The growing complexity of telecommunication networks increases the need for automated monitoring and detection of incidents. The increasing amount of data generated by current information and communication technologies equipment can be used to identify abnormal behavior and detect anomalies. In this paper, we propose a volume-based real-time solution for anomaly detection in operator networks. The proposal is based on a seasonal autoregressive integrated moving average (SARIMA) forecasting of time-series data, and the identification of outliers. We validate the proposed solution using real traffic traces from a nationwide operator’s network which we make publicly available. Our experimental results show that the proposed solution outperforms baseline approaches in terms of precision, being able to substantially reduce the amount of false positives, while maintaining the same level of recall.

ABSTRACT Mental well‐being is a worldwide priority of health systems. Nevertheless, the diagnosis and recovery rates are still low. This work demonstrates an Artificial Intelligence (AI)‐based, entertainment‐oriented, engaging assistant that can deliver on‐demand, non‐judgmental assessment in an accessible, scalable, and personalised way to people affected by anxiety and depression. For this purpose, we combine Machine Learning (ML) and Large Language Models (LLMs) in a stream‐based framework. Here, the LLMs are exploited to extract high‐level reasoning features from natural language utterances for an accurate ML prediction model. During a study lasting for 14 months, the participants, 146 users mostly within the 65–80 age range, used the conversational assistant. Each user was free to participate as they pleased, with average individual activity times of 4.5 months. During their participation, each user completed an average of two standard mental condition tests, which allowed updating the mental condition tags for classifier retraining in streaming mode. Our solution achieved promising results for detecting anxiety and depression in free dialogues, with accuracy metrics exceeding 90%, outperforming competing works from the literature. Moreover, prior research on anxiety and depression detection has often been limited to providing binary outcomes without explanations behind their rationale. Therefore, this work also addresses interpretability by automatically explaining its prediction in natural language. The contributions of this work are threefold: (i) detecting mental conditions from free dialogues in real‐time with minimal supervision, (ii) conducting a non‐invasive longitudinal analysis based on user engagement, and (iii) automatically providing explanations of the predictive capabilities of the solution. Our approach, supporting continuous interactions suitable for longitudinal studies, combined with explainability mechanisms, connects directly with several strategic lines promoted by the European Union (EU). In particular, the emphasis on early prevention and detection is aligned with a conversational tool that monitors indicators over time. Similarly, the EU promotes transparency, ethical governance, and trust in digital health technologies, as well as data interoperability and common standards as part of the push toward the European Health Data Space. In this context, incorporating explainability, that is, providing the user or researcher with understandable reasons for the model's inferences, strengthens acceptability, accountability, and alignment with good digital governance principles. In this way, our approach contributes to translating the European objectives of promoting accessible, safe, ethical, and evidence‐based digital mental health tools into practice, while facilitating longitudinal monitoring and proactive intervention.

ABSTRACT Quantum networks rely on entanglement distribution to enable secure communication and distributed quantum computation. Most analytical models address idealized, single‐flow scenarios and overlook the impact of concurrent entanglement requests, limiting their applicability to realistic network conditions. This work studies the behavior of a quantum switch under simultaneous entanglement demands, motivated by the need to understand how concurrency affects link utilization, generation rate, and end‐to‐end fidelity. Our main contribution is a concurrency‐aware switch model that jointly captures capacity and fidelity under multi‐flow conditions, offering the first systematic simulation‐based analysis of this problem. Results show that handling several requests at the same time improves throughput but reduces fidelity once the network is too busy. This highlights the need for scheduling and routing strategies that keep a good balance between efficiency and entanglement quality in future quantum networks.

In narrowband Internet of Things, the cell can be divided into up to three coverage enhancement (CE) levels, each associated with a narrowband physical random access channel (NPRACH) that has a CE level-specific configuration. Providing resources to NPRACHs increases the success rate of the random access procedure but detracts resources from the uplink carrier for other transmissions. To effectively address this tradeoff we propose to adjust the NPRACH parameters along with the power thresholds that determine the CE levels, which allows to control at the same time the traffic distribution between CE levels and the resources allocated to each CE level. Since the traffic is dynamic and random, reinforcement learning (RL) is a suitable approach for finding an optimal control policy, but its inherent sample inefficiency is a drawback for online learning in an operational network. To overcome this issue, we propose a new model-based RL algorithm that achieves high efficiency even in the early stages of learning.

Natural Language Generation ( nlg ) popularity has increased owing to the progress in Large Language Models ( llm s), with zero-shot inference capabilities. However, most neural systems utilize decoder-only causal (unidirectional) transformer models, which are effective for English but may reduce the richness of languages with less strict word order, subject omission, or different relative clause attachment preferences. This is the first work that analytically addresses optimal text generation order for non-causal language models . We present a novel Viterbi algorithm-based methodology for maximum likelihood word order estimation. We analyze the non-causal most-likelihood order probability for nlg in Spanish and, then, the probability of generating the same phrases with Spanish causal nlg . This comparative analysis reveals that causal nlg prefers English-like svo structures. We also analyze the relationship between optimal generation order and causal left-to-right generation order using Spearman’s rank correlation. Our results demonstrate that the ideal order predicted by the maximum likelihood estimator is not closely related to the causal order and may be influenced by the syntactic structure of the target sentence.

Abstract Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt’s direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.

Terahertz communications are envisioned as a key enabler for 6G networks. The abundant spectrum available in such ultra high frequencies has the potential to increase network capacity to huge data rates. However, they are extremely affected by blockages, to the point of disrupting ongoing communications. In this paper, we elaborate on the relevance of predicting visibility between users and access points (APs) to improve the performance of THz-based networks by minimizing blockages, that is, maximizing network availability, while at the same time keeping a low reconfiguration overhead. We propose a novel approach to address this problem, by combining a neural network (NN) for predicting future user–AP visibility probability, with a probability threshold for AP reselection to avoid unnecessary reconfigurations. Our experimental results demonstrate that current state-of-the-art handover mechanisms based on received signal strength are not adequate for THz communications, since they are ill-suited to handle hard blockages. Our proposed NN-based solution significantly outperforms them, demonstrating the interest of our strategy as a research line.

