality and 2) design analytical models that enable the thorough investigation of the GoS and delay performance in various scenarios. Our results (analytical and simulation) show that MTFP improves both metrics compared to the best effort approach, whereas its performance is affected by the number of flows and the resource allocation frequency.

Emerging 5G communication paradigms, such as machine-type communication, have triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. Ensuring a reliable network operation under fading conditions is not straightforward, as the transmission schemes and the network topology, i.e., uniform or clustered deployments, affect the performance and should be taken into account. Moreover, as the number of nodes increases, exploiting natural energy sources and wireless energy harvesting (WEH) could be the key to the elimination of maintenance costs while also boosting immensely the network lifetime. In this way, zero-energy wireless-powered sensor networks (WPSNs) could be achieved, if all components are powered by green sources. Hence, designing accurate mathematical models that capture the network behavior under these circumstances is necessary to provide a deeper comprehension of such networks. In this paper, we provide an analytical model for the connectivity in a large-scale zero-energy clustered WPSN under two common transmission schemes, namely, unicast and broadcast. The sensors are WEH-enabled, while the network components are solar-powered and employ a novel energy allocation algorithm. In our results, we evaluate the tradeoffs among the various scenarios via extensive simulations and identify the conditions that yield a fully connected zero-energy WPSN.

As wearables provide new health-related functionalities, they can be employed in hospitals to monitor patients and notify the medical personnel regarding their status. However, in order to be approved by the medical community, wearables need to have reliable communication and high lifetime. In such scenarios, it is important to know the probability of correct notification which is affected mainly by the deployment of the wireless wearables and their energy supply. Typically, rooms in hospitals host multiple people and, thus, a clustered communication model should be adopted for more trustworthy results. Moreover, by employing wireless charging, it is possible to provide an uninterrupted operation with high reliability. Therefore, in this paper, we study the aforementioned probability in a clustered network while the wearable devices are wirelessly charged. We provide an analytical model for the wearables' ability to inform quickly the medical personnel and discuss different trade-offs via extensive simulations.

The present paper studies fair sharing possibilities of stored renewable energy (RE) in densely populated areas among multiple mobile network operators (MNOs). The main objective is to enable MNOs, who already share their infrastructure in terms of a green network operation, to further reduce their power consumption by fairly sharing (i) the solar power harvested during daylight and (ii) the harvested energy that is stored in storage devices, e.g. batteries, during night hours. To this end, a bankruptcy game is designed so as to capture the energy sharing interactions among MNOs. Taking into consideration the energy deficits of each MNO for a studied period of time, Shapley Value (SV) is employed to fairly determine the amounts of energy to be allocated to each MNO. Focusing on night hours, it is shown that the proposed scheme provides all cooperative MNOs with long store-energy-based network operation during night in comparison to baseline schemes. Moreover, we display that the proposed scheme outperforms its counterparts in terms of needed grid support and allocation fairness among the cooperative MNOs for different MNO traffic patterns and volumes.

Facing the increasing energy demands associated with the perspective of fifth generation (5G) wireless networks, the Mobile Network Operators (MNOs) are motivated to gradually convert their traditional Radio Access Network (RAN) infrastructure to more flexible and power efficient centralized architectures, i.e., Cloud-RAN (C-RAN). Apart from their promising benefits in terms of management and network optimization, these new architectures further enable the sharing of spectrum and network elements, such as the Remote Radio Heads (RRHs) and the Baseband Units (BBUs), among multiple operators. In this paper, we introduce a novel scheme based on coalitional game theory to identify the potential room for cooperation among different MNOs that provide service to the same area. The proposed scheme sets the rules for profitable collaboration and identifies the core formation conditions (i.e., pricing) for various scenarios with different market and spectrum shares among three operators. Our results show that i) cooperation among subcoalitions of MNOs is always beneficial, yielding both higher revenues and enhanced Quality of Service (QoS) for the end users, and ii) the cooperation of all operators (grand coalition) is profitable for given user pricing in different scenarios.

