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Environmental noise monitoring for smart cities need to be as much efficient as possible in order to mitigate its significant impact in the health of their inhabitants. 5G Internet of Things (IoT) systems offer a big opportunity to offload the computation from the sensor nodes, since it provides a series of new concepts for dynamic computing that the previous technologies did not offer. In this article, a complete 5G IoT system for psycho-acoustic monitoring has been designed and implemented using different options for offloading computation to different parts of the system. This offloading has been done by developing different functional splittings of the psycho-acoustic metrics algorithms to allocate such splits in different locations. Finally, a performance comparison among different functional splittings and their implementation are shown with a detailed discussion.

Traffic surveillance is an important issue for Intelligent Transportation Systems (ITS) that helps detect incidents automatically, such as wrong-way drivers, still-standing vehicles and jams. Sometimes these systems require a fast and short-term deployment of video cameras. In these cases, ad-hoc networks could be a low-cost and feasible option, but they have poor performance for video delivery. Thus, we propose a smart live video adaptive streaming technique in order to transport video streams from the cameras to the external road traffic monitoring servers. To achieve this goal, these networks need a thorough study in order to analyze video quality under their inherent constraints, given by their specific topologies (once deployed), video streaming protocol, nodes’ performance and routing protocols. From our results both in simulated and real scenarios, we demonstrate the feasibility of our proposal in these specific scenarios, validated with several performance metrics in terms of Quality of Service and video quality.

Sensor networks have become an extremely useful tool for monitoring and analysing many aspects of our daily lives. Noise pollution levels are very important today, especially in cities where the number of inhabitants and disturbing sounds are constantly increasing. Psycho-acoustic parameters are a fundamental tool for assessing the degree of discomfort produced by different sounds and, combined with wireless acoustic sensor networks (WASNs), could enable, for example, the efficient implementation of acoustic discomfort maps within smart cities. However, the continuous monitoring of psycho-acoustic parameters to create time-dependent discomfort maps requires a high computational demand that prevents real-time computations within the nodes. Moreover, sending audio streams outside of the WASN for their further computation, would require extra communication and computational efforts without warranting a real-time monitoring, with the added problem of violating some privacy laws. As a result, most existing systems for nuisance assessment are usually based on less accurate indicators that require lower computational cost. In this paper, we describe the design and analysis of a deep convolutional neural network (CNN) trained with a big dataset of typical sounds occurrying in a city. The CNN allows to predict the psycho-acoustic parameters considered by the well-known Zwicker’s psycho-acoustic nuisance model with great accuracy, directly from the raw recorded audio signal. The proposed CNN-based system has been tested on both desktop computers and typical WASN devices (such as Raspberry Pi), achieving very fast calculation times that allow real-time operation and a continuous monitoring of psycho-acoustic parameters.

Soundscape standard (ISO 12913) is mainly oriented to describe the psychoacoustic annoyance (PA) due to the perceived sound in different environments. The evaluation of this annoyance is commonly based on the Zwicker and Fastl model that defines several components related to this subjective annoyance, such as loudness, sharpness, roughness, and fluctuation strength. But due to their complexity, these components are difficult to be calculated on small board computers (SBCs) in real time in order to enable a wireless acoustic sensor network for PA monitoring. In this article, we describe the necessary procedures to implement the complete psychoacoustic model by Zwicker and Fastl in a precise and efficient way, keeping the standards and criteria defined for each model, adding hints to improve its performance. We evaluate their execution time on different platforms and languages, including SBCs, in particular on Raspberry Pi family. Thus, this article shows a feasible implementation of this monitoring approach on low-cost platforms as well as a visualization tool.

As minimum area SRAM bit-cells are obtained when using cell ratio and pull-up ratio of 1, we analyze the possibility of decreasing the cell ratio from the conventional values comprised between 1.5-2.5 to 1. The impact of this option on area, power, performance and stability is analyzed showing that the most affected parameter is read stability, although this impact can be overcome using some of the read assist circuits proposed in the literature. The main benefits are layout regularity enhancement, with its consequent higher tolerance to variability, cell area reduction by 25 percent (with respect to a cell having a cell ratio of 2), leakage current improvement by a 35 percent, as well as energy dissipation reduction and a soft error rate per bit improvement of around 30 percent.

