Accurate localization is a key enabler for advanced autonomous applications in UAVs and Internet of Things (IoT) networks, particularly in outdoor scenarios where GPS may be unavailable, unreliable, or insufficiently precise. Ultra-Wideband (UWB) technology offers high-accuracy ranging capabilities and has shown strong performance in indoor environments, but its deployment in large-scale outdoor networks remains underexplored. This work investigates the presence of range-dependent bias in long-distance UWB measurements and evaluates its impact on multilateration-based localization systems for UAVs. We present a field measurement campaign with specifically designed UWB testing boards over distances up to 1.2 km, revealing that all tested modules exhibit a growing positive bias, up to 20 cm at the maximum range. To assess how such bias affects positioning, we implement a simulation framework modeling a UWB localization network. We analyze the effect of varying anchor spacing and compare ideal measurements to biased ones using the measured trend. Our results show that geometric anchor deployment can mitigate some of the bias effects, and that awareness of systematic errors is crucial for the design of scalable, accurate outdoor localization networks.

Spectrum monitoring has become essential to ensure the reliability of wireless communications. Spectrograms provide a visual time-frequency representation of the radio signal, which analyzed with deep learning tools of the visual world, enables the identification of coexistence problems, interference and critical propagation conditions. In this paper, we propose a spectrum analysis system able to detect 5G control signals. Our methodology is based on the acquisition of 16 -bit IQ signals on extended time scales, transformed and analyzed as images. The paper discusses issues related to dataset creation for model training, as well as the possibility of using pre-trained models through transfer learning and few-shot learning to detect signals in real environments. To this end, a proof of concept of the proposed methodology was conducted, based on a dataset created from real-world acquisitions used to train a YOLOv8x model. The results show that the methodology is very promising, but highlight the need for significantly more examples to train the model.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 38%) compared to the total one radiated by the smartphone.

The Google's Loon$^{TM}$initiative aims at covering rural or underdeveloped areas via fleets of high-altitude balloons supporting LTE connectivity. But how effective and stable can be the coverage provided by a network deployed via propulsion-free balloons, floating in the sky, and only loosely controllable through altitude variations? To provide some insights on the relevant performance and trade-offs, in this paper we gather real-world data from publicly available flight tracking services, and we analyze coverage and service stability in three past deployment scenarios. Besides employing a variety of metrics related to spatial and temporal coverage, we also assess service continuity, by also leveraging recently proposed “meaningful availability” metrics. While our analyses show that balloons are certainly a cost-effective way to provide a better-than-nothing and delay-tolerant service, there is yet no empirical evidence that an increase in the number of overlapping balloons may be rewarded with a substantial performance increase — in other words, we suspect that guaranteeing coverage and service stability levels comparable to that of a terrestrial cellular network is a challenging goal.

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population’s exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than$20 \text {BSs/km}^{2}$.

In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

We target the planning of a 5G cellular network under 5G service and ElectroMagnetic Fields (EMFs) constraints. We initially model the problem with a mixed integer linear programming (MILP) formulation. The pursued objective is a weighed function of next-generation Node-B (gNB) installation costs and 5G service coverage level from a massive multiple input multiple output (MIMO) system. In addition, we precisely model restrictive EMF constraints and we integrate scaling parameters to estimate the power radiated by 5G gNBs. Since the considered planning problem is NP-Hard, and therefore very challenging to be solved even for small problem instances, we design an efficient heuristic, calledPLATEA, to practically solve it. Results, obtained over a realistic scenario that includes EMF exposure from pre-5G technologies (e.g., 2G, 3G, 4G), prove that the cellular planning selected byPLATEAensures 5G service and restrictive EMF constraints. However, we demonstrate that the results are strongly affected by: i) the relative weight between gNB installation costs and 5G service coverage level; ii) the scaling parameters to estimate the exposure generated by 5G gNBs; iii) the amount of exposure from pre-5G technologies; and iv) the adopted frequency reuse scheme.

A very popular theory circulating among non-scientific communities claims that the massive deployment of Base Stations (BSs) over the territory, a.k.a. cellular network densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency “Pollution” (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: cellular network densification triggers an RFP decrease (up to three orders of magnitude) when the radiated power from the BS is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.

The debate over whether electromagnetic fields (EMFs) exposure poses a danger to human health is recurring and goes back centuries to when our society first began to rely on electricity [1].