In this paper, we propose a novel policy for device caching that facilitates popular content exchange through high-rate device-to-device (D2D) millimeter-wave (mmWave) communication. The D2D-aware caching policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices, with the goal to enable D2D connections. By exploiting the high bandwidth availability and directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the throughput gains of the D2D communication in the sub-6-GHz bands. Furthermore, based on a stochastic-geometry modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex modes for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte Carlo simulations. In addition, for a wide range of a content popularity indicator, the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.

In long term evolution advanced (LTE-A) networks, the mobile devices can concurrently participate in cooperative outband device-to-device (D2D) data exchange by virtue of user- or network-related parameters (e.g., interest in the same content and cooperative transmissions, respectively). In these scenarios, two major problems arise: 1) the coexistence of multiple devices creates channel access issues, demanding effective medium access control (MAC) schemes, and 2) cellular network factors (i.e., scheduling policy and channel conditions) affect the D2D communication, as the circulating information in D2D links is mainly of cellular network origination, stressing the need for cross network approaches. In this context, the contribution of this paper is threefold. First, exploiting idle devices as relays and the benefits of network coding (NC) in bidirectional communications, we propose an adaptive cooperative NC-based MAC (ACNC-MAC) protocol for the D2D data exchange. Then, we devise a cross-network model that captures the impact of cellular network characteristics on D2D communication. Finally, we evaluate the performance of the ACNC-MAC in terms of throughput, energy efficiency, and battery consumption. Our results show that the LTE-A parameters and the relays' participation significantly affect the D2D throughput, while the D2D performance deteriorates with the increase of cell congestion.

Wireless network virtualization enables efficient network sharing in Long Term Evolution Advanced (LTE-A) networks. For the accommodation of ever-increasing user demands, Mobile Virtual Network Operators (MVNOs) can lease and operate virtual resources in network infrastructure that belongs to Mobile Network Operators (MNOs). The coexistence of multiple MVNOs that serve users with different Quality of Service (QoS) requirements and spatial distribution in an LTE-A cell further complicates the arising resource allocation problem. Moreover, the MVNOs aim to satisfy the QoS demands for the maximum possible number of users with the minimum possible cost. This multifaceted context renders centralized optimization approaches for virtual resource allocation in shared LTE-A networks unsuitable. As an alternative, the framework of matching theory, thanks to its distributed nature, can achieve a proper allocation through a stable matching between resources and MVNOs. In this context, we introduce a matching-theoretic formulation for the virtual resource allocation problem and propose a distributed algorithm that properly matches the MVNOs with the available resources. Our simulation results show that the proposed algorithm reaches a stable matching that satisfies the QoS demands for more users in comparison with other approaches.

Full-Duplex (FD) wireless and Device-to-Device (D2D) communication are two promising technologies that aspire to enhance the spectrum and energy efficiency of wireless networks, thus fulfilling key requirements of the 5thgeneration (5G) of mobile networks. Both technologies, however, generate excessive interference, which, if not managed effectively, threatens to compromise system performance. To this direction, we propose two transmission policies that enhance the communication of two interfering FD-enabled D2D pairs, derived from game theory and optimization theory. The game-theoretic policy allows the pairs to choose their transmission modes independently and the optimal policy to maximize their throughput, achieving significant gains when the pairs interfere strongly with each other.

The emerging Internet of Things paradigm has triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. However, the channel randomness and the random deployment of such networks could cause missed detections by isolated (i.e., unable to disseminate their messages) or inactive (i.e., without enough energy to transmit) nodes. Moreover, as the number of nodes increases, the use of “green” solutions such as wireless energy harvesting for powering battery-less devices could eliminate the maintenance costs and boost immensely the network lifetime. In this paper, we study the connectivity in a wireless-powered sensor network (WPSN) with battery-less devices under two common routing schemes, namely unicast and broadcast. We provide an analytical model for the ability of a WPSN to be reliable and evaluate the trade-offs among the different scenarios via simulation.