Social media include diverse interaction metrics related to user popularity, the most evident example being the number of user followers. The latter has raised concerns about the credibility of the posts by the most popular creators. However, most existing approaches to assess credibility in social media strictly consider this problem a binary classification, often based on a priori information, without checking if actual real-world facts back the users’ comments. In addition, they do not provide automatic explanations of their predictions to foster their trustworthiness. In this work, we propose a credibility assessment solution for financial creators in social media that combines Natural Language Processing and Machine Learning. The reputation of the contributors is assessed by automatically classifying their forecasts on asset values by type and verifying these predictions with actual market data to approximate their probability of success. The outcome of this verification is a continuous credibility score instead of a binary result, an entirely novel contribution by this work. Moreover, social media metrics (i.e., user context) are exploited by calculating their correlation with the credibility rankings, providing insights on the interest of the end-users in financial posts and their forecasts (i.e., drop or rise). Finally, the system provides natural language explanations of its decisions based on a model-agnostic analysis of relevant features.

Abstract Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (nlp) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (lda) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by nlp researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and rouge-l results endorse its potential as a valuable tool for busy investors. The rouge-l values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with lda to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous NLP nor online Machine Learning approaches to TABEA.

Abstract In this article, we evaluate the first experience of computation offloading from drones to real fifth‐generation (5G) operator systems, including commercial and private carrier‐grade 5G networks. A follow‐me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is processed at the edge, and image tracking displacements are translated into positioning commands that are sent back to the drone, so that the drone keeps the camera focused on the person at all times. The application is characterised to identify the processing and communication contributions to service delay. Then, we evaluate the latency of the application in a real non standalone 5G operator network, a standalone carrier‐grade 5G private network, and, to compare these results with previous research, a Wi‐Fi wireless local area network. We considered both multi‐access edge computing (MEC) and cloud offloading scenarios. Onboard computing was also evaluated to assess the trade‐offs with task offloading. The results determine the network configurations that are feasible for the follow‐me application use case depending on the mobility of the end user, and to what extent MEC is advantageous over a state‐of‐the‐art cloud service.

Human activity recognition (HAR) for boxing training typically requires specialized hardware or large labeled datasets to identify different types of punches and analyze their basic properties. However, measuring the unpredictability of boxers, a key characteristic to deceive opponents during combats, has remained an unexplored challenge. We tackled this challenge by combining a series of novel techniques and ideas. Unpredictability was computed as the entropy of the trajectories of the Markov chain characterizing the training session, estimated from the combinations (sequences of punches) thrown by the boxer to the training bag. Punch combinations were obtained by detecting punches as outliers in the raw acceleration sensor datastream coming from the bag, and separating them by type with an ensemble of PCA+GMM clusters. Punches were then divided into combinations using statistical analysis of separation intervals. The detection procedure works even with noisy data, in contrast to force-threshold methods commonly reported in the literature. As distinctive features of our system, it uses only unlabeled data from the current training session, and sensor positions and orientations inside the bag are unconstrained. However, punch clustering achieves up to 94% accuracy, comparable to state-of-the-art supervised approaches. These features are currently unseen in the literature and demonstrate the capacity for unsupervised learning techniques to address challenging problems in sports. Multiple validation experiments were consistent with boxers’ level and session performance. Overall, our approach provides significant contributions to the field of HAR and has the potential to improve the way boxing and other sports are trained and evaluated.

Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems. In news, an expert shares opinions beyond plain technical analyses that include context such as political, sociological and cultural factors. In the same text, the expert often discusses the performance of different assets. Some key statements are mere descriptions of past events while others are predictions. Therefore, understanding the temporality of the key statements in a text is essential to separate context information from valuable predictions. We propose a novel system to detect the temporality of finance-related news at discourse level that combines Natural Language Processing and Machine Learning techniques, and exploits sophisticated features such as syntactic and semantic dependencies. More specifically, we seek to extract the dominant tenses of the main statements, which may be either explicit or implicit. We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field. Experimental results reveal a high detection precision compared to an alternative rule-based baseline approach. Ultimately, this research contributes to the state-of-the-art of market screening by identifying predictive knowledge for financial decision making.

Multi-Access Edge Computing (MEC) is one of the fundamental technologies committed to satisfy the requirements targeted by 5G and beyond networks, such as low latency and massive communications. Nevertheless, deploying a large-scale MEC infrastructure will require a huge investment that should be minimized by optimizing the resources at edge locations, and to use centralized datacenters when possible. Thus, automated orchestration is essential for implementing mechanisms that deploy applications at the best location and even that relocate them, when necessary, to satisfy the Quality of Service (QoS) requirements. In this paper we describe an architecture for this purpose, which we have implemented in an experiment that demonstrates how Open Source MANO (OSM) can automate the relocation of a video processing application that helps drivers to remember the latest traffic sign viewed. Our proposal also includes two new components: the first one maintains the state of the applications when they are deployed at a new location, and the second one allows OSM managing the Open Network Edge Services Software (OpenNESS) edge platform. Finally, we elaborate on open challenges in MEC platforms.

Over the last few years the Multi-Access Edge Computing (MEC) paradigm has been gaining attention as a key enabler for low latency applications in cellular networks. In this paper we analyze a solution based on Software-Defined Networking (SDN) for supporting dynamic and transparent relocation of the endpoint of a communication from the core to an edge infrastructure in current cellular networks. We also provide results of real-world experiments utilising a Network Function Virtualization (NFV)-based testbed for evaluating session continuity and latency reduction when the gateway or anchor point used by two end User Equipments (UEs) is relocated to edge resources. Our experimental results show that the communication can be dynamically relocated from the core to the edge while guaranteeing session continuity during the whole process. We demonstrate that the mechanism is able to reduce latency considerably when the core network is congested.

Emoji sentiment analysis is a relevant research topic nowadays, for which emoji sentiment lexica are key assets.Manual annotation affects directly their quality (where high quality usually corresponds to high self-agreement and interagreement).In this work we present an unsupervised methodology to evaluate emoji sentiment lexica generated from online resources, based on a correlation analysis between a gold standard and the scores resulting from the sentiment analysis of the emoji descriptions in those resources.We consider in our study four such online resources of emoji descriptions: Emojipedia, Emojis.wiki,CLDR emoji character annotations and iEmoji.These resources provide knowledge about real (intended) emoji meanings from different author approaches and perspectives.We also present the automatic creation of a joint lexicon where the sentiment of a given emoji is obtained by averaging its scores from the unsupervised analysis of all the resources involved.The results for the joint lexicon are highly promising, suggesting that valuable subjective information can be inferred from authors' descriptions in online resources.