Small cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones. However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites. To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection. In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network. We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components. In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances. We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

A common concern among the population is that installing new 5G Base Stations (BSs) over a given geographic region may result in an uncontrollable increase of Radio-Frequency “Pollution” (RFP). To face this dispute in a way that can be understood by the layman, we develop a very simple model, which evaluates the RFP at selected distances between the user and the 5G BS locations. We then obtain closed-form expressions to quantify the RFP increase/decrease when comparing a pair of alternative 5G deployments. Results show that a dense 5G deployment is beneficial to the users living in proximity to the 5G BSs, with an abrupt decrease of RFP (up to three orders of magnitude) compared to a sparse deployment. We also analyze scenarios where the user equipment minimum detectable signal threshold is increased, showing that in such cases a (slight) increase of RFP may be experienced.

We focus on the ElectroMagnetic Field (EMF) exposure safety for people living in the vicinity of cellular towers. To this aim, we analyze a large dataset of long-term EMF measurements collected over almost 20 years in more than 2000 measurement points spread over an Italian region. We evaluate the relationship between EMF exposure and the following factors: (i) distance from the closest installation(s), (ii) type of EMF sources in the vicinity, (iii) Base Station (BS) technology, and (iv) EMF regulation updates. Overall, the exposure levels from BSs in the vicinity are below the Italian EMF limits, thus ensuring safety for the population. Moreover, BSs represent the lowest exposure compared to Radio/TV repeaters and other EMF sources. However, the BS EMF exposure in proximity to users exhibits an increasing trend over the last years, which is likely due to the pervasive deployment of multiple technologies and to the EMF regulation updates. As a side consideration, if the EMF levels continue to increase with the current trends, the EMF exposure in proximity to BSs will saturate to the maximum EMF limit by the next 20 years at a distance of 30 meters from the closest BS.

We consider the IPv6 Segment Routing (SRv6) technology for Service Function Chaining of Virtual Network Functions (VNFs). Most of the VNFs are legacy VNFs (not aware of the SRv6 technology) and expect to process traditional IP packets. An SR proxy is needed to support them. We have extended the implementation of SRv6 in the Linux kernel, realizing an open source SR-proxy, referred to as SRNK (SR-Proxy Native Kernel). The performance of the proposed solution (SRNKvl) has been evaluated, identifying a poor scalability with respect to the number of VNFs to be supported in a node. Therefore we provided a second design (SRNKv2), enhancing the Linux Policy Routing framework. The performance of SRNKv2 is independent from the number of supported VNFs in a node. We compared the performance of SRNKv2 with a reference scenario not performing the encapsulation and decapsulation operation and demonstrated that the overhead of SRNKv2 is very small, on the order of 3.5%.

Summary The highly dynamic nature of the current network traffics makes the network managers to exploit the flexibility of the state‐of‐the‐art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN‐MPLS. In this paper, a new traffic engineering architecture for SDN‐MPLS network is proposed. To this end, OpenFlow‐enabled switches are applied over the edge of the network to improve flow‐level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re‐assigns flows to the Label‐Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow‐level re‐routing is insufficient, the proposed scheme re‐computes and re‐creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems, ie, i) flow re‐routing and ii) LSP re‐creation, and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN‐MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks.

Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.

With the growing popularization of Electric Vehicles (EVs), charging management has become an increasingly important research problem in smart cities. Different from plug-in charging technology, we alternatively enable the battery switch technology to provide fast EV charging (reduce the service waiting time from tens of minutes to a few minutes), by facilitating the switchable (fully-recharged) batteries maintained at CSs and also the batteries cycling procure to refresh their availability. Nevertheless, potential hot spot may still happen at CSs, due to running out of switchable batteries as well as long batteries charging queue. With this concern, we next propose a reservation based EV charging management scheme to alleviate such situation, considering EVs' anticipated charging reservations (including arrival time, expected charging time) to coordinate EVs' charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our enabling technology, in terms of minimized waiting time for the battery switch as the benefit of EV drivers, and higher number of batteries switched as the benefit of CSs.

This paper focuses on analyzing some key business aspects that arise during the deployment of key novel enabling technologies for 5G systems. Results are taken out of two EU-funded ongoing research projects, namely 5G-MiEdge and Superfluidity, which largely exploit mmWave communications and softwarization concepts for 5G networks. We initially provide a stakeholder analysis of the 5G ecosystem, as well as a Strengths Weaknesses Opportunities Threats (SWOT) analysis of a couple of key and most promising 5G use cases. For one use case also a preliminary business model is provided. Then we detail an economic 5G cost model, which is able to provide indications on the profitability of 5G networks. Finally, we highlight the planned future works.

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (μeNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of μeNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After formulating the optimal problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer as well as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE).

We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers' CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several scenarios from a real CDC, show that MECDC largely outperforms two reference algorithms, which instead either target the load balancing or the energy consumption of the servers.

