Search

Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu- ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache Kafka

The use of multipath techniques in transmission has emerged in the last years thanks to their potential in increasing throughput. They can also be used as a means to counteract errors or losses in transmission, thus increasing reliability. In this work, we focus on the challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) via multiple wireless channels. We propose a lightweight scheduler capable of dynamically selecting the paths to be used and of determining the necessary redundancy rate to protect the multimedia flow. Our scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The results we present show that the proposed scheduler can be used to target a very low loss rate by dynamically adapt to varying channel conditions in terms of losses and experienced delay.

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver’s stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving emulation software, CARLA, which allows testing the approach’s feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.

Mega-LEO satellite constellations are becoming a concrete reality. Companies such as SpaceX, Virgin Orbit, and OneWeb have already started launching hundreds of LEO satellites and are turning their services on. Even if the aim of such LEO satellite constellations is just, for now, to offer worldwide Internet access equality, their deployment proves their feasibility and suggests usefulness for further purposes. In this article, we shed some light on the possible integration of the in-network computing paradigm in mega-LEO satellite constellations. Terrestrial and/or non-terrestrial nodes can benefit from offloading the computing to an orbital edge (OE) platform reachable through the satellite constellation, exploiting its fast and distributed computational capability. In this context, a preliminary analysis highlights that task offloading strategies can lead to performance improvements that open up novel challenges in the design and setup of OE platforms.

Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that, once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, through two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data., This work is partially supported by the MIUR PON project OK-INSAID (GA #ARS01_00917) and by the H2020 projects TEACHING (GA #871385), HumanAI-Net, (GA #952026), MARVEL (GA #957337), and So-BigData++ (GA #871042).

The rate of traffic generated by Machine-type Communications (MTC) is growing at a very fast pace, putting under strong pressure existing network infrastructures. One of the most challenging tasks attoday is being able to support machine-to-machine (M2M)/Internet ofThings (IoT) data exchanges by providing connectivity between any pair of M2M devices all over the world. For this reason, we analyse the roleof Space Information Networks (SINs) in supporting MTC, in this work. Horizontal solutions are analysed herein in order to allow interworkingby acting as relay entities among different protocol stacks and services, vertically implemented over different network segments. We analyse thestill pending challenges hampering interworking, and propose a possibleprotocol stack for M2M/IoT communications based on the oneM2M standard. Eventually, this paper compares the performance achievableby using two of the most diffused application protocols, ConstrainedApplication Protocol (CoAP) and Message Queuing Telemetry Transport(MQTT), shedding light on their efficiency and differences.

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges

This work presents the ongoing development of a simulation/emulation framework for real-time multimedia transmissions in multi-channel scenarios. The proposed software engine can be used to simulate the aggregation of multiple network links delivering a video flow from a video source, such as a camera mounted on-board an Unmanned Aerial Vehicle (UAV), to the indented receiver, such as the pilot on the ground. The software engine can also be used in real testbeds exploiting multiple physical links, such as 4G/5G cellular connections. The main aim of this novel software framework is to support the design, development, and test of different scheduling strategies to achieve real-time, good quality and fluidity, energy-efficient multimedia transmissions, by selecting the optimal subset of network channels able to meet the target Quality of Experience (QoE) at the receiver.

As the Internet of Things (IoT)/machine-to-machine market is rapidly growing, a special role is expected to be played by satellite communications in that they offer ubiquitous coverage and therefore enable typical monitoring, telemetry, and control services also in areas with a poor terrestrial infrastructure. In this respect, the case of massive IoT devices deployment calls for random access solutions, which have been long analyzed by the scientific satellite community in the last ten years. This article further elaborates on the relation between the normalized offered load and the achievable performance in terms of packet loss rate, which was not much addressed so far at high loads. The proposed theoretical framework has been validated through extensive simulation campaigns, which show an excellent match at different loads and number of interfering packets configurations, by significantly improving the results achievable through other existing theoretical frameworks.

The historical buildings of a nation are the tangible signs of its history and culture. Their preservation deserves considerable attention, being of primary importance from a historical, cultural, and economic point of view. Having a scalable and reliable monitoring system plays an important role in the Structural Health Monitoring (SHM): therefore, this paper proposes an Internet Of Things (IoT) architecture for a remote monitoring system that is able to integrate, through the Virtual Reality (VR) paradigm, the environmental and mechanical data acquired by a wireless sensor network set on three ancient buildings with the images and context information acquired by an Unmanned Aerial Vehicle (UAV). Moreover, the information provided by the UAV allows to promptly inspect the critical structural damage, such as the patterns of cracks in the structural components of the building being monitored. Our approach opens new scenarios to support SHM activities, because an operator can interact with real-time data retrieved from a Wireless Sensor Network (WSN) by means of the VR environment.