Summary Network “softwarization” is enabling new features such as detecting the risk to violate a Service Level Agreement (SLA) and performing the required automated control actions on a network slice. A real‐time Network Function Virtualization (NFV) Management and Orchestration (MANO) system should detect any SLA degradation and act to recover the SLA. The closed‐loop automation approach monitors the state of the network and triggers policy‐based control actions when needed. The delay between the detection of an SLA issue and the completion of a correcting action is critical for satisfying latency‐related SLAs. Thus, in this article we propose this delay as a new key performance indicator (KPI) for NFV MANO systems, which we call Closed‐Loop Orchestration Delay (CLOD). CLOD is subdivided into independent time intervals that depend on the NFV MANO platform, the target Virtualized Infrastructure Management (VIM) and the target virtual network function (VNF) software. CLOD is evaluated on a real use case: a latency‐sensitive elastic network slice in which Internet user round‐trip time (RTT) is monitored. When the service level of the slice drops below a predefined limit a new data plane is deployed near the user reducing latency. In the evaluation, we considered two approaches: (i) a general implementation of the NFV MANO architecture based on the Open Network Automation Platform (ONAP) and OpenStack and (ii) an optimized ad‐hoc baseline implementation to minimize CLOD. We provide the details of both implementations and compare their CLODs leading to useful insights for the NFV MANO community.

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working towards the standardization of MEC in 5G networks and the corresponding solutions for routing user traffic to applications in local area networks. Nevertheless, there are neither practical implementations for dynamically relocating applications from the core to a MEC host nor from one MEC host to another ensuring service continuity. In this article we propose a solution based on Software-Defined Networking (SDN) to create a new instance of the IP anchor point to dynamically redirect User Equipment (UE) traffic to a new physical location (e.g., an edge infrastructure). We also present a novel approach that leverages SDN to replicate the previous context of the connection in the new instance of the IP anchor point, thus guaranteeing Session and Service Continuity (SSC), and compare it with alternative state replication strategies. This approach can be used to implement edge services in 4G or 5G networks.

The sixth-generation (6G) of wireless communications systems will significantly rely on fog/edge network architectures for service provisioning. To realize this vision, AI-based fog/edge enabled reinforcement solutions are needed to serve highly stringent applications using dynamically varying resources. In this paper, we propose a cognitive dynamic fog/edge network where primary nodes (PNs) temporarily share their resources and act as fog nodes (FNs) for secondary nodes (SNs). Under this architecture, that unleashes multiple access opportunities, we design distributed fog probing schemes for SNs to search for available connections to access neighbouring FNs. Since the availability of these connections varies in time, we develop strategies to enhance the robustness to the uncertain availability of channels and fog nodes, and reinforce the connections with the FNs. A robustness control optimization is formulated with the aim to maximize the expected total long-term reliability of SNs’ transmissions. The problem is solved by an online robustness control (ORC) algorithm that involves online fog probing and an index-based connectivity activation policy derived from restless multi-armed bandits (RMABs) model. Simulation results show that our ORC scheme significantly improves the network robustness, the connectivity reliability and the number of completed transmissions. In addition, by activating the connections with higher indexes, the total long-term reliability optimization problem is solved with low complexity.

In recent years, social media have been intensively analyzed using sentiment analysis in order to support marketing campaigns.However, when monitoring major public events, social media users' behavior may be strongly biased by the actions of the characters in an event and by a sense of group belonging, typically linked to a specific geographical area.For an event with worldwide impact, we describe a novel solution to automatically assessing the engagement of social media users that combines a location estimation procedure with an unsupervised sentiment analysis technique.The location estimation procedure, which is content-agnostic and applies a network model based on follower accounts, is competitive with previous solutions or outperforms them.The unsupervised sentiment analysis technique, based on language semantics, achieves quite satisfactory results, bearing in mind that, unlike supervised systems, it does not require domain-specific training using annotated text.As far as we are aware, this is the first time these techniques have been implemented jointly.We demonstrate that our solution is coherent with the intrinsic predisposition of users towards the actions of the characters in an event and with a sense of group belonging.

Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software defined networking (SDN) technology may be suitable for network monitoring, signaling, and control. We recently proposed embedding virtual switches in user terminals for direct management by an SDN controller, further contributing to SDN-oriented access network optimization. However, since users may restrict terminal-side traffic monitoring for privacy reasons (a common assumption by previous authors), we infer user traffic classes at the APs. On the other hand, since handovers will be more frequent in dense small-cell networks (e.g., mmWave-based 5G deployments will require dense network topologies with inter-site distances of ~150-200 m), the delay to take assignment decisions should be minimal. To this end, we propose taking fast decisions based exclusively on extremely simple network-side application flow-type predictions based on past user behavior. Using real data we show that a centralized allocation algorithm based on those predictions achieves network utilization levels that approximate those of optimal allocations. We also test a distributed version of this algorithm. Finally, we quantify the elapsed time since a user traffic event takes place until its terminal is assigned an AP, when needed.

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

The availability of very wide spectrum in millimeter wave bands combined with large antenna arrays and ultra-dense networks raises two basic questions: What is the true value of overly abundant degrees of freedom and how can networks be designed to fully exploit them? This paper determines the capacity scaling of large cellular networks as a function of bandwidth, area, number of antennas, and base station density. It is found that the network capacity has a fundamental bandwidth scaling limit, beyond which the network becomes power-limited. An infrastructure multi-hop protocol achieves the optimal network capacity scaling for all network parameters. In contrast, current protocols that use only single-hop direct transmissions cannot achieve the capacity scaling in wideband regimes except in the special case when the density of base stations is taken to impractical extremes. This finding suggests that multi-hop communication will be important to fully realize the potential of next-generation cellular networks. Dedicated relays, if sufficiently dense, can also perform this task, relieving user nodes from the battery drain of cooperation. On the other hand, more sophisticated strategies such as hierarchical cooperation, that are essential for achieving capacity scaling in ad hoc networks, are unnecessary in the cellular context.