5G is expected to become the dominant technology in the forthcoming years. In this work, we consider a 5G Superfluid network, as an outcome of the H2020 project SUPERFLUIDITY. The project exploits the concept of Reusable Functional Block (RFB), a virtual resource that can be deployed on top of 5G physical nodes. Specifically, we focus on the management of the RFBs in a Superfluid network to deliver a high definition video to the users. We design an efficient algorithm, called P5G, which is based on Particle Swarm Optimization (PSO). Our solution targets different Key Performance Indicators (KPIs), including the maximization of user throughput, or the minimization of the number of used 5G nodes. Results, obtained over a representative scenario, show that P5G is able to wisely manage the RFBs, while always guaranteeing a large throughput to the users.

We target the evaluation of a Superfluid 5G network from an economic point of view. The considered 5G architecture has notably features, such as flexibility, agility, portability and high performance, as shown by the H2020 SUPERFLUIDITY project. The proposed economic model, tailored to the Superfluid network architecture, allows to compute the CAPEX, the OPEX, the Net Present Value (NPV) and the Internal Rate of Return (IRR). Specifically, we apply our model to estimate the impact for the operator of migrating from a legacy 4G to a 5G network. Our preliminary results, obtained over two realistic case studies located in Bologna (Italy) and San Francisco (CA), show that the monthly subscription fee for the subscribers can be kept sufficiently low, i.e., typically around 5 [USD] per user, while allowing a profit for the operator.

The distinguishing feature of the Fog Computing (FC) paradigm is that FC spreads communication and computing resources over the wireless access network, so as to provide resource augmentation to resource and energy-limited wireless (possibly mobile) devices. Since FC would lead to substantial reductions in energy consumption and access latency, it will play a key role in the realization of the Fog of Everything (FoE) paradigm. The core challenge of the resulting FoE paradigm is tomaterialize the seamless convergence of three distinct disciplines, namely, broadband mobile communication, cloud computing, and Internet of Everything (IoE). In this paper, we present a new IoE architecture for FC in order to implement the resulting FoE technological platform. Then, we elaborate the related Quality of Service (QoS) requirements to be satisfied by the underlying FoE technological platform. Furthermore, in order to corroborate the conclusion that advancements in the envisioned architecture description, we present: (i) the proposed energy-aware algorithm adopt Fog data center; and, (ii) the obtained numerical performance, for a real-world case study that shows that our approach saves energy consumption impressively in theFog data Center compared with the existing methods and could be of practical interest in the incoming Fog of Everything (FoE) realm.

We consider the problem of evaluating the performance of a 5G network based on reusable components, called Reusable Functional Blocks (RFBs), proposed by the Horizon 2020 SUPERFLUIDITY project. RFBs allow a high level of flexibility, agility, portability and high performance. After formally modelling the RFB entities and the network physical nodes, we optimally formulate the problem of maximizing different Key Performance Indicators (KPIs) on an RFB-based network architecture, in which the RFBs are shared among the nodes, and deployed only where and when they are really needed. Our results, obtained by solving the proposed optimization problem over a simple yet representative scenario, show that the network can be managed in a very efficient way. More in depth, the RFBs are placed into the nodes in accordance with the amount of requested traffic from users and the specific pursued KPI, e.g., maximization of user throughput or minimization of the number of used nodes. Moreover, we evaluate the relationship between the capacity of each node and the number of RFBs deployed on it.

We consider the problem of controlling the rate of failures triggered by fatigue processes of Base Stations (BSs) in cellular networks subject to Sleep Modes (SMs). Specifically, the increase of time spent in SM tends to decrease the BS failure rate by following, e.g., the Arrhenius law. However, the transitions between the power states tend to increase the BS failure rate, which can be predicted by the Coffin-Manson model. In this context, the energy savings triggered by SMs would not be economically useful if the BS failure rate were increased too much. Our goal is therefore to tackle the problem of minimizing the BS failure rate in a cellular network subject to SMs. After showing that the optimal formulation of the problem is NP-Hard, we propose a new algorithm, named LIFE, to practically solve it. We run LIFE on different scenarios (driven by LTE and legacy UMTS technologies). Our results show that LIFE outperforms two previous energy-aware algorithms, which instead do not take into account the BS failure rate. Specifically, our solution is able to achieve up to 40 percent of power saving at night, without a strong penalty in the BS failure rate.