We consider an Underwater Acoustic Network (UAN) where each node is equipped with a suite of signals and so there is the flexibility to aim for different bit rates at each transmission slot. A Cross-Entropy (CE) centralized algorithm is explored to optimize the combination of modulation scheme and transmission power level in the presence of unreliable channels. Optimization metrics such as throughput, energy per bit, latency and their combination are considered. The motivation for this research stems from the fact that surveillance networks using battery-powered Autonomous Underwater Vehicles (AUVs) need to be able to promptly deliver critical data while prolonging their lifetime and reducing the footprint of their transmissions. The proposed strategy has been validated by post-processing thousands of acoustic signals recorded during the Littoral Acoustic Communications Experiment 2017 (LACE17) sea trial in the Gulf of La Spezia, Italy. Our analysis shows the trade-off between the bit rate and the transmission power given the selected optimization metrics. The solution computed when combining all the considered metrics makes possible to improve up to three times the throughput performance and up to one order of magnitude the energy consumption with respect to considering single other optimization metrics.

Providing reliable and efficient connectivity for maritime systems is a key objective to enable new services and to offer anytime-anywhere communication solutions to vessels operating in remote areas (e.g. oceans). Such a task has recently become quite compelling because of the increasing maritime traffic, which should be supported by an information distribution infrastructure in order to guarantee efficient data communication among vessels and control centres on land. To this end, satellites are the perfect candidates for achieving such an objective. This paper focuses on the case of messaging from moving vessels to fixed control centres on land by means of advanced channel random access schemes exploiting time diversity. In more detail, the paper extends an existing theoretical framework to evaluate the packet loss probability in Framed Slotted ALOHA systems by delving into the probability distribution of both colliding users within a frame and their replicas in time slots. The theoretical framework is validated through simulations campaigns, showing a good match between analytical and simulated results.

This work describes a real testbed for enabling Unmanned Aerial Vehicles (UAVs)-to-ground real-time video streaming. The aim is in providing a video feed to the pilot on the ground for Beyond Visual Line of Sight (BVLoS) operations exploiting cellular networks in urban/suburban areas. Towards this aim, multipath communications are used in a multi-operator setup to counteract the intermittent network coverage in urban and suburban ares. The main requirements are: low latency, and a continuous video stream of reasonable quality. We rely on both upper-layer Forward Error Correction (FEC) techniques and link diversity, so to increase the probability of a fluid video playback with acceptable quality. We report per-link statistics, collected during field trials, of three different cellular operators, to analyse the impact of using a set of physical links as a single logical one on a RTP-based video streaming. In our tests, such a setup has provided a good level of performance.

Radio-Frequency based device-free localisation systems are able to pinpoint people's location in a given area without their cooperation. They work by analysing the perturbations that the presence of a person causes on the communications exchanged by a high number of radio devices installed around the area. Typical numbers are some tens of devices for an area the size of a single-family house, with an accuracy around one meter and a high sensitivity to even the slightest movement. The literature about device-free localisation systems typically concentrates on improving sensitivity, accuracy and discriminating power, without worrying too much about the number of involved radio devices. In this paper, for the first time, we demonstrate that device-free localisation can work with reduced performance with as low as four anchors in an environment comRadio-Frequency based device-free localisation systems are able to pinpoint people's location in a given area without their cooperation. They work by analysing the perturbations that the presence of a person causes on the communications exchanged by a high number of radio devices installed around the area. Typical numbers are some tens of devices for an area the size of a single-family house, with an accuracy around one meter and a high sensitivity to even the slightest movement. The literature about device-free localisation systems typically concentrates on improving sensitivity, accuracy and discriminating power, without worrying too much about the number of involved radio devices. In this paper, for the first time, we demonstrate that device-free localisation can work with reduced performance with as low as four anchors in an environment composed by two wide rooms. Being able to use so few anchors opens the possibility of several use cases where installing tens of devices is not desirable.posed by two wide rooms. Being able to use so few anchors opens the possibility of several use cases where installing tens of devices is not desirable.

International audience; The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillance module. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.

