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This work studies a well-known shared-cache coded caching scenario where each cache can serve an arbitrary number of users, analyzing the case where there is some knowledge about such number of users (i.e., the topology) during the content placement phase. Under the assumption of regular placement and a cumulative cache size that can be optimized across the different caches, we derive the fundamental limits of performance by introducing a novel cache-size optimization and placement scheme and a novel information-theoretic converse. The converse employs new index coding techniques to bypass traditional uniformity requirements, thus finely capturing the heterogeneity of the problem, and it provides a new approach to handle asymmetric settings. The new fundamental limits reveal that heterogeneous topologies can in fact outperform their homogeneous counterparts where each cache is associated to an equal number of users. These results are extended to capture the scenario of topological uncertainty where the perceived/estimated topology does not match the true network topology. This scenario is further elevated to the stochastic setting where the user-to-cache association is random and unknown, and it is shown that the proposed scheme is robust to such noisy or inexact knowledge on the topology., 39 pages

The advance of mobile network technologies and components heavily relies on data-driven techniques. This is especially true for fifth generation (5G) and the upcoming sixth generation (6G) networks, as the optimization of network components and protocols is expected to be fueled by artificial intelligence (AI) based solutions. When using real-world radio access measurement traces, the identity of individual users is not directly accessible because at runtime operation Base Stations (BSs) assign Radio Network Temporary Identifiers (RNTIs) to users. RNTIs are not bound to a user but are reused upon expiration of an inactivity timer, whose duration is operator dependent. This implies that, over time, multiple users are mapped to the same RNTI. In fact, the allocation of RNTIs to users is implemented in diverse and proprietary ways by operators and equipment vendors. Distinguishing individual users within the RNTI space is a non-trivial task and key to analyze traffic traces properly. In this paper, we make the following contributions: i) we propose and validate two complementary methodologies to identify the RNTI inactivity threshold, and we characterize ii) the RNTI allocation process of network operators, and iii) the user traffic patterns given the specific RNTI allocation process. Our study is based on a large dataset we collected from production BSs of several mobile network operators across five different countries. We find that there exist heterogeneous strategies for RNTI allocation that BSs dynamically use depending on the traffic load and daytime. We further observe that the RNTI expiration threshold is in the order of minutes, and demonstrate how using thresholds around 10 seconds, as in the vast majority of the literature, can bias subsequent analyses. Overall, our work provides an important step towards dependable mobile network trace analysis, and lays solid foundations to research relying on traffic traces for data-driven analysis. Comunidad de Madrid Ministerio de Ciencia e Innovación TRUE pub

The use of vector coded caching has been shown to provide important gains and, more importantly, to alleviate the impact of the file-size constraint, which prevents coded caching from obtaining its ideal gains in practical settings. In this work, we analyze the performance of vector coded caching in the massive MIMO regime, aiming at understanding the benefits that allowing users to cache a practical amount of data could bring to realistic settings in such massive MIMO regime. In particular, we separately consider two linear precoding schemes and analyze the corresponding throughput, for which we derive simple but precise upper and lower bounds. These bounds enable us to characterize the delivery speed-up gain over the uncoded caching setting when the CSI acquisition costs are taken into account. Numerical results demonstrate the tightness of the derived bounds and show a significant boost over uncoded caching and the standard cacheless setting. Comunidad de Madrid - Atracción de talento TRUE inpress

Call detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of processing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting, with a DBSCAN-based clustering approach, the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows. TRUE pub

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://github.com/GCGImdea/coronasurveys., During the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed toward case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic., At the time of writing this article, Corona Surveys has received no public funding. Social networks surveys have been partially funded via donations through our website. CoronaSurveys received an award from the UMD/CMU COVID-19 Symptom Data Challenge.