Nowadays, the use of virtual reality/virtual acoustics (VR/VA) in archaeology for rendering lost buildings is an important topic in the cultural heritage field. Moreover, the addition of additional senses apart from the sight increases the feeling of immersion in virtual environments. The aim of this paper is to show the interaction work developed in a VA system, based on Unity and FMOD, the graphical and acoustical reconstruction of an ancient building and the development of a VR goggles with headphones to render 3D audio and video interactively. This system has been implemented to render auralizations in a binaural system and has been applied to the renderization of an old and lost theatre in València (Spain). The first building of theatre was built in the 16th century, and was rebuilt several times until the 18th century. The auralization of several theatrical excerpts of different Spanish authors of that time is also presented. The integrated system has been subjectively evaluated, obtaining very satisfactory results.

In recent years, Wireless Acoustic Sensor Networks (WASN) have been widely applied to different acoustic fields in outdoor and indoor environments. Most of these applications are oriented to locate or identify sources and measure specific features of the environment involved. In this paper, we study the application of a WASN for room acoustic measurements. To evaluate the acoustic characteristics, a set of Raspberry Pi 3 (RPi) has been used. One is used to play different acoustic signals and four are used to record at different points in the room simultaneously. The signals are sent wirelessly to a computer connected to a server, where using MATLAB we calculate both the impulse response (IR), and different acoustic parameters, such as the Speech Intelligibility Index (SII). In this way, the evaluation of room acoustic parameters with asynchronous IR measurements two different applications has been explored. Finally, the network features have been evaluated to assess the effectiveness of this system.

In educational context, a source of nuisance for students is carbon dioxide ($$CO_2$$ C O 2 ) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide ($$CO_2$$ C O 2 ), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

Las comunicaciones digitales se enseñan tradicionalmente en sesiones de laboratorio desde un punto de vista teórico, utilizando plataformas de simulación. Sin embargo, el sistema de Acreditación Académica incluye la dimensión de "lo que se espera que los estudiantes sean capaces de hacer", lo que plantea la necesidad de disminuir la brecha entre las sesiones de laboratorio simuladas y un enfoque más práctico y realista. En este trabajo, proponemos una metodología para mejorar el aprendizaje de los aspectos prácticos relacionados con los cursos de Comunicaciones Digitales, así como para aumentar la motivación de los estudiantes, mediante el uso de dispositivos de radio definida por software de bajo coste. Esta metodología se ha aplicado en el Grado en Ingeniería Telemática de la Escuela Técnica Superior de Ingeniería de la Universidad de Valencia, España. Con el objetivo de mejorar el compromiso de los estudiantes de máster y de grado, se ha implementado una metodología de aprendizaje basado en proyectos. En el trabajo se describe la metodología, se describen varias actividades de laboratorio basadas en el estándar 802.11 y se dan recomendaciones para la implementación de esta metodología. UV-SFPIE PID-1353656 Digital Communications have been traditionally taught in laboratory sessions from a theoretical point of view, using simulation platforms. However, current Academic Accreditation includes the dimension of “what students are expected to be able to do”, which poses the need of decreasing the gap between standard simulated laboratory sessions and more practical and realistic approaches. In this paper, we propose a methodology to enhance the learning of practical aspects related to Digital Communications courses, as well as increasing the student’s engagement, via the use of low-cost software defined radio devices. This methodology has been applied in the Degree in Telematics Engineering at the School of Engineering from the University of Valencia, Spain. With the aim of improving the engagement of both master and undergraduate students, a project based learning methodology has been implemented. In the paper, the methodology is described, several laboratory activities based on the 802.11 standard are presented, and recommendations for practice are given.

In Next-Generation Technologies, the monitoring of environmental noise nuisance in the Smart City should be as efficient as possible. 5G IoT systems offer a great opportunity to offload the node calculation, as they provide a number of new concepts for dynamic computing that previous technologies did not offer. In this case, a complete 5G IoT system for psycho-acoustic monitoring has been implemented using different options to offload the calculation of the parameters to different parts of the system. This offloading has been implemented by directly computing the metrics in the node (as a Raspberry Pi), and in a ESP32 device (FiPy) and by sampling the audio and sending it to the EDGE in the psycho-acoustic metrics algorithm in order to evaluate the performance of the system and has been compared with the calculation at the node itself.