Nowadays, Machine-to-Machine and Internet of Things traffic sources puts the terrestrial networks under great pressure. While 5G is still on its way, satellites are used to deliver a fraction of such an enormous traffic rate. In this work, we investigate the use of the Constrained Application Protocol (CoAP) to reliably deliver Machine-to-Machine and Internet of Things traffic in a push fashion, which also implements a Selective Repeat Automatic Repeat reQuest and a sender-based variant of the TCP Friendly Rate Control protocol. We aim at providing an analytical model to evaluate the working point of the system in non-saturated conditions as a function of the MAC parameters in use, when such a closed-loop congestion control mechanism is in use over a random access satellite channel. The proposed analytical model is then validated against simulation results, showing a good precision.

Unmanned Aerial Vehicles (UAVs) are attracting an in- creasing interest from both the industrial and the research fields, because of the large number of scenarios and applications that they can support. One of the big challenges of the next future is the use of UAV swarms, in order to exploit the advantages that coordinated actions of multiple drones can provide. In this work, we propose an analytical framework to evaluate the probability of a reliable command and control message delivery from a Ground Control Station to a UAV swarm via satellite, also exploiting intra-swarm gossiping.

Device-free indoor localisation based on received signal strength (RSS) is unobtrusive and cheap. In a world where most environments are rich in ubiquitous small radio transmitters, it has the potential of being used in a `parasitic' way, by exploiting the transmissions for localisation purposes without any need for additional hardware installation. Starting from state of the art, several steps are needed to reach this aim, the first of which are tackled in this study. The most promising algorithms from the literature are used to experiment in a real-world environment and with a rigorous measurement and analysis framework. Their positioning error performance is analysed versus number and position of devices. The original results obtained show that the currently available RSS-based device-free indoor localisation methods may be well suited to serve as a basis for providing a cheap localisation service in smart environments rich in Internet of things radio devices.

We are the witnesses of a technological ascent accomplished by the Internet of Things (IoT) in the new era of shared informatization. Smart cities make up one of the major areas of IoT applications. Despite several definitions for this new concept, the aim is to achieve a better use of public resources by increasing the quality of services and decreasing the costs of public services. Some initiatives of smart cities around the world have become reality, such as in [1]. Many smart components compose a smart city, such as transportation, energy, education, and buildings. A smart building includes services offered to the occupants, resources distributed to the city, and, in the case of historical buildings, information provided to municipalities about their state of health.

This paper presents the design framework, in which a Decision Support System (DSS) tool has been developed to assist the bridge operator during a challenging navigation condition. The present project would be another step for using state of the art, IT devices and hardware, to increase safety at sea mainly focusing on both collision and grounding avoidance. In this paper, the modules to detect an obstacle and to calculate the evasive route are based on a customized simulation model: such a model is able to represent the dynamic behaviour of a ship, including hydrodynamics, propulsion, and control effects. The suggested route selected by the decision support system and some environmental parameters coupled with some of the ship parameters are visualized on a smart “virtual bridge” exploiting virtual reality techniques. A suitable graphical interface has been developed and installed, in order to enhance the situation awareness. The project also focuses on the communication architecture, which relies on a publish-subscribe paradigm and is responsible to forward ship control parameters both to the virtual bridge and towards an ashore control centre, either for supervising or for remote control. Overall, both the potentiality and the limits of the proposed system have been critically discussed.

Unmanned Aerial Vehicles (UAVs) are getting momentum. A growing number of industries and scientific institutions are focusing on these devices. UAVs can be used for a really wide spectrum of civilian and military applications. Usually these devices run on batteries, thus it is fundamental to efficiently exploit their hardware to reduce their energy footprint. A key aspect in facing the “energy issue” is the exploitation of properly designed solutions in order to target the energy- and hardware-constraints characterising these devices. However, there are not universal approaches easing the implementation of ad-hoc solutions for UAVs; it just depends on the class of problems to be faced. As matter of fact, targeting machine-learning solutions to UAVs could foster the development of a wide range of interesting application. This contribution is aimed at sketching the challenges deriving from the porting of machine-learning solutions, and the associated requirements, to highly distributed, constrained, inter-connected devices, highlighting the issues that could hinder their exploitation for UAVs.