In recent years, mobile device tracking technologies based on various positioning systems have made location data collection an ubiquitous practice. Applications running on smartphones record location samples at different frequencies for varied purposes.The frequency at which location samples are recorded is usually pre-defined and fixed but can differ across applications; this naturally results in big location datasets of various resolutions. What is more, continuous recording of locations results usually in redundant information, as humans tend to spend significant amount of their time either static or in routine trips, and drains the battery of the recording device. In this paper, we aim at answering the question "at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?". Our analyses on fine-grained GPS trajectories from users around the world unveil (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Building on these results, we challenge the idea of a fixed sampling frequency and present a lightweight, energy efficient, mobility aware adaptive location sampling mechanism. Our mechanism can serve as a standalone application for adaptive location sampling, or as complimentary tool alongside auxiliary sensors (such as accelerometer and gyroscope). In this work, we implemented our mechanism as an application for mobile devices and tested it on mobile users worldwide. The results from our preliminary experiments show that our method adjusts the sampling frequency to the mobility habits of the tracked users, it reliably tracks a mobile user incurring acceptable approximation errors and significantly reduces the energy consumption of the mobile device.

International audience; The growing diversification of mobile services imposes requirements on network performance that are ever more stringent and heterogeneous. Network slicing aligns mobile network operation to this context, by enabling operators to isolate and customize network resources on a per-service basis. A key input for provisioning resources to slices is real-time information about the traffic demands generated by individual services. Acquiring such knowledge is however challenging, as legacy approaches based on in-depth inspection of traffic streams have high computational costs, which inflate with the widening adoption of encryption over data and control traffic. In this paper, we present a new approach to service-level demand estimation for slicing, which hinges on decomposition, i.e., the inference of per-service demands from traffic aggregates. By operating on total traffic volumes only, our approach overcomes the complexity and limitations of legacy traffic classification techniques , and provides a suitable input to recent 'Network Slice as a Service' (NSaaS) models. We implement decomposition through Microscope, a novel framework that uses deep learning to infer individual service demands from complex spatiotemporal features hidden in traffic aggregates. Microscope (i) transforms traffic data collected in irregular radio access deployments in a format suitable for convolutional learning, and (ii) can accommodate a variety of neural network architectures, including original 3D Deformable Convolutional Neural Networks (3D-DefCNNs) that we explicitly design for decomposition. Experiments with measurement data collected in an operational network demonstrate that Microscope accurately estimates per-service traffic demands with relative errors below 1.2%. Further, tests in practical NSaaS management use cases show that resource allocations informed by decomposition yield affordable costs for the mobile network operator.

Proceeding of: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 6-9 July 2020 (Virtual Conference) The combination of network softwarization with network slicing enables the provisioning of very diverse services over the same network infrastructure. However, it also creates a complex environment where the orchestration of network resources cannot be guided by traditional, human-in-the-loop network management approaches. New solutions that perform these tasks automatically and in advance are needed, paving the way to zero-touch network slicing.In this paper, we propose AZTEC, a data-driven framework that effectively allocates capacity to individual slices by adopting an original multi-timescale forecasting model. Hinging on a combination of Deep Learning architectures and a traditional optimization algorithm, AZTEC anticipates resource assignments that minimize the comprehensive management costs induced by resource overprovisioning, instantiation and reconfiguration, as well as by denied traffic demands.Experiments with real-world mobile data traffic show that AZTEC dynamically adapts to traffic fluctuations, and largely outperforms state-of-the-art solutions for network resource orchestration. The work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GROWTH project (grant agreement no. 856709). The research of M. Fiore was partially supported by ANR CANCAN project (ANR-18-CE25-0011).

Proceeding of: 2020 Mediterranean Communication and Computer Networking Conference (MedComNet), Aroa, Italy, 17-19 June 2020 In this paper, we investigate the existence and prevalence of comparable dynamics in the temporal fluctuations for the traffic demands generated by mobile applications.To this end, we hinge upon a spectral analysis framework, by computing Discrete Fourier Transforms of the typical demands for tens of popular mobile services observed in an operational metropolitan-scale network. We filter, cluster, and analyse hundreds of frequency components, and identify a substantial set of regular patterns that are common across most service demands. We also unveil how several mobile services defy classification, and have instead highly distinguishing temporal dynamics. The work of Orange Labs was supported by ANR through the CANCAN project (ANR-18-CE25-0011). The work of UC3M was supported by the H2020 5G-TOURS project (grant agreement no. 856950).