We propose a framework to manage the link lifetime in an IP backbone network by exploiting the sleep mode (SM). In particular, when an SM feature is available, two different effects coexist: during the SM state, the lifetime tends to be increased and however, when the link changes its power state (from SM to full power or vice-versa), the lifetime tends to be decreased. We, therefore, define an optimal formulation of the lifetime-aware network problem. Moreover, we propose a heuristic, called Acceleration Factor Algorithm, to practically manage the device lifetime. We solve the problem both optimally and with our heuristic, considering two representative case studies. Results show that our approach outperforms the previous energy-aware algorithms, which instead do not consider the lifetime decrease triggered by the power state change. Thus, we argue that a lifetime-aware network management should be pursued when deciding to set an SM state for each device in an Internet Service Provider network.

Traffic variation heavily impacts energy savings achieved by putting Line Cards (LCs) into Sleep Mode (SM) in core Internet Protocol (IP)-over-Wavelength Division Multiplexing (WDM) networks. We propose a simple traffic model covering temporal and spatial traffic variation and investigate the impact of (controlled) traffic variations on energy savings achieved with the Energy Watermark Algorithm (EWA) as well as on devices lifetime in realistic network scenarios. The results indicate that spatial variation of traffic is no obstacle in saving energy in the networks. We point out the need for consistent routing schemes between the reference network (designed with a sophisticated Mixed-Integer Linear Programming (MILP) formulation in our case) and the solutions computed dynamically during network operation (with the lifetime-unaware EWA in our case).

In this work, we show that there are two effects impacting cellular networks owned by an operator as a consequence of the application of the green networking via Sleep Modes (SMs) state to Base Stations (BSs). On one side, it is possible to save money from the electricity bill by exploiting SMs. On the other hand, however, the SM state triggers a variation in the failure rate of the BS, and therefore in the replacement/reparation costs to fix it. We therefore derive a simple model to compute the total savings vs. costs for an operator network. Our results, obtained over 3G and 4G scenarios, show that there is a trade-off between the saving and the related costs. Moreover, we show that the costs incurred by the operator depend on different factors, including: i) the energy-aware strategy, ii) the specific technology adopted, and iii) the components used to build the BS.

We propose a dynamic and cooperative approach to support the desired Quality of Experience (QoE) of the mobile users in a heterogeneous cellular network. Specifically, we focus on a scenario in which a mobile video streaming service is provided to users. We then develop a mobile streaming traffic micro-to-macro offloading scheme based on a cross layer approach, in which we exploit the knowledge of the video structure at a proxy side. Our results, obtained over a detailed case study, show that the proposed approach is able to enable high quality video streaming services that are otherwise not feasible. Moreover, even a moderate portion of the macro bandwidth (typically ranging between 15% and 25% of the total bandwidth) is already able to serve users that have been offloaded from the small cell. Finally, we show that, when the offloading is based on a cross-layer approach, the QoE experienced by users (e.g., in terms of delay) is higher than in the case in which the offloading decision is solely based on lower layer parameters like the channel quality.

This paper targets the lifetime-aware management of a set of optical line amplifiers (OLAs) in an optical network exploiting sleep mode (SM) in order to save energy. We first present a simple model to predict the OLA lifetime. We then provide different mixed-integer linear programming (MILP) formulations, which jointly consider energy saving and lifetime. The proposed MILP formulations are then solved on different realistic scenarios, by taking into account the spatial and temporal variations of traffic demands. Results show that our lifetime-aware approach outperforms classical energy saving ILP formulations, which instead tend to notably decrease the OLA lifetime. More important, the proposed approaches can achieve a good lifetime performance without consuming significantly more energy than purely energy-aware strategies.

We target the problem of managing the Base Stations (BSs) lifetime and their energy efficiency when a sleep mode (SM) is adopted. We first show that the BS lifetime is affected by two opposite effects: the duration of SM, which tends to increase the lifetime, and the SM frequency, which on the contrary decreases the lifetime. After optimally formulating the problem for a cellular network, we propose a new heuristic, called LIFE, to practically solve it. Our solution integrates the BS lifetime when SM decisions are considered. Results, obtained over an UMTS scenario and a LTE one, prove that LIFE outperforms two previous energy-efficient algorithms in terms of lifetime performance, since all the previous solutions tend to drastically reduce the BS lifetime. Moreover, we show that LIFE is able to save up to 40% of power during night in the long-term.

In this work we study the impact of energy-aware routing algorithms on IP backbone networks, focusing on the routing protocol transients due to network reconfiguration. We first propose the Green Partial Exportation (GPE) algorithm, which is fully compatible with the OSPF protocol and targets the reduction of the number of changed paths in the network; we also realize a green software router by integrating GPE in the Quagga routing suite. Then we define an experimental methodology to evaluate the effects of a green routing strategy on the network behavior, in terms of delay increase and packet loss. Finally, we evaluate our solution on an emulated testbed from a national telecom operator. Our results show a maximum increment of 320 ms for the RTT and a packet loss of 1.45% during the network transients. Moreover, GPE can be safely applied in the network with a time granularity of less than one minute.