The proliferation of commercial low-cost Small Board Computers (SBC) devices have allowed the deployment of many Wireless Sensor Networks (WSN) focused on different applications, mainly based on monitoring issues. These networks are characterized by a set of these SBCs devices working in a collaborative way where each device is sensing, processing and later sending out the data to the sink. These devices are equipped with power supply, a processing unit and communications capabilities (in particular WiFi), making themselves very interesting to fit in many topologies. However, their performance in terms of communications basically depends on the environment and usually heuristic techniques are used with any guarantee if they will succeed or not. To model the different scenarios there are theoretically several approaches, such as ray tracing technique, two-ray, as well as semi-empirical models, but it should be interesting to analyze them also from a experimental point of view. In this paper we carry out a thorough performance analysis, in particular using Raspberry Pi3 (RPi3) devices, in order to understand their behavior in terms of Quality of Services (throughput, delay, losses and jitter (delay variation) for different types of communications: indoor and outdoor environments, with and without multipath reflections in order to establish precisely the amount of signal required depending on the required througput. In the outdoor environments, we analyze the QoS over the links between two nodes in a deployment.We conclude that using this type of devices we can achieve a throughput of more than 45 Mbps in indoor within distances shorter than 50 m and 40 Mbps in outdoor till 100 m and then it starts decaying till 300 m in normal conditions.

Wireless sensor networks (WSN) are groups of small devices that contain a microcontroller in which a large number of sensors can be added. They transmit data and communicate to each other in the ISM band, standard IEEE 802.15.4, exchanging packets using a multi-hop routing. These devices are named motes and are nodes of the WSN. They are very simple and easy to program, powered by batteries of 1.5Volts (AA and AAA). The nodes are autonomous elements that can be deployed implementing any type of network. In a typical deployment the nodes communicate with each other and with a master node or Base Station (BS), which in turn transmits the information to an external server, which collects the environmental and any type of parameters collected by each sensor. WSNs are used in a multitude of military, industrial and civil applications. They use a very low amount of power, and this fact limits the maximum communication distance between motes within the WSN, which is around 50m for civil current motes. The Stochastic Collaborative Beamforming (SCB), was proposed as a way to overcome the low range. in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. However the dimensions of the WSN deployment are so large as to consider the effects of the near field. WSN with 200m diameter have Fresnel areas ranging to distances as far as 325Km. In the present work we present the theoretical background to analyze the SCB transmission of a cluster of radiating motes, and also the theoretical planning for a SCB long range communication in the Albufera de Valencia natural reservation area, some preliminary simulations and measurements are also presented.

Reducing energy consumption has become a hot research topic in the past few years. The concept of Green Networking is gaining popularity not only because of the growing environmental awareness worldwide but also the economic opportunities and benefits of reducing energy consumption in the ICT industry. In this work, we will identify the importance of green research and explore the state of the art in the field with a special focus on green base stations. After identifying the key green metrics, we will explore the energy efficiency of traditional base stations and review the main possible improvements to its current structure as a standalone unit as well as the base station as a component in a cellular network. We find that a substantial mitigation of CO2 emissions from 2.4% to 7% of total emissions could be achieved, where the use of renewable energies is economically and technically feasible.

This paper reports on the work being done towards achieving an immersive and accessible reconstruction of Historical and Cultural Heritage, focusing on Theaters of the Early Modern as use case. In particular, the paper presents and discusses potential possibilities to enable: 1) the acoustical reconstruction of the virtual (lost) environments – beyond the graphical reconstruction for the buildings, elements and performances; 2) effective interaction features and navigation within the virtual environment (e.g. by means of adaptive interfaces, guiding methods, insertion of Point of Interest); and 3) accessible experiences, by means of an innovative and personalized presentation modes for access services, like subtitles and audio description. For most of these aspects and features, proof of concept implementation are provided, and opportunities for future work are outlined. With an effective combination of all these contributions, the goal is to bring back valuable (both tangible and intangible) Cultural Heritage from the past, providing high benefits in relevant sectors like Culture, Tourism and Education.

Nowadays, more than 75% of Internet traffic is multimedia traffic, moreover mobile traffic is growing at a rate of 50% each year. All these data together with the evolution of the cloud infrastructures lead us to develop Cloud Mobile Media (CMM) architectures to support the needs demanded by end users. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience (QoE) of the delivered video. Thus, to keep client’s satisfaction within good levels in terms of Mean Opinion Score (MOS), we propose an adaptive QoE-based architecture running on CMM infrastructures for live streaming services. In order to carry out this goal, we propose an estimation of MOS values using an statistical method based on factor analysis. This estimation is based on different measured variables throughout the CMM infrastructure. In addition, we compare the accuracy of the estimated MOS against well-known publicly available video quality algorithms. With these estimations, our proposal is based on two added controllers to the CMM infrastructure: (a) the Software Defined Network controller that acts as a master and (b) the Media Streamer controller. Each one does different actions on the CMM infrastructure in order to maintain and improve the QoE at each end user. Finally, this architecture has been implemented over a fat tree topology in order to show their functionality. The results show that our proposal works properly and it adapts quickly to the network changes in order to deliver a good MOS.