The dynamic management of network resources is both a critical and challenging task in upcoming multi-tenant mobile networks, which requires allocating capacity to individual network slices so as to accommodate future time-varying service demands. Such an anticipatory resource configuration process must be driven by suitable predictors that take into account the monetary cost associated to overprovisioning or underprovisioning of networking capacity, computational power, memory, or storage. Legacy models that aim at forecasting traffic demands fail to capture these key economic aspects of network operation. To close this gap, we present DeepCog, a deep neural network architecture inspired by advances in image processing and trained via a dedicated loss function. Unlike traditional traffic volume predictors, DeepCog returns a cost-aware capacity forecast, which can be directly used by operators to take short- and long-term reallocation decisions that maximize their revenues. Extensive performance evaluations with real-world measurement data collected in a metropolitan-scale operational mobile network demonstrate the effectiveness of our proposed solution, which can reduce resource management costs by over 50% in practical case studies. The work of University Carlos III of Madrid was supported by H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5G-TRANSFORMER project (grant agree-ment no. 761536) and 5GROWTH project (grant agreement no. 856709).

Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator’s desired balance between resource overprovisioning (i.e., allocating resources exceeding the demand) and service request violations (i.e., allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting. Comparative evaluations with real-world measurement data prove that DeepCog’s tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-theart mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic., © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The economic sustainability of future mobile networks will largely depend on the strong specialization of its offered services. Network operators will need to provide added value to their tenants, by moving from the traditional one-size-fits-all strategy to a set of virtual end-to-end instances of a common physical infrastructure, named network slices , which are especially tailored to the requirements of each application. Implementing network slicing has significant consequences in terms of resource management: service customization entails assigning to each slice fully dedicated resources, which may also be dynamically reassigned and overbooked in order to increase the cost-efficiency of the system. In this paper, we adopt a data-driven approach to quantify the efficiency of resource sharing in future sliced networks. Building on metropolitan-scale real-world traffic measurements, we carry out an extensive parametric analysis that highlights how diverse performance guarantees, technological settings, and slice configurations impact the resource utilization at different levels of the infrastructure in presence of network slicing. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms at different locations and reconfiguration timescales. The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the work of NEC Laboratories Europe by the 5GTransformer project (Grant Agreement No. 761536). The work of CNR-IEIIT was partially supported by the ANR CANCAN project (ANR-18-CE25-0011). Publicado

International audience

International audience; The paper relates to multi-resource sharing between flows with heterogeneous requirements as arises in networks with wireless links or software routers implementing network function virtualization. Bottleneck max fairness (BMF) is a sharing objective in this context with good performance. The paper shows that BMF results when local fairness is imposed at each resource while flow rates are controlled by an end-to-end window. We analytically prove convergence to BMF under a fluid model when flows share a network limited to 2 resources while numerical results confirm BMF convergence for larger networks. Simulation results illustrate the impact of packetized transmission.