Summary Many recommender systems are currently available for proposing content (movies, TV series, music, etc.) to users according to different profiling metrics, such as ratings of previously consumed items and ratings of people with similar tastes. Recommendation algorithms are typically executed by powerful servers, as they are computationally expensive. In this paper, we propose a new software solution to improve the performance of recommender systems. Its implementation relies heavily on Apache Spark technology to speed up the computation of recommendation algorithms. It also includes a webserver, an API REST, and a content cache. To prove that our solution is valid and adequate, we have developed a movie recommender system based on two methods, both tested on the freely available Movielens and Netflix datasets. Performance was assessed by calculating root‐mean‐square error values and the times needed to produce a recommendation. We also provide quantitative measures of the speed improvement of the recommendation algorithms when the implementation is supported by a computing cluster. The contribution of this paper lies in the fact that our solution, which improves the performance of competitor recommender systems, is the first proposal combining a webserver, an API REST, a content cache and Apache Spark technology. Copyright © 2016 John Wiley & Sons, Ltd.

In this work we integrate user terminals into an SDN architecture that allows global network optimization. We embed an SDN virtual switch into a mobile user terminal in such a way that a centralized controller, using both terminal and network-side information, can decide which access network terminal should connect to. User profiling and terminal interface configuration are integral parts of the architecture.

In this paper, we consider a recent cellular network connection paradigm, known as a user-provided network (UPN), where users share connectivity and act as an access point for other users. To incentivize user participation in this network, we allow the users to trade their data plan and obtain a profit by selling and buying leftover data capacities (caps) from each other. We formulate the data trading association between buyers and sellers as a matching game. In this game, buyers and sellers rank each other based on preference functions that capture the buyers' demand for data and QoS requirements, the data available for purchase from the sellers and energy resources. We show that these preferences are interdependent and influenced by existing network-wide matching. For this reason, the game can be classified as a one-to-many matching game with externalities. To solve the game, we propose a distributed algorithm that combines notions from matching theory and market equilibrium. The algorithm enables the players to self-organize into a stable matching and ensures dynamic adaptation of price to data demand and supply. The properties of the resulting matching are discussed. We also calculate operator gains and the benchmark price that will encourage users to join the UPN. Simulation results show that the proposed algorithm yields average utility per user improvements of up to 25% and 50% relative to random matching and worst case utility, respectively.

Summary Virtual desktop infrastructure (VDI) solutions seek to provide a satisfactory user experience at the client side when accessing remote desktop applications, even from mobile devices with limited capabilities. This paper presents a new approach, improving on previous work by the authors, in which a combination of Virtual Network Computing (VNC) and streaming protocols allowed efficient remote web access to virtualized applications within a cloud architecture. The new approach simplifies virtual machine templates, from which virtual machine instances are deployed, by centralizing software modules, greatly simplifying their management. Our new contribution consists of an integrated solution with specific WebM video encoding modules in charge of application visual output processing, an Hypertext Transfer Protocol (HTTP) streaming server, and a VNC server. The solution can be installed in the hypervisor of the host machines instead of replicating the servers and modules throughout the guest (virtual) machines that run the virtualized applications. Consequently, their implementations are unique and independent of the operating system of the virtual machines. In short, it is not necessary to provide different implementations for different operating systems, which reduces the complexity of virtual machine templates and greatly simplies platform management. To demonstrate our solution, we have modified the Quick Emulator (QEMU)‐Kernel‐based Virtual Machine (KVM) hypervisor source code accordingly. We also present qualitative and quantitative analyses that demonstrate that the new approach is advantageous in terms of software management and quality of experience, compared with our previous work and other well‐known thin clients, contributing to the enhancement of VDI systems. Copyright © 2015 John Wiley & Sons, Ltd.

Fourth Generation (4G) cellular networks have millions of active subscribers around the globe. These networks have allowed data rates that were traditionally out of reach of mobile terminals and, ultimately, opened up new business opportunities for content providers. However, 4G carriers are also deploying WiFi hotspots as a complementary technology to access their core networks and have found it necessary to offload traffic from their cellular access networks to aleviate the serious congestion problems they were facing. These congestion are expected to persist and even worsen in the transition to 5G cellular networks. In this work we borrow ideas from the field of Software-Defined Networks (SDNs) to include user terminals as an integral part of that paradigm, thus contributing to global network optimization. By integrating a virtual switch into the terminals, a centralized controller can decide which access network the terminals should connect to based on both terminal (user profiling) and network-side information. A novel aspect of our proposal is that wireless terminal interface configuration is an integral part of the architecture.

SUMMARY T‐Learning makes it possible to deliver educational content to home TVs. TV operators, which manage huge populations of devices such as set‐top‐boxes (STBs) in user homes, are considering t‐Learning as an interesting option for expanding the service they offer. However, typical STB hardware configurations are limited in terms of satisfying operator needs and do not easily support all types of applications or content. In this work, we consider graphic < e‐Adventure > educational games, which are not directly executable on typical STBs. To cover this gap and guarantee an enjoyable user experience, we present an architecture based on a combination of streaming and remote desktop protocols that relies on virtualized servers deployed in a cloud computing infrastructure. It features an original image‐encoding signalling mechanism that identifies multimedia content in educational games and permits seamless protocol switching at the client side. This architecture is a complete technological solution to virtualize heavy educational games and execute them smoothly on STB light clients over Internet Protocol Television networks. We present performance results that show that our proposal is an efficient scalable solution to deliver t‐Learning to home environments. Copyright © 2013 John Wiley & Sons, Ltd.

This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a k-nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Católica San Antonio de Murcia volleyball team, which competes in the Spanish women's premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.