Microelectromechanical resonators nonlinearities can be exploited in many ways to obtain a set of diverse new applications. In particular, some applications of bistable behavior includes threshold mechanical switches, memory cells, energy harvesting and chaotic signal generators. A key step for practical and efficient design for bistability behavior involves accounting for accurate and efficient models. In this paper we present a nonlinear electromechanical model for capacitive clamped-clamped beam resonators implemented in an analog hardware description language (AHDL) enabling system level electrical simulations. The model accounts for nonlinearities from variable resonator-electrode gap, residual fabrication stress, fringing field contributions as well as an accurate resonator deflection profile in contrast to parallel plate approximations. The model has been applied to derive for first time accurate analytical expressions for bistability design conditions. The work includes FEM analysis and experimental data that corroborates the correctness of the model in describing the required bias voltage conditions for bistability.

A wireless acoustic sensor network comprises a distributed group of devices equipped with audio transducers. Typically, these devices can interoperate with each other using wireless links and perform collaborative audio signal processing. Ranging and self-positioning of the network nodes are examples of tasks that can be carried out collaboratively using acoustic signals. However, the environmental conditions can distort the emitted signals and complicate the ranging process. In this context, the selection of proper acoustic signals can facilitate the attainment of this goal and improve the localization accuracy. This letter deals with the design and evaluation of acoustic probe signals allowing the implementation of ranging applications using unsynchronized audio signals acquired by different network nodes. The proposed signals have been evaluated in diverse simulated environments and implemented in a real deployment. The results confirm the suitability of these signals for implementing localization applications with an accuracy that is in the order of 1 cm.

Current mobile service providers are offering Gigabit Internet access over LTE-Advanced networks. Traditional services, such as live video streaming, over wired networks are feasible on these networks. However different aspects should be taken into account due to the fast changing network conditions as well as the constrained resources of the mobile phones, in order to provide a good subjective video quality in terms of Mean Opinion Score (MOS). Our goal is to estimate and predict this subjective metric without information or reference from the original video, known as Non Reference approach. This approach is important for the Service Provider from a practical point of view, because it can keep the customer satisfaction at good levels. We analyze different estimation techniques running over a set of monitored variables throughout the whole steaming system, from the streaming server to the mobile phone. We have gathered variables related to bit stream, basic video quality metrics as well as Quality of Services variables. These variables are used to estimate MOS in a reliable and robust way. We compare three techniques such as Artificial Neural Networks (ANN), Factor Analysis (FA) and Multinomial Linear Regression, at different time scales and with Full Reference and Non Reference approaches. We carry out a performance evaluation of these techniques, concluding that the behavior of MOS estimation based on FA is more accurate, unless we had a lossless scenario related to Guaranteed Bit Rate services, where ANN performs better. The subjective video quality has been evaluated through surveys. Finally, we evaluate the accuracy of the estimated MOS against well known publicly available video quality algorithms following the recommendations given by Video Quality Experts Group (VQEG).

The adoption of smart phones, the increased access to mobile broadband networks and the availability of public cloud infrastructures are aligning to the next generation of truly ubiquitous multimedia services, known as Cloud Mobile Media (CMM) services offering mobile video. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear. One challenge is given by live video streaming applications when trying to keep a good Quality of Experience of the delivered video, measured in terms of a subjective video quality metric, named Mean Opinion Score (MOS). Our goal is to estimate and predict this subjective metric in a holistic manner using different estimation techniques, such as Artificial Neural Networks, Factor Analysis and Multinomial Linear Regression, with Full Reference and Non Reference approaches. For this, we have analyzed and measured different variables related to Quality of Service, bit stream and basic video quality metrics, throughout the CMM infrastructure. With these variables, we apply the mentioned techniques which allows us to estimate MOS of the delivered video in a robust and reliable way, achieving an average estimation error between 0.46 and 15.94% depending on the technique used. The real MOS has been evaluated through surveys. Finally, we compare the accuracy of the estimated MOS against well known publicly available video quality algorithms, following the recommendations given by Video Quality Experts Group.