A precise and dynamic visual coverage of a given area is an essential task in many smart contexts, ranging from civil communities to military applications. Due to the last years advancement in hardware miniaturization and efficiency, area coverage is often performed with a combination of static and moving devices, such as unmanned aerial vehicles (drones). Drones are useful to cope with the highly unpredictability and dynamicity of environments, but require specific and efficient solutions toward and efficient area coverage. In this paper we proposes an initial work toward a drone-based approach for the task of area coverage. In particular, we focus our analysis on the following points: (i) decentralized consensus for movement planning, and (ii) the integration of cloud computing infrastructures and technologies for computation offloading, both for image analysis and movement planning.

Nanosatellites and unmanned aerial vehicles are attracting more and more the interest of both industrial and research fields. They are low-cost and easy deployable items, therefore their use is expected to quickly grow in the next few years. This work proposes a survey on the network architectures and the applications for nanosatellite swarms and constellations, as well as for flying ad hoc networks, by character- izing distinctive features and issues yet to be resolved in order to take advantage from both technologies in a joint fashion.

The EU EPBD recast regulation marked the application of the net zero energy building (Net ZEB) concept in all fields of building construction in Europe as a building able to generate as much energy as it consumes over a selected time frame. A more detailed insight is however needed, as even if a building achieves a long-term energy balance between energy generated and consumed, smaller time scales must also be considered. For example, from the utility’s point of view, if a Net ZEB is a heavy consumer in the winter, it will appear to be quite similar to a conventional building, requiring the use of additional generation. The increase in the generation-load match means reducing the stress to the energy grid, the peak system energy required, and the wasted energy, thus avoiding unnecessary generation and, consequently, limiting carbon-related emissions. In this paper, the authors address the problem of determining the optimal storage size starting from the data of an Italian nearly net zero energy building that produced a detailed database on generated and consumed electricity. The study presented in this paper analyses the following steps: modelling of energy consumption through non-parametric estimators, modelling of energy generation through detailed models available in the literature, quantification of the load match levels for existing case studies, analysis of the state of charge (SOC) for a given model of the energy consumption probability distribution, and the identification of the minimum size of the storage required to improve energy load match. The results show that even when selecting unfavourable boundary conditions it could be possible to lower the energy import from the electricity grid by approximately 40%, that can possibly rise to more than 90% during summer.

Device-free localization algorithms attract, among others, the attention of researchers working in the Ambient Assisted Living (AAL) scenarios, where the target user might not be able or willing to wear any devices. We concentrate on systems that exploit the Received Signal Strength indicator coming from wireless devices whose position is known, called anchors. In this paper we select and test the main device-free localization solutions and experimentally compare their performance using a smaller number of anchors than commonly found in the literature. We illustrate the procedure used to validate our comparing procedure and we give suggestions on usability in the application scenarios typical of AAL. To the best of our knowledge, this is the first direct comparison between different device-free algorithms using the same input data for all of them, and the first one that compares their performance with a varying number of anchors. Thanks to the characteristics of our comparison procedure, we can make suggestions about the more appropriate algorithms to use for different kinds of applications.

The direct consequence of the rapid growth of the demand for computational power by cloud based-applications has been the creation of an increasing number of large-scale data centres. In such a competitive market, each Cloud vendor needs to lower the price of the offered resources in order to increase its shares. This is done by reducing the cost associated with the execution of the users' applications, but still maintaining an adequate quality of Service. To reach this goal, each Cloud infrastructure needs to self-organise, by efficiently allocating its own resources. The complexity of the problem (exact solutions are NP-complete) calls for new, adaptive and highly-automated approaches that, at the arrival of new resource requests, are able to autonomously estimate potential resource consumptions. Hence the resource managemente subsystem is tuned up just keeping the associated costs as low as possible. This paper represent our contribution to this problem. We propose an approach that exploits the Cross-Entropy minimisation method to forecast the impact of different resource allocations on a Cloud infrastructure, assuming that many objective functions need to be optimised. Yet, in order to select the best allocation among those presented here, we make use of an adaptive, fast, and low resource- demanding decision-making strategy, derived from models coming from the cognitive science field. Preliminary results show the effectiveness of the proposed solution.

This work starts from the analysis of the literature about the Random Access protocols with contention resolution, such as Contention Resolution Diversity Slotted Aloha (CRDSA), and introduces a possible enhancement, named Generalized Encoding Contention Resolution Diversity Slotted Aloha (GE-CRDSA). The GE-CRDSA aims at improving the aggregated throughput when the system load is less than 50%, playing on the opportunity of transmitting an optimal combination of information and parity packets frame by frame. This paper shows the improvement in terms of throughput, by performing traffic estimation and adaptive choice of information and parity rates, when a satellite network undergoes a variable traffic load profile.