In this article we propose solutions to diverse conflicts that result from the deployment of the (still immature) relay node (RN) technology in LTE-A networks. These conflicts and their possible solutions have been observed by implementing standard-compliant relay functionalities on the Vienna simulator. As an original experimental approach, we model realistic RN operation, taking into account that transmitters are not active all the time due to half-duplex RN operation. We have rearranged existing elements in the simulator in a manner that emulates RN behavior, rather than implementing a standalone brand-new component for the simulator. We also study analytically some of the issues observed in the interaction between the network and the RNs, to draw conclusions beyond simulation observation. The main observations of this paper are that: i) Additional time-varying interference management steps are needed, because the LTE-A standard employs a fixed time division between eNB-RN and RN-UE transmissions (typical relay capacity or throughput research models balance them optimally, which is unrealistic nowadays); ii) There is a trade-off between the time-division constraints of relaying and multi-user diversity; the stricter the constraints on relay scheduling are, the less flexibility schedulers have to exploit channel variation; and iii) Thee standard contains a variety of parameters for relaying configuration, but not all cases of interest are covered.

Software-Defined Networking (SDN) has gained momentum in recent years, in a bid to transform networking ecosystems. It has already been applied to data center networks and wide area networks, such as Google's WAN. A software-defined approach is thus a reasonable candidate architecture for other highly dynamic environments such as future 5G mobile networks. This paper presents a two-level software-defined approach for the future 5G architecture at the network and radio access layer. At each level, a dedicated logical centralised controller and northbound/southbound Application Programming Interfaces are defined. Two cross-layer schemes are also introduced for interaction between controllers.

SUMMARY Traditionally, call authentication and security have not raised user concerns because wiretapping requires physical access to the phone line and special equipment. However, Voice over Internet Protocol (VoIP) communications are becoming increasingly popular, and there is the perception that they may be easier to intercept or impersonate (thus creating higher demand for security solutions), especially if the connection occurs over wireless links. The Session Initiation Protocol is widely used for managing voice and video communications over the Internet, and the Real‐time Transport Protocol is used to carry voice and/or video streams. Session Initiation Protocol, however, was not designed with security in mind and is vulnerable to attacks. Furthermore, there are cases in which it is necessary to authenticate the call participants (e.g., when they are asked to disclose confidential information). There are few practical solutions to this problem, essentially because they require complex infrastructures to manage keys and certificates. In this paper, we solve this problem with a novel approach: the use of national public key infrastructures in combination with national Electronic Identity smart cards, created by governments to execute trusted electronic transactions and establish trusted identities. In addition, we will show how our approach enables new use cases (e.g., institutions – hospitals, banks, government, etc. – can be confident that they exchange confidential information to the intended person and vice versa) and simplifies the steps necessary to secure and authenticate a VoIP communication with ZRTP, simplifying and therefore encouraging the use of secure VoIP communications between authenticated users. Copyright © 2012 John Wiley & Sons, Ltd.

Road safety and vehicle information applications may be jointly enhanced by IEEE 802.11 communications and Delay- and Disruption-Tolerant Networking, thus allowing for reliable and efficient communication in a highly mobile environment with frequent network disruptions and short inter-node contacts. Previous work on this topic has addressed several challenges of vehicular opportunistic communications such as routing and packet loss problems. In this paper we focus on the optimization of the throughput of opportunistic links. This throughput drops with the speed of moving vehicles because the effective communication interval is limited by the time the nodes lie within mutual radio range. We study how this effective communication interval is affected by possible modifications of the protocol stacks involved, and draw some useful conclusions about their practical application.

This paper studies mutual information in a novel scenario combining cooperative diversity and cognitive radio based on spectrum leasing. In the scenario there is a primary transmitter subject to random channel fading, with full spectrum rights available, and a secondary transmitter without any spectrum rights, which offers its cooperation to the primary transmitter in exchange for a share of the resulting resource gains. Two decision-making schemes, based on different levels of channel knowledge, are considered in the primary transmitter. These are knowledge of the statistics of random fading (averages) and full knowledge of instantaneous channel state. The contributions of this paper are the radio leasing scenario itself, which, unlike previous approaches in cognitive radio, is not based on game theory, and the statistical characterization of the resource gains achieved in this scenario using cooperative diversity for spectrum leasing under diverse channel conditions and the aforementioned decision-making schemes. Analytical expressions are obtained for the probability of cooperation, the mutual information probability density function and its average, and the proportion of resource gains achieved for the statistical and instantaneous channel knowledge schemes. We identify the conditions for resource gains: for statistical channel knowledge to suffice, the primary link must be of low quality, whereas for instantaneous channel knowledge, although resource gains are achieved in any situation, they increase as the primary channel gets worse.

Traditional video surveillance systems require infrastructures including expensive servers with capabilities to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute on them specific image analysis in real-time in order to detect safety events. We propose a video surveillance system based on Cloud Computing that collects multimedia streams generated by surveillance cameras, optimizes their transmissions according to network condition and stores them in a cloud storage system in an efficient and secure way.

This paper studies the Implicit Error Correction (IEC) near-Video on Demand (nVoD) schema, which relies on the intrinsic redundancy of nVoD protocols, in channels that deliver damaged data. The results rather than discard show that IEC provides good protection against errors without increasing the bandwidth requirements of other schemas for the same error probability.

An increasing number of new geolocation services are exploiting the new capabilities of smartphones, most of which incorporate GPS location. There are fewer applications for providing indoor location, partly due to the cost of the required infrastructure. In this paper, we present QR-Maps, a simple and efficient tool that can be used in smartphones to obtain accurate indoor user locations. This tool employs QR-Codes containing a short text which indicates locations shown within a custom Google map. The proposed solution is cheaper than other approaches based on wireless technologies.

Optical Packet Switching (OPS) has been proposed as the ultimate solution for Wavelength Division Multiplexing (WDM) core networks, but it has not achieved commercial status due to numerous technological requisites such as packet tunable wavelength conversions, switching times in the order of nanoseconds, and individual wavelength switching. Nevertheless, new OPS paradigms reduce technological complexity by eliminating wavelength conversions and performing joint wavelength switching. Moreover, new interferometric optical devices capable of switching two optical signals in sub-nanosecond times are already commercially available. In this paper, we propose an all-optical switch architecture based on these devices, which switch all wavelengths as a bundle. We also perform a preliminary analysis of this architecture with some simplifications that allow us to set an upper bound on the maximum bit rate attainable in an ideal network (relaxing the need for 3R regeneration and electro-optical conversions).