We consider the effect of power state transitions on the lifetime of Base Stations (BSs) in a cellular network. In particular, we take into account the impact of putting in sleep mode the BS, and also the change of the radiated power. When the BS reduces its power consumption, its lifetime tends to increase, as a consequence of the temperature reduction. However, the change in the power state triggers a negative effect which instead tends to reduce the BS lifetime. We therefore propose a model to evaluate the BS lifetime considering the two aforementioned effects, triggered either by the application of a sleep mode state or a change in the radiated power. Our results, obtained over a representative case study, indicate that the BS lifetime may be negatively affected when power state transitions take place. Therefore, we argue that the lifetime should be considered in the process of deciding how and when to change from a power state to another one.

We study the problem of reducing power consumption in an Internet Service Provider (ISP) network by designing the content distribution infrastructure managed by the operator. We propose an algorithm to optimally decide where to cache the content inside the ISP network. We evaluate our solution over two case studies driven by operators feedback. Results show that the energy-efficient design of the content infrastructure brings substantial savings, both in terms of energy and in terms of bandwidth required at the peering point of the operator. Moreover, we study the impact of the content characteristics and the power consumption models. Finally, we derive some insights for the design of future energy-aware networks.

The problem of reducing power consumption in backbone networks by adopting sleep modes has been extensively studied in the literature. In contrast to previous work, we forecast power consumption and traffic in two operator networks (France Telecom (FT) and Telefo´nica Investigacio´n y Desarrollo (TID)) for the year 2020, and consider both Single Line Rates (SLRs) and Mixed Line Rates (MLRs) deployments. Given these realistic scenarios, the set of devices in sleep mode is computed autonomously on each network node by a simple power-aware algorithm using only local information as input and requiring no rerouting. Results show that the savings obtained by adopting sleep modes are significant, even if power efficiency (W/Gbps) is increased. Moreover, we show that networks using MLR always consume less energy than SLR-based networks when the power-aware algorithm is applied to the considered scenarios.

The energy consumption of network devices, and, as a consequence, of communication networks, is generally independent from their level of utilization, which results in a waste of energy when the network is lightly loaded. Ideally the consumption of a network should be proportional to the amount of traffic it conveys. The most straightforward way to enforce such a proportionality between the network energy consumption and its utilization level, is to dynamically adapt the status of network devices to the load, forcing a subset of them to enter a sleep state during the low activity periods. We present in this paper an algorithm to dynamically put links into a sleep state, based on a cooperative-game approach, named “L-Game”. Our approach decides which links can be switched off based on a measure of the criticality of each link expressed as its Shapley value. This measure combines topological aspects and traffic conditions. Simulation results on real network scenarios show that our solution achieves a better trade off between energy saving and Traffic Engineering than other legacy approaches.

According to several studies, the power consumption of the Internet accounts for up to 10% of the worldwide energy consumption and is constantly increasing. The global consciousness on this problem has also grown, and several initiatives are being put into place to reduce the power consumption of the ICT sector in general. In this paper, we face the problem of minimizing power consumption for Internet service provider (ISP) networks. In particular, we propose and assess strategies to concentrate network traffic on a minimal subset of network resources. Given a telecommunication infrastructure, our aim is to turn off network nodes and links while still guaranteeing full connectivity and maximum link utilization constraints. We first derive a simple and complete formulation, which results into an NP-hard problem that can be solved only for trivial cases. We then derive more complex formulations that can scale up to middle-sized networks. Finally, we provide efficient heuristics that can be used for large networks. We test the effectiveness of our algorithms on both real and synthetic topologies, considering the daily fluctuations of Internet traffic and different classes of users. Results show that the power savings can be significant, e.g., larger than 35%.

In this paper, Internet data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose a novel framework, named "NetCluster", suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of our campus LAN and discuss the network characteristics as seen at the vantage point.

According to several studies, the power consumption of the Internet accounts for up to 10% of the worldwide energy consumption, and several initiatives are being put into place to reduce the power consumption of the ICT sector in general. To this goal, we propose a novel approach to switch off network nodes and links while still guaranteeing full connectivity and maximum link utilization. After showing that the problem falls in the class of capacitated multi-commodity flow problems, and therefore it is NP-complete, we propose some heuristic algorithms to solve it. Simulation results in a realistic scenario show that it is possible to reduce the number of links and nodes currently used by up to 30% and 50% respectively during off-peak hours, while offering the same service quality.