Contemporary technologies as implemented in the field of healthcare have provided the everyday clinical practice with a plethora of tools to be used in various settings. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most promising technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. WSN4QoL is a Marie Curie project involving academic and industrial partners from three EU countries, which aims to show how new WSNs-based technologies suit the specific requirements of pervasive healthcare applications. In particular, in this paper, the WSN4QoL's system architecture is presented as designed to exploit the Network Coding (NC) mechanisms to achieve energy efficiency in the wireless communications and distributed positioning solutions to locate patients in indoor home environments. The system has been validated through experimental activities using commercial off the shelf (COTS) WSN testbeds and medical devices prototypes offered by a commercial partner. Results demonstrate that NC helps in achieving substantial gains in terms of energy efficiency as compared to traditional relay mechanisms, while the proposed positioning solution is able to locate people in indoor environments at a sub-room accuracy level, without requiring any extra dedicated hardware.

In the Internet of Things (IoT) context, the massive proliferation of wireless devices implies dense networks that require cooperation for the multihop transmission of the sensor data to central units. The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the introduction of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a non-parametric statistical approach, based on the Kruskal-Wallis method, for the detection of user misbehavior in network coding scenarios. The proposed method is shown to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.

Due to the dense small cell deployment to cope with the increased data traffic of fifth generation (5G) networks, the direct backhaul (BH) connection of all small cells to the core network becomes challenging. To that end, millimeter wave (mmWave) is a promising solution for cost-efficient high capacity small cell BH links. However, due to the severe signal attenuation at high frequencies, mmWave can provide good coverage only for short distances, thus favoring a multi-hop 5G BH architecture. In this context, user association that impacts both the network and user performance calls for BH-aware strategies. Hence, in this paper, we study the user association problem aiming at the joint energy and spectrum efficiency maximization of the network, without compromising the user quality of service. The problem is formulated as a generalized assignment problem, which considers the capacity and energy consumption both in the access network and BH links. Provided that the considered problem is shown to be NP-hard, we employ optimization techniques to obtain an upper bound on system performance. We also propose a low complexity algorithm, which is shown to outperform the state-of-the-art while achieving near-optimal performance.

As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between colocated sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the tradeoffs between the direct and cooperative communication scenarios.

The dense deployment of high and low power nodes, i.e., eNodeBs (eNBs) and small cells (SCs), respectively, is expected to be the core element of fifth generation (5G) networks. Given the massive SC deployment, the aggregate power consumption attributed to SCs in the coverage area of a macrocell becomes considerable, thus raising issues for the network energy efficiency. However, the coexistence of multiple mobile network operators (MNOs) in the same geographic area fosters cooperation agreements that suggest further energy and cost saving. In this paper, with a view to address the aforementioned issues, we attempt to study the energy efficiency aspects in the case of next generation outdoor Long Term Evolution (LTE) heterogeneous networks (HetNets) owned by rival MNOs. More specifically, we propose a cooperative switching off scheme for low-traffic hours that applies to both eNBs and SCs and exploits roaming-based infrastructure sharing to guarantee the provided quality of service (QoS). To gain insights, we study the energy consumption impact compared to existing switching off schemes. It is shown that the proposed scheme achieves notable energy saving, without compromising the QoS to the network users.

Smart Grid (SG) is considered as the future of the electrical power distribution system, where Advanced Metering Infrastructure (AMI) will let utilities to acquire and analyze the consumption data and access and control several home appliances for power balancing purposes through special devices, known as Smart Meters (SMs). Long Term Evolution (LTE) appears as a promising solution to handle the SMs traffic due to its high bandwidth and flexibility. However, given that SMs need to periodically send measurements to the eNodeB (eNB), the network uplink could be a potential bottleneck, thus affecting the users running real-time applications, such as voice calls. To overcome this problem, in this paper, we present a scheduling policy that jointly considers the channel quality, the traffic prioritization and the AMI Packet Delay Budget (PDB) in order to provide the SMs with the required resources and reduce the impact on the real-time traffic. Extensive simulation experiments have been carried out, indicating the smooth coexistence between the SMs and the voice users, since the proposed scheduler achieves higher percentage of served users compared to widely employed schedulers.