International audience; 5G is the next generation mobile network that enables innovation and supports progressive change across all vertical industries and across our society . Through its Radio Access Network (RAN) design and its orchestrated end-to-end architecture, it has the potential to boost innovation and generate economic growth in the European economy. The 5G service models support agility and dynamicity, thereby impacting the granularity, duration and trustworthiness of business relationships. The ability to combine private and public networks and data centres across multiple domains in a secure and controlled way facilitates collaborative business processes. It reshapes the digital business ecosystem with new value chains linking stakeholders from the telecommunications world and the vertical industries in win-win situations. New stakeholders emerge in this evolved ecosystem, for example cloud companies and software houses that profit from the cloudification and virtualization of the infrastructure, and brokers that facilitate sharing of spectrum and trading of connectivity and processing resources. Small and medium-sized enterprises and start-ups are able to embed 5G in their innovative products and services for existing and new customers and markets, leveraging on the Anything as a Service (XaaS) model.These opportunities are conditioned by the ability of 5G architecture and technologies to deliver the performance levels required for vertical industry stakeholders to engage in the 5G digital business ecosystem. This white paper highlights the technological innovations of the first phase of the 5G Public Private Partnership (5G PPP) and how they contribute to the key performance targets for the 5G service classes: enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine Type Communications (mMTC). The performance levels ensure an unprecedented experience for end users including high data rates, reduced end-to-end latency, massive connectivity, ultra-reliability and support for very high mobility. The 5G PPP innovations go far beyond what is announced for early 5G deployments. For eMBB service, the integration of mm-wave and frequencies below 6 GHz, along with ultra-dense networks and nomadic nodes, ensure the targeted performance levels with ubiquitous coverage and in high mobility scenarios, in contrast with standalone deployments of mm-wave networks, suitable for fixed usage. The innovations related to the transport network allow also translating the peak throughputs available at the air interface into perceived user experience at affordable deployment cost for operators. In addition to this, the envisioned 5G air interface serves simultaneously all service classes (eMBB, mMTC and URLLC) in a cost effective way, paving the way for new business opportunities with and for verticals. The 5G concept developed in this paper is not limited to the RAN; it covers the end-to-end path and allows the 5G network to act as a secure, reliable and flexible orchestration platform across multiple domains. The 5G PPP innovations converge towards the vision of 5G as a holistic orchestration platform that integrates networking, computing and storage resources into one programmable and unified infrastructure. The 5G PPP innovations on multi-domain orchestration enable quick end-to-end service deployment and dynamic sharing of networking and processing resources among stakeholders. The 5G security architecture is built on a baseline trust model as a fundamental feature, and provides tools to analyse trust and make it explicit in specific scenarios. The 5G architecture ensures resilience of the network against attacks and its availability during failure incidents. Availability and reliability are achieved by mechanisms such as error recovery, fault detection and fault resolution. These security, reliability and flexibility properties, along with the multi-service air interface, ensure that the 5G network is not just an enhanced air interface as for pre-5G early deployments, announced for the period 2018-2020, but also an open platform for new business opportunities.The architecture and protocols are designed to adapt to a wide range of deployment scenarios including deep indoor, hot spots, urban areas, rural areas, maritime areas and in an aeronautical context. The 5G concept combines various access technologies, such as cellular, wireless, satellite and wireline, for delivering reliable performance for critical communications and improve area coverage. Standardization and spectrum regulation are critical elements for avoiding fragmentation of future deployments and increasing efficiency by eliminating redundant options. Spectrum regulation must ensure the early availability of a limited number of frequency bands, which eases the development of the necessary equipment and facilitates faster preparation of tests and trials. As of standardization, 5G PPP projects contribute to 5G standards development by building consensus among European industry, leading to individual and concerted actions towards standardization bodies. In contrast to early announcements of 5G deployments, the 5G results are aligned with the standardization trends in 3GPP, ensuring a global impact of European 5G innovations. Once the first 5G standards are released and the frequency bands are available, deployments of 5G networks will start, adopting cost efficient upgrade paths building on existing 4G infrastructure. Networking and processing resource sharing strategies between stakeholders can be implemented for delivering the performance targets, e.g. for URLLC use cases, at an affordable cost. This resource sharing/integration is enabled by the multi-domain orchestration advocated by 5G PPP projects and aim at achieving win-win situations for all the stakeholders involved in the 5G service. Regulation must facilitate such flexibility in infrastructure sharing in order to foster the development of the 5G digital business ecosystem.

The CONGEST model is a synchronous, message-passing model of distributed computation in which each node can send (possibly different) messages of O(log n) bits along each of its incident communication links in each round, where n is the number of computing nodes in the system. In the particular case where the communication network is a complete graph, we have the unicast congested clique model. On the other end is the broadcast version of the congested clique model, in which each node can only broadcast a single message over all its links in each round. In this paper we explore the space, in terms of round complexity, that lies between these two congested clique models. Hence, we parametrize the congested clique model with the range r, the maximum number of different messages a node can send over its incident links in one round. Additionally, we study the effect of the bandwidth b, the maximum size in bits of these messages. We show that the space between the unicast and broadcast congested clique models is very rich and interesting. For instance, we show that a problem (especially designed for this work) takes(n= log n) rounds in the broadcast model (r = 1), while it can be solved in two rounds if two messages can be sent (r = 2). Other gaps are found in other parts of the spectrum of values of r. We do this by providing techniques to simulate protocols with different parameters. Therefore, we conclude that, with respect to their power to solve certain problems, there is a strict hierarchy of congested clique models. TRUE pub