Several privacy concerns about the massive deploy- ment of smart meters have been arisen recently. Namely, it has been shown that the fine-grained temporal traces generated by these meters can be correlated with different users behaviors. A new architecture, called multi-party metering, for enabling privacy-preserving analysis of high-frequency metering data without requiring additional complexity at the smart meter side is here proposed. The idea is to allow multiple entities to get a share of the high-frequency metering data rather than the real data, where this share does not reveal any information about the real data. By aggregating the shares provided by different users and publishing the results, these entities can statistically analyze the consumption data, without disclosing sensitive information of the users. In particular, it is proposed how to implement a user profiling clustering mechanism in this architecture. The envisaged solution is tested on synthetic electricity consumption data and real gas consumption data.

This work starts from the analysis of the literature about the Random Access protocols with contention resolution as Contention Resolution Diversity Slotted Aloha (CRDSA) and introduces a possible enhancement, named Generalized Contention Resolution Diversity Slotted Aloha (GE-CRDSA). The GE-CRDSA aims to improve the aggregated throughput when the system load is less than 50%, playing on the opportunity of transmitting an optimal combination of information and parity packets frame by frame.

Wireless mesh networks are difficult to be characterized, especially under multi-hop traffic streams. The problem is that the local view of the channel and the correlation between the buffers of consecutive nodes in a stream path make complicated the identification of the contention level perceived by each station along the time. Such a figure is used in the models based on the so called decoupling assumption for evaluating the final scheduling of simultaneous channel access grants. In this paper we propose a simplified mesh network model focused on capturing the correlation due to the network topology and traffic routes rather than the access protocol state at each node. To this purpose, we consider the whole collection of node states (in terms of transreceiver activity and buffer length) as network model state. Despite of the large state dimension, the model complexity is very low thanks to the strong simplifications introduced in modeling the access protocol. Numerical results show that the approach is promising and suitable for those network scenarios (limited number of nodes, multi-hop traffic streams) in which previous models results not appropriate or too complex.

With the evolution of telecommunication networks and of their services the role of service provider is changed, so nowadays there is a coexistence of Network Operators and Virtual Operators. The difference between these players is not in the way they offer a service but primarily in their economic objectives and risk attitudes. Essentially, Network Operators own their infrastructures and typically have to sustain both fixed costs (CAPEX) and recurrent costs (OPEX), while Virtual Operators may have a simpler cost structure, mainly consisting of OPEX for the hire of network resources. Since these two operators can provide the same service in two different markets, their objectives differ substantially: on one hand, Network Operators aim to recover their costs with traditional pricing schemes (flat or time based), while the goal of Virtual Operators is to stay in the market, trying to attract as more users as they can through innovative pricing schemes (time varying, congestion pricing) providing, at the same time, QoS levels comparable to the corresponding ones of Network Operators. From above considerations we can understand the importance for Virtual Operators and for Regulatory Agencies to develop new pricing schemes. To develop a new pricing scheme it is necessary to study a model that shows vs. time the occupancy of network resources and moreover we present a method to estimate the final price of the service offered from Virtual Operator, for a given occupancy state of network resources, which is evaluated by American Options Pricing Scheme. This manner it is possible to address the problems of co-existence in the same communication market of these two service providers and the one of prevention of possible arbitrage, driven by different pricing schemes.

In the recent years, the role of service provider has split into two broad categories: Network Operators, offering services over their networks, and Virtual Operators, providing services over leased resources. What differentiates these players is to a minor extent a matter of enhanced service offer, and primarily a matter of economic objectives and risk attitudes. Essentially, Network Operators own their infrastructures and typically have to sustain both fixed costs (CAPEX) and recurrent costs (OPEX), while Virtual Operators may have a simpler cost structure, mainly consisting of OPEX for hiring the network. Correspondingly, the objectives of these players may differ profoundly: on one hand, infrastructure operators are aimed at recovering their costs at a limited risk with traditional pricing schemes (flat or time based), while the goal of Virtual Operators is to stay in the market, trying to attract as many users as they can over their virtual network through new pricing schemes (time varying, congestion pricing) and at the same time providing comparable QoS levels as Network Operators to be competitive. The paper addresses the problem of co-existence in the market of these two service providers and how arbitrage opportunities, driven by different pricing schemes, can be prevented with the adoption of an American option based pricing scheme by Virtual Operators.