Device-to-Device (D2D) communication allows the direct connection of mobile devices in a cellular network. In D2D networking, cooperative communication is inherent due to the proximity of the devices that are able to overhear and forward information. Particularly, adjacent devices can act as relays and assist the communication of other devices. Network Coding (NC) can further increase the cooperation gains, since a number of packets can be encoded and transmitted together. However, the contention among multiple relays causes channel access issues that must be regulated by effective Medium Access Control (MAC) protocols. In this context, we propose an Adaptive Cooperative Network Coding-based MAC (ACNC-MAC) protocol that utilizes cooperative relaying and exploits NC opportunities in a D2D topology. Both analytical and simulation results show that the proposed protocol is advantageous in terms of energy efficiency without sacrificing the Quality of Service (QoS).

As the number of nodes in wireless sensor networks (WSNs) increases, new challenges have to be faced in order to maintain their performance. A fundamental requirement of several applications is the correct transmission of the measurements to their final destinations. Thus, it is crucial to guarantee a high probability of connectivity, which characterizes the ability of every node to report to the fusion center. This network metric is strongly affected by both the fading characteristics and the different routing protocols that are used for the dissemination of data. In this paper, we study the probability of a network to be fully connected for two widely employed routing mechanisms, namely unicast and K-anycast. The analytical derivations and the simulations evaluate the trade-offs among the different routing mechanisms and provide useful guidelines on the design of WSNs.

Wireless body area networks (WBANs) are composed of sensors that either monitor and transmit vital signals or act as relays that forward the received data to a body node coordinator (BNC). In this paper, we introduce an energy efficient vital signal telemonitoring scheme, which exploits compressed sensing (CS) for low-complexity signal compression/reconstruction and distributed cooperation for reliable data transmission to the BNC. More specifically, we introduce a cooperative compressed sensing (CCS) approach, which increases the energy efficiency of WBANs by exploiting the benefits of random linear network coding (RLNC). We study the energy efficiency of RLNC and compare it with the store-and-forward (FW) protocol. Our mathematical analysis shows that the gain introduced by RLNC increases as the link failure rate increases, especially in practical scenarios with a limited number of relays. Furthermore, we propose a reconstruction algorithm that further enhances the benefits of RLNC by exploiting key characteristics of vital signals. With the aid of electrocardiographic (ECG) and electroencephalographic (EEG) data available in medical databases, extensive simulation results are illustrated, which validate our theoretical findings and show that the proposed recovery algorithm increases the energy efficiency of the body sensor nodes by 40% compared to conventional CS-based reconstruction methods.

The explosive data traffic demands stress the need for additional capacity in cellular systems, which can be achieved through the exploitation of Small Cells (SCs). However, the deployment of SCs implies higher energy consumption in the network, something that translates into higher financial cost for the operators. To that end, the existence of a third party that provides a common SC infrastructure for the operators has been introduced as an appealing "green" solution, raising though important issues with regard to the cost sharing for the operators. In this paper, we effectively address this issue by proposing an accurate cost model for the SCs and employing different state-of-the-art techniques to share this cost. In addition, taking into account the impact of the traffic pattern on this cost, we propose a novel hybrid cost sharing policy that provides a fair outcome for the operators. Our results highlight the potential energy efficiency gains in the network along with different ways of sharing the cost of the deployed SCs.

Wireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches.