Present MOOC and SPOC platforms do not provide teachers with precise metrics that represent the effectiveness of students with educational resources and activities. This work proposes and illustrates the application of the Precise Effectiveness Strategy (PES). PES is a generic methodology for defining precise metrics that enable calculation of the effectiveness of students when interacting with educational resources and activities in MOOCs and SPOCs, taking into account the particular aspects of the learning context. PES has been applied in a case study, calculating the effectiveness of students when watching video lectures and solving parametric exercises in four SPOCs deployed in the Khan Academy platform. Different visualizations within and between courses are presented combining the metrics defined following PES. We show how these visualizations can help teachers make quick and informed decisions in our case study, enabling the whole comparison of a large number of students at a glance, and a quick comparison of the four SPOCs divided by videos and exercises. Also, the metrics can help teachers know the relationship of effectiveness with different behavioral patterns. Results from using PES in the case study revealed that the effectiveness metrics proposed had a moderate negative correlation with some behavioral patterns like recommendation listener or video avoider. This work has been supported by the EEE project (Spanish Ministry of Science and Innovation, “Plan Nacional de I+D+I TIN2011-28308-C03-01), the “GEEWHEZ: ManaGEmEnt and leisure middleWare for tHemE parks and Zoo” project (European Commission under grant FP7-SME-2011-286533) and the postdoctoral fellowship Alliance 4 Universities

Proceeding of: 11th ACM Conference on Emerging Networking Experiments and Technologies ACM (CoNEXT 2015), Heidelberg, Germany, 1-4 December 2015 Preservation of user privacy is paramount in the publication of datasets that contain fine-grained information about individuals. The problem is especially critical in the case of mobile traffic datasets collected by cellular operators, as they feature high subscriber trajectory uniqueness and they are resistant to anonymization through spatiotemporal generalization. In this work, we first unveil the reasons behind such undesirable features of mobile traffic datasets, by leveraging an original measure of the anonymizability of users' mobile fingerprints. Building on such findings, we propose GLOVE, an algorithm that grants k-anonymity of trajectories through specialized generalization. We evaluate our methodology on two nationwide mobile traffic datasets, and show that it achieves k-anonymity while preserving a substantial level of accuracy in the data. This work was supported by the French National Research Agency under grant ANR-13-INFR-0005 ABCD and by the EU FP7 ERA-NET program under grant CHIST-ERA-2012 MACACO.

The topic of this paper is the study of Information Dissemination in Mobile Ad-hoc Networks by means of deterministic protocols. We characterize the connectivity resulting from the movement, from failures and from the fact that nodes may join the computation at different times with two values, α and β, so that, within α time slots, some node that has the information must be connected to some node without it for at least β time slots. The protocols studied are classified into three classes: oblivious (the transmission schedule of a node is only a function of its ID), quasi-oblivious (the transmission schedule may also depend on a global time), and adaptive. The main contribution of this work concerns negative results. Contrasting the lower and upper bounds derived, interesting complexity gaps among protocolclasses are observed. More precisely, in order to guarantee any progress towards solving the problem, it is shown that β must be at least n − 1 in general, but that β ∈ Ω(n 2/ log n) if an oblivious protocol is used. Since quasi-oblivious protocols can guarantee progress with β ∈ O(n), this represents a significant gap, almost linear in β, between oblivious and quasi-oblivious protocols. Regardingthe time to complete the dissemination, a lower bound of Ω(nα + n 3/ log n) is proved for oblivious protocols, which is tight up to a polylogarithmic factor because a constructive O(nα + n 3 log n) upper bound exists for the same class. It is also proved that adaptive protocols require Ω(nα + n 2), which is optimal given that a matching upper bound can be proved for quasi-oblivious protocols. These results show that the gap in time complexity between oblivious and quasioblivious, and hence adaptive, protocols is almost linear. This gap is what we call the profit of global synchrony, since it represents the gain the network obtains from global synchrony with respect to not having it. TRUE pub