Authorities recommend the practice of moderate physical activity to improve health and quality of life, but sometimes it is difficult to find the willpower or the motivation to reserve a few minutes to exercise. In this paper we describe a new tool designed to help users to practice sport and record their achievements, which makes the activity more social (finding friends, sharing results, etc.). Furthermore, this tool has been integrated in the "Ubiquitous Oulu", an intelligent city that provides the building blocks to bring it closer to citizens. In this real-life environment we had the opportunity to assess the technical viability of RunWithUs and to identify the main interests of its potential users.

Automatic identification based on radio frequency identification (RFID) is progressively being introduced into industrial environments, enabling new applications and processes. In the context of communications, RFID rely mostly on Frame Slotted Aloha (FSA) anticollision protocols. Their goal is to reduce the time required to detect all the tags within range (identification time). Using FSA, the maximum identification rate is achieved when the number of contending tags equals the number of contention slots available in the frame. Therefore, the reader must estimate the number of contenders and allocate that number of slots for the next frame. This paper introduces the new MFML-DFSA anticollision protocol. It estimates the number of contenders by means of a maximum-likelihood estimator, which uses the statistical information from several frames (multiframe estimation) to improve the accuracy of the estimate. Based on this expected number of tags, the algorithm determines the best frame length for the next reading frame, taking into account the constraints of the EPCglobal Class-1 Gen-2 standard. The MFML-DFSA algorithm is compared with previous proposals and found to outperform these in terms of (lower) average identification time and computational cost, which makes it suitable for implementation in commercial RFID readers.

We present a service model that allows terminals to extend their capabilities by delivering services to intelligent infrastructures. For example, a terminal may deliver a pack containing a visualization application and a file to an infrastructure screen. We also present a variant where a user can invite other users to access a shared service on the infrastructure, from other terminals.

Sharing documents across different platforms is highly necessary nowadays. However, many devices, as embedded ones, cannot deal with formats like PDF or PowerPoint, despite their widespread use. In this paper we propose an automated format conversion system to allow the visualization of formats that are not supported in simple multimedia embedded devices.

Optical switching fabrics are gaining interest as in multi Terabit switching devices; the advantages over their electronic equivalents are mainly their information density and power consumption. Arrayed Waveguide Gratings (AWGs) are promising optical devices proposed by the academic and industrial community to build optical switching fabrics. Because of their wavelength routing property, AWGs allow wavelength reuse over different ports introducing in-band crosstalk which strongly limits scalability of AWG-based backplanes. However, this effect can be mitigated or even completely avoided by means of proper scheduling algorithms. In this paper, we present several modified scheduling algorithms which limit the effect of coherent crosstalk in AWG-based switching fabrics and achieve good performance in terms of throughput and delay.

Abstract In this paper we present our experience in the development of a web‐based business intelligence tool according to the Associative Query Logic paradigm, which can represent large amounts of data in a way that allows extremely fast queries. It has been developed as an open‐source, multi‐platform software, relying on data compression techniques for the storage of large amounts of data in the main memory. The performance of our solution in terms of compression, load time and response time is close to that of the commercial tool of reference, QlikView. Moreover, we provide solutions to some open problems in QlikView published description, which may be beneficial to assist in the development of other open or proprietary tools. Copyright © 2010 John Wiley & Sons, Ltd.

Most all-optical switching paradigms assume that different wavelengths are switched independently, which limits scalability. In optical cell switching (OCS), time is divided into time slots of fixed size by time-division multiplexing, and the wavelengths in a time slot are all bundled. Thus, each OCS switch (OCX) has a single switching plane and performs mere time-space switching. In OCS, each OCX requires optical slot synchronizers (OSYNs) at all inputs for the arrival slots to be aligned, so that cells can be simultaneously forwarded. In a recent OCS paradigm -not-aligned OCS-, the OSYNs and the alignment process are no longer required. Cell shifting still takes place inside the OCXs for minimizing the gaps between cells, but it is not necessary to align them to a reference time. Not-aligned OCS has clear advantages over aligned OCS: the total number of fiber delay loops (FDLs) and the hardware cost are reduced, and the number of switching operations is also lower. Moreover, cell arrival time to the switch is not critical, and the network becomes simpler and more flexible. In this paper, we propose a new distributed resource scheduling algorithm for not-aligned OCS networks, which takes connection blocking probability to reasonable values for practical loads.

This paper focuses on automated content update for MHP applications. Some of the authors have been involved in the first MHP trials of TVG, the TV channel of Galicia, NW Spain (three million population). It has a 30% share at regional level, competing with national channels. However, the initial MHP trials have raised the problem of manual content update cost, since in many key applications, like news and weather forecasts, contents vary on a daily basis. We have reviewed the status of MHP deployment in similar TV channels, and we have determined that the problem is currently relevant. We describe our solutions for automated content update with minimal human intervention, and our practical experience with them.

Remote monitoring of physical effort has a great importance in the treatment of some cardiopathies. Nowadays, wearable motion-tracking devices represent an easy solution to acquire relevant data. We demonstrate a system based on a commercial sensor that sends real-time information to a hospital via a mobile gateway -the patient's cell phone- in the same Bluetooth WPAN.

As wireless mesh networks become more widely deployed, media streaming in such networks poses a pressing research issue because of the increasing importance of multimedia applications. The challenge with wireless mesh networks is the time-varying wireless channel compounded by multi-hop transmission. However, wireless mesh networks also present an opportunity, i.e., the redundancy provided by broadcast wireless media and mesh connectivity. In this paper, we present a joint interchannel and network coding schema to take advantage of this opportunity and to overcome the challenge of wireless mesh networks. We evaluate of the proposed joint coding through both mathematical analysis and computer simulation. The results demonstrate significant benefits in terms of protection against packet losses and bandwidth utilization of the joint coding schema.