Current mobile networks are providing high speed access to Internet at a rate of Gigabits per second. In this scenario, traditional services over wired networks are an alternative, in particular those based on live video streaming. But in the transition, different issues should be considered due to the rapid changing network conditions and the limited resources of the mobile devices. These issues should be taken into account to keep a good Quality of Experience (QoE) of the video in terms of a high Mean Opinion Score (MOS), a subjective video quality. Our goal is to estimate and predict this subjective metric in a holistic manner. Thus, we have analyzed and measured different variables related to Quality of Service, bit stream and basic video quality metrics of live video streaming over LTE Advanced (4G) mobile networks, as well as server side. With these variables, we apply a statistical method, called Factor Analysis based on the correlation of the variables, which allows us to estimate MOS of the delivered video in a robust and reliable way, both in Full Reference and Non Reference approaches. The real MOS has been evaluated through surveys. Finally, we compare the accuracy of the estimated MOS for the different approaches and compare their performance with well known publicly available video quality algorithms.

Lightning and thunder are some of the most violent natural phenomena. They generate a great deal of expenditure and economic loss, especially when they strike in cities. The identification of the concrete geographic area where they strike is of critical importance for emergency services in order to enhance their effectiveness by doing an intensive coverage of the affected area. To achieve this purpose at the city scale, this paper proposes the design, prototype, and validation of a distributed network of Internet of Things (IoT) devices. The IoT devices are empowered with lightning detection capabilities and are synchronized with the other devices in the sensor network. All of them cooperate within a network that is able to locate different events thanks to a trilateration algorithm implemented in a big data environment. The designed low-cost lightning detection system is based on the AS3935 sensor. This system alone has a limited range of effective detection, but when it is embedded in an IoT mesh network, the accuracy and performance are increased. A fully operational IoT network was deployed, and a functional validation and empirical measurements are provided.

The Internet of Things (IoT) is a network widely used with the purpose of connecting almost everything, everywhere to the Internet. To cope with this goal, low cost nodes are being used, otherwise, it would be very expensive to expand so fast. These networks are set up with small distributed devices (nodes) that have a power supply, processing unit, memory, sensors, and wireless communications. In the market, we can find different alternatives for these devices, such as small board computers (SBCs), e.g., Raspberry Pi (RPi)), with different features. Usually these devices run a coarse version of a Linux operating system. Nevertheless, there are many scenarios that require enhanced computational power that these nodes alone are unable to provide. In this context, we need to introduce a kind of collaboration among the devices to overcome their constraints. We based our solution in a combination of clustering techniques (building a mesh network using their wireless capabilities), at the same time we try to orchestrate the resources in order to improve their processing capabilities in an elastic computing fashion. This paradigm is called fog computing on IoT. We propose in this paper the use of cloud computing technologies, such as Linux containers, based on Docker, and a container orchestration platform (COP) to run on the top of a cluster of these nodes, but adapted to the fog computing paradigm. Notice that these technologies are open source and developed for Linux operating system. As an example, in our results we show an IoT application for soundscape monitoring as a proof of concept that it will allow us to compare different alternatives in its design and implementation, in particular, with regard to the COP selection, between Docker Swarm and Kubernetes. We conclude that using and combining these techniques, we can improve the overall computation capabilities of these IoT nodes within a fog computing paradigm.

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

In the last decade there has been an increasing interest and demand on the Internet of Things (IoT) and its applications. But, when a high level of computing and/or real time processing is required for these applications, different problems arise due to their requirements. In this context, low cost autonomous and distributed Small Board Computers (SBC) devices, with processing, storage capabilities and wireless communications can assist these IoT networks. Usually, these SBC devices run an operating system based on Linux. In this scenario, container-based technologies and fog computing are an interesting approach and both have led to a new paradigm in how devices cooperate, improving overall capacity in a cluster of these SBC devices. The use of containers is considered a lightweight virtualization, allowing an application to be broken into small tasks as services, enabling load balancing, flexibility and scalability. Nevertheless when the number of devices and containers increases in the cluster, it is required an orchestration layer. There are not many solutions and available alternatives using these technologies applied on these networks, and less an assessment of their performances. This paper focuses on these technologies when we use fog computing with low cost SBC devices in a context of IoT. We use Linux containers and different available orchestration platforms (in particular Docker Swarm and Kubernetes), to run on the top of the cluster of commercial SBC devices. Thus, we