In this paper, we aim at the optimal use of the scarce energy collected by a node powered by human energy harvesting in order to improve the provided Quality of Service (QoS). To achieve this goal, we have developed a Power-QoS control scheme, called PEH-QoS. PEH-QoS is composed of three sub-modules that interact with each other in order to make optimal use of energy and get the best possible QoS. The scheme intends to ensure that a node can both capture/detect the medical events and transmit the respective data packets efficiently. One of the main features of our mechanism is that only useful data sequences are transmitted, discarding data packets that have lost their clinical validity (i.e., out of date). Extensive simulations have been conducted in order to evaluate the behavior of PEH-QoS in a typical medical node under energy harvesting conditions.

Random dense deployment of heterogeneous networks (HetNets), consisting of macro base stations (BS) and small cells (SC), can provide higher quality of service (QoS) while increasing the energy efficiency of the cellular network. In addition, it is possible to achieve lower deployment cost and, therefore, maximize the benefits for the network providers. In this paper, we propose a novel method to determine the minimum deployment cost of a two-tier heterogeneous cellular network using random deployment. After deriving the coverage probability of the two-tier deployment by using stochastic geometry tools, we identify the tier intensities that provide the minimum deployment cost for a given coverage probability. Extensive simulations verify the existence of a unique set of intensities for different coverage constraints.

In this paper, a novel Network Coding (NC)-aided Medium Access Control (MAC) protocol with mobility support (NCMOB-MAC) is presented. The proposed protocol is compatible with Automatic Repeat reQuest (ARQ) techniques and can be applied in Vehicular Ad hoc Networks (VANETs). NCMOB-MAC proposes a new way of coordinating the communication between a sender, a moving destination and a set of fixed relay nodes. The relays employ different Contention Window (CW) sizes to assign priorities and optimize the NC opportunities. Extensive simulations have been carried out to evaluate the performance of the protocol and the results show an improvement in throughput and energy efficiency compared to conventional NC-aided MAC protocols.

The existence of multiple active sources in wireless data dissemination scenarios raises considerable channel access issues. Although the overall goal in the network is the successful accomplishment of the dissemination, the individual wireless nodes aim at maximizing their battery lifetime by minimizing their particular energy consumption. Considering thus the self-centered behavior of the nodes, we anticipate to design Medium Access Control (MAC) policies in order to provide the terminals with energy efficient solutions without compromising the dissemination completion time. In this paper, we propose non-cooperative game theoretic channel access strategies by estimating equilibrium points that achieve balance between conserving energy and completing the data dissemination. In particular, we present two different MAC strategies: i) a distributed approach for ad-hoc networks, and ii) a coordinated approach for infrastructure networks, where a central controller is sporadically used to accelerate the data dissemination. In addition, network-coded transmissions are considered to eliminate the need of control packets exchange. Both analytical and simulation results are provided to evaluate our proposed schemes, demonstrating the significant gains that game theoretic techniques can bring to the network performance compared to standardized solutions.

In this paper, we propose a hybrid polling Medium Access Control (MAC) protocol with Human Energy Harvesting capabilities, called HEH-BMAC, designed for Wireless Body Area Networks (WBANs). The proposed protocol uses a dynamic schedule algorithm to combine User Identification polling (ID) and Probabilistic Contention (PC) random access, adapting the network operation to the random, time-varying nature of the human energy harvesting sources. HEH-BMAC offers different levels of node priorities (high and normal), energy-awareness and flexibility. Extensive simulations have been conducted in order to evaluate the energy efficiency and throughput of our scheme compared to IEEE 802.15.6 standard in energy harvesting conditions.

Life expectancy is projected to increase significantly in the coming years. This fact has pushed the need for designing new and more pervasive healthcare systems. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most suitable technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. This paper proposes recent advancements in this field by introducing WSN4QoL, a Marie Curie project which involves academic and industrial partners from three EU countries. The project aims to propose new WSN-based technologies to meet the specific requirements of pervasive healthcare applications. In particular, in this paper, a Network Coding (NC) mechanism and a distributed localization solution are presented. They have been implemented on WSN testbeds to achieve efficiency in the communications and to enable indoor people tracking. Preliminary results in a real environment show good system performance that meet our expectations.