Always-on mobile users need high bandwidth channels with negligible access delay and limited power consumption. Such a continuous connectivity mode requires the management of high-speed channels, which can turn into substantial operational costs (i.e., power consumption rate) even in presence of low traffic, unless a power saving mechanism is enforced. In this paper, we analyze the impact of 3GPP-defined power saving mechanisms on the performance of users with continuous connectivity. We develop a model for packet transmission and operational costs. We model each downlink mobile user’s traffic by means of an M/G/1 queue, and the base station’s downlink traffic as an M/G/1 PS queue with multiple classes and inhomogeneous vacations. The model is validated through packet-level simulations. Our results show that consistent power saving can be achieved in the wireless access network, as high as 75% for mobiles and 55% for base stations. TRUE pub

International audience; In this work, we analyze the power saving and its impact on web traffic performance when customers adopt the continuous connectivity paradigm. To this end, we provide a model for packet transmission and cost. We model each mobile user's traffic with a realistic web traffic profile, and study the aggregate behavior of the users attached to a base station by means of a processor-shared queueing system. In particular, we evaluate user access delay, download time and expected economy of energy in the cell. Our study shows that dramatic energy saving can be achieved by mobile devices and base stations, e.g., as much as 70%-90% of the energy cost in cells with realistic traffic load and the considered parameter settings.

International audience; Book Front Matter of LNCS 7289

In this paper, we analyze the power save and its impact on web traffic performance when customers adopt the continuous connectivity paradigm. To this aim, we provide a model for packet transmission and cost. We model each mobile user’s traffic with a realistic web traffic profile, and study the aggregate behavior of the users attached to a base station by means of a processor-shared queueing system. In particular, we evaluate user access delay, download time and expected economy of energy in the cell. The model is validated through packet-level simulations. Our model shows that dramatic energy save can be achieved by both mobile users and base stations, e.g., as much as 70% of the energy cost due to packet transmission at the base station. TRUE pub

Distributed Computing and Networking, Monitoring physical phenomena in Sensor Networks requires guaranteeing permanent communication between nodes. Moreover, in an effective implementation of such infrastructure, the delay between any two consecutive communications should be minimized. The problem is challenging because, in a restricted Sensor Network, the communication is carried out through a single and shared radio channel without collision detection. Dealing with collisions is crucial to ensure effective communication between nodes. Additionally, minimizing them yields energy consumption minimization, given that sensing and computational costs in terms of energy are negligible with respect to radio communication. In this work, we present a deterministic recurrent-communication protocol for Sensor Networks. After an initial negotiation phase of the access pattern to the channel, each node running this protocol reaches a steady state, which is asymptotically optimal in terms of energy and time efficiency. As a by-product, a protocol for the synchronization of a Sensor Network is also proposed. Furthermore, the protocols are resilient to an arbitrary node power-up schedule and a general node failure model.

Most of the work reported in this paper has been carried out while A. Chatzipapas and V. Mancuso were with INRIA Sophia Antipolis, France. A. Chatzipapas was also with Université de Nice Sophia Antipolis, France.; International audience; Using energy generated with fossil fuel causes global warming due to the greenhouse effect, which threatens our environment. One of the challenges for New Generation Networks (NGN) is then the reduction of energy consumption, in particular at the BSs (Base Stations) which use about 85% of the total network energy. We contribute to the research with a mathematical model that calculates the total power consumption of a BS and enlightens the way to minimize it. First, we analyze the power consumed at every different component of the BS. Second, based on the cost incurred in turning off the BS's power amplifiers, we show how to decide whether it is convenient to keep the BS idle during those intervals in which no traffic has to be sent, or to turn off the amplifiers. Our model is evaluated by means of numerical examples, and shows that interesting power gain can be obtained under a large spectrum of load conditions.