Packet switches with optical fabrics can potentially scale to higher capacities. It is also potentially possible to improve their reliability, and reduce both their footprint and power consumption. A well-known alternative for implementing hardwired switches is Arrayed Waveguide Grating (AWG). Ideally, AWG insertion losses do not depend on the number of input-output ports, meaning that scalability is theoretically infinite. However, accurate second-order assessment has demonstrated that in-band crosstalk exponentially increases the power penalty, limiting the realistic useful size of AWG commercial devices to about 10-15 ports (13-18 dB). On the other hand, the in-band crosstalk at AWG outputs depends on the connection pattern set by the scheduling algorithm and this port count limitation is calculated for worst-case scenarios. In this paper, we show that distributed schedulers with predetermined connection patterns can be used to avoid these harmful arrangements. We also show that the probability of worst-case patterns is very low, allowing us to set a more realistic port limit for general centralized schedulers and very small losses. With these results, we calculate more realistic port count limits for both scheduler types.

Abstract For a long time, the design of relational databases has focused on the optimization of atomic transactions (insert, select, update or delete). Currently, relational databases store tactical information of data warehouses, mainly for select‐like operations. However, the database paradigm has evolved, and nowadays on‐line analytical processing (OLAP) systems handle strategic information for further analysis. These systems enable fast, interactive and consistent information analysis of data warehouses, including shared calculations and allocations. OLAP and data warehouses jointly allow multidimensional data views, turning raw data into knowledge. OLAP allows ‘slice and dice’ navigation and a top‐down perspective of data hierarchies. In this paper, we describe our experience in the migration from a large relational database management system to an OLAP system on top of a relational layer (the data warehouse), and the resulting contributions in open‐source ROLAP optimization. Existing open‐source ROLAP technologies rely on summarized tables with materialized aggregate views to improve system performance (in terms of response time). The design and maintenance of those tables are cumbersome. Instead, we intensively exploit cache memory, where key data reside, yielding low response times. A cold start process brings summarized data from the relational database to cache memory, subsequently reducing the response time. We ensure concurrent access to the summarized data, as well as consistency when the relational database updates data. We also improve the OLAP functionality, by providing new features for automating the creation of calculated members. This makes it possible to define new measures on the fly using virtual dimensions, without re‐designing the multidimensional cube. We have chosen the XML/Ade factostandard for service provision. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper we present a novel multicast near-Video on Demand (nVoD) scheme. It relies on the intrinsic redundancy of nVoD protocols to provideimplicit error correction, by employing content segments themselves as blocks for coding operations. The results in this paper show that our proposal outperforms previous schemes in terms of transmission bandwidth, for the same packet loss probability and response time.

In this paper we present a novel multicast near- video on demand (nVoD) schema which relies on the intrinsic redundancy of nVoD protocols to provide implicit error correction, by employing content segments as blocks for coding operations. The results in this paper show that our proposal outperforms previous approaches involving explicit error correction (error protection within content segments) in terms of transmission bandwidth for the same packet loss probability.

Abstract This paper presents the development experience and the evaluation of a system to emulate interactive features of Access Grid virtual rooms (venues) at Web endpoints. The system includes a toolset to demultiplex Access Grid sources, transcode them to adequate formats for Internet distribution and store the resulting multimedia streams in repositories in real time. A specialized server asynchronously delivers those streams to standard Web browsers, which synchronize them and emulate typical Access Grid interactive features such as multiple windows or dynamic video selection. Web browsers can compose multiple independent streams. We call the resulting paradigm Presentations on Demand. From the point of view of software practice, the system relies on a combination of SMIL scripts, CSS and Javascript. Copyright © 2008 John Wiley & Sons, Ltd.

The parallel hierarchical matching (PHM) algorithm is a distributed maximal size matching scheduler for virtual output-queued switches. In a previous letter, we formulated an upper bound on the maximum number of iterations PHM requires to achieve a maximal size matching in any traffic scenario. In this letter, we follow an analytical approach to find the average number of iterations for PHM to achieve a maximal size matching under diverse traffic models. The estimated number of iterations is O(log2N), as in the case of iSLIP-like algorithms

High-speed telecommunications routers are very important systems in today's networked environments. The purpose of this paper is to propose a mathematical model of the faulty behaviour of such systems and, derived from it, a scheme for the detection of errors occurring concurrently with their normal operation. Although the ultimate goal is to obtain a fault-tolerant router, this work concentrates on the scheduler part of the system and, in particular, in the case of virtual output-queued (VOQ) switches. As starting point, in this paper a balanced traffic load is assumed. The faulty behaviour of complex digital processing systems is usually better described at the algorithmic level, particularly when the operation of the system relies on complex mathematical principles. Therefore, the issues related to concurrent error detection are addressed from the developed mathematical model. Results are presented that point to the ability of the proposed solution to detect errors at a high abstraction level. They have been obtained by injecting faults in the algorithm flow rather than in the hardware itself.

HomePlug powerline communications networks link individual computers or network segments via low-voltage power lines. The powerline medium is a harsh channel, since it varies in time and has a high attenuation. This makes difficult to communicate different devices in distant places in a building. A possible solution is the HomePlug repeater we propose in this paper. We show that it is impossible to develop a repeater at the physical layer, so we have developed it at medium access control (MAC) level. Its main distinctive features are transparency (no planning is necessary) and availability (it is built from off-the-shelf hardware).

The deployment of optical packet switching (OPS) in dense wavelength division multiplexing (DWDM) backbone networks is perceived as a medium term promising alternative. Scalability restrictions imply that conventional switching architectures are unfeasible in this large-scale scenario. In a previous paper, the wavelength-distributed knockout architecture was proposed as a cost-effective scaling strategy for OPS switching fabrics. In this paper, this growable architecture is applied to OPS switching fabrics able to emulate output buffering. We also propose an scheduling algorithm which provides optimum performance if knockout packet losses are made negligible. The mathematical analysis to evaluate the knockout packet loss probability of this architecture is obtained, under uniform and non-uniform traffic patterns. To complement the switch dimensioning process, an upper bound assuring 0-knockout packet losses is compared with the exact analytical results.

Diverse social applications rely on remote location of personal devices: house arrest tracker systems, remote monitoring of alcoholic persons, child locator devices and remote protection of victims of domestic abuse are some examples. However, existing personal location devices either depend on a fixed station (a domestic gateway) -thus limiting user mobility-, or include a full cell phone -thus increasing weight, cost and power consumption-. In this demonstration, we present a new system we have developed for the Galician regional government (Spain) to track domestic abusers and their victims. It consists of two units, namely a personal low-cost light microdevice (bracelet in the sequel) and a commercial cell phone. The latter acts as a mobile gateway, which the bracelet accesses via a PAN. By doing so, we benefit from the advantages of the two previous approaches: the small size of bracelets for fixed gateways and the full mobility of those including a full cell phone. Wide-area location capability may reside either in the bracelet (as a GPS modem) or in the mobile gateway (either as a GPS modem or via core network-assisted location). If the bracelet leaves mobile gateway PAN range, the latter alerts the user and/or sends an alarm to a remote monitoring station.

A practical real-time mobile auction system must fulfill diverse constraints. In Rodriguez-Hernandez P.S. et al. (2002), we described a mobile auction system implemented with 433 MHz band modems. Nowadays, that system has become a standard in the Galician fish markets, which include the most important fishing port in Europe, Vigo (Spain). Although our system has been performing satisfactorily for four years, it is obvious that new technologies such as Bluetooth and IEEE 802.11 have shaped the wireless market and deserve our attention. In this paper, we evaluate Bluetooth in the context of real-time mobile auctions. As a result, we propose a novel approach to implement mobile auction systems by exploiting the park mode in Bluetooth specifications, which may also be useful for other remote-command applications with hard real-time constraints

We propose a methodology to deploy large scale IEEE 802.11b telecommunications infrastructures, which are being currently used to provide broadband access in Spanish rural areas, as an expansion of shared asymmetric DVB-S or DVB-T gateways. Wireless broadband access networks have become possible with the advent of technologies like IEEE 802.11. Our approach is based on a set of basic rules to generate mesh topologies with survivability capabilities. These rules help to mitigate cochannel interference and inter-channel interference. Two infrastructure deployment algorithms are given: a distributed version, executed by infrastructure nodes themselves, and a centralized version derived from a mixed integer linear optimization model. Numerical results show that the optimization model is more efficient but the distributed algorithm is competitive, since it can be executed by the wireless network itself and generates good quality results.

In this paper, we analyze survivability issues of auxiliary Bluetooth location networks (BLN) for location-aware or context-driven mobile networks. We assume that there exist service servers that need to know user location in real-time, to send context-oriented information to user handhelds when necessary. The BLN transmits position information to service servers, without user participation. It is not subject to line-of-sight constraints and is supported by existing commercial handhelds. BLN users carry either a Bluetooth-enabled handheld or any mobile data terminal and a Bluetooth badge. The BLN is composed of wireless Bluetooth nodes, which establish a spontaneous network topology at system initialization. The BLN can coexist with other Bluetooth systems.

In this paper, we propose a real-time transcoding system to generate contents for mobile networks. Video transcoding is widely used to change storage formats. However, as far as the authors know, it has not been proposed to generate real-time contents. We implement and evaluate an indoor video service that delivers transcoded contents to wireless pocket PCs via IEEE 802.11b multicast. We propose a generic methodology for mobile video planning.

Input-queued packet switches are more scalable than output-queued ones. However, due to HOL blocking, their throughput is poor. The virtual output queueing (VOQ) switch architecture and several buffer schedulers have been proposed to overcome this problem. Among them, the class of iterative maximal matching algorithms, with the first example being parallel iterative matching (PIM), which uses random selection, and iSLIP that uses round-robin selection, and has become a de facto standard in switching research. iSLIP admit efficient practical implementations, and has several variants with different pointer updating strategies - like FIRM, DRRM and RDSRR - that improve performance. In previous work, we formulated a new scheduler, parallel hierarchical matching (PHM), which compare favorably to iSLIP-like algorithms. PHM can be considered a parallelization of previous high-performance sequential hierarchical matching algorithms, like 2DRR or WWFA. In this paper, we compare their delay performance for same decision response-time, determined from ASIC implementations, and for different traffic models.

iSLIP and parallel hierarchical matching (PHM) are distributed maximal size matching schedulers for input-buffered switches. Previous research has analyzed the hardware cost of those schedulers and their performance after a small number of iterations. In this paper, we formulate an upper bound for the number of iterations required by PHM to converge. Then, we compare the number of iterations required by iSLIP and PHM to achieve a maximal throughput under uniform Bernoulli traffic, by means of simulation. Finally, we obtain the corresponding delay performances, which are similar. The results suggest that PHM has both the advantages of previous hierarchical matching algorithms (low hardware complexity) and iSLIP (low number of iterations).

We propose a Bluetooth location network (BLN) for location-aware or context-driven mobile networks, such as m-commerce networks or e-museums. We assume that, in any of those scenarios, there exist service servers that need to know user location in real-time, to send context-oriented information to user handhelds when necessary. The BLN transmits position information to the service servers, without user participation. It is not subject to line-of-sight constraints and is supported by existing commercial handhelds. BLN users carry either a Bluetooth-enabled handheld or any mobile data terminal and a Bluetooth badge. The BLN is composed of small, wireless Bluetooth nodes, which establish a spontaneous network topology at system initialization. The BLN can coexist with Bluetooth devices that are not part of the location system, such as printers or headphones.

Virtual machines for remote execution are a useful tool for utilizing light user interfaces and intensive application cores in different physical machines connected through the Internet. In a virtual machine, application cores are distributed in a network. Specific locations, operating systems and hardware characteristics are hidden by virtual machines. They make it possible to use a PC to execute user interfaces and (a few) high-performance computers for application cores. We present a Java/CORBA-based brokerage platform that allows remote execution of optimization solvers from a client running on any platform. The system offers a dynamic library of available problem solvers, and a graphic interface to browse several defined properties and metadata on available solvers. In addition, an embedded file compression module to reduce data transfer time is included as a plug-in feature of the proposed virtual machine. Analogous systems could be constructed for applications in which interaction traffic time is much lower than execution time. Copyright © 2001 John Wiley & Sons, Ltd.

This paper formulates an incomplete projection algorithm that is applied to the image recovery problem. The algorithm allows an easy implementation of dynamic load balancing for parallel architectures. Furthermore, the local computation-communication load ratio can be adjusted, since each processor performs a finite number of iterations of any projection-type technique, and this number can be provided as a parameter of the algorithm. Numerical results compare favorably with those obtained by the extrapolated method of parallel subgradient projections.