The emerging increase of the number of Base Stations causes redundant energy consumption, especially during low traffic periods, when the Base Stations capacity is underutilized. In this paper, we study energy efficiency issues in multi-operator mobile networks. Our primary goal is to save energy, without compromising the offered Quality of Service, by switching off the excessive Base Stations. To this end, we propose a novel game theoretic strategy using cost-based functions to decide the best suitable Base Stations to remain active. Mathematical analysis and simulations demonstrate that the proposed scheme can significantly reduce the energy consumption.

In this paper we present an analog-network-coding-aided layout for data dissemination. We propose a game theoretic Medium Access Control (MAC) protocol that exploits the recent developments in network coding domain to enhance the system performance. In particular, random linear network coding is used to eliminate the need of exchanging control packets, while ZigZag decoding techniques are applied to resolve the data packet collisions. Our proposed protocol is proved to enhance both the provided Quality of Service and the energy efficiency of the system.

In this paper we propose a game theoretic medium access strategy for data dissemination in wireless networks. In data dissemination scenarios, where the global target is the spreading of a particular content in a network, the main goal of the wireless nodes is twofold: to guarantee a low completion time, and to increase their battery lifetime. Hence, the existence of many active sources in the network implies conflicting situations, since the nodes have to balance a trade-off between proceeding the dissemination and saving energy. To model these conflicts, we introduce a n-player medium access game using energy-based utility functions. Both analytical and simulation results are provided to evaluate our proposed strategy.

In this paper we introduce a novel Medium Access Control (MAC) protocol for Automatic Repeat reQuest-based (ARQ-based) cooperative wireless networks. Our protocol coordinates the channel access among a set of relays that use network coding techniques to minimize the number of total transmissions, thus enhancing the performance of the network in terms of Quality of Service (QoS) metrics. The proposed solution is compared to other cooperative schemes, while analytical and simulation results are provided to evaluate our protocol.

In this paper we propose game theoretic Medium Access Control (MAC) strategies for data dissemination scenarios. In particular, we use energy-based utility functions that inherently imply power-awareness, while we consider network coding techniques to eliminate the necessity of exchanging acknowledgement control packets. Simulation results show that our proposed strategies enhance the energy efficiency of the system and reduce the dissemination completion time compared to an optimized standard protocol.

In this paper, we propose a switch on/off algorithm for Base Stations (BSs), which exploits the knowledge of the distance between the User Equipments (UEs) and their associated BS. Our novel approach hopes to provide an improvement to the problem of energy consumption. The major concern lies on reducing the energy consumption of the telecommunication networks by optimizing the power utilization without sacrificing the offered Quality of Service (QoS). Our proposed scheme achieves a significant power saving, based on switching off the Base Stations that are underutilized during low traffic periods (especially during night) in the LTE-Advanced.

In this paper we introduce a novel Automatic Repeat reQuest (ARQ) scheme for cooperative wireless networks. Our scheme adopts network coding techniques in order to enhance the total bandwidth of the network by minimizing the total number of transmissions. The performance of the proposed approach is evaluated by means of computer simulations and compared to other cooperative schemes, while an analytical solution is provided to validate the results.

In this paper we introduce a connection admission control (CAC) mechanism for IEEE 802.16 broadband wireless access standard. Our scheme is based on the bandwidth reservation concept and has been developed considering the problem of "busy hour" in communications traffic variation during a typical day. The proposed solution, which is compatible to the IEEE 802.16 Standard, provides higher priority to VoIP calls compared to other types of traffic in the network. The performance of the proposed mechanism is evaluated by means of computer simulations and compared to simple traditional admission control schemes, while we provide an analytical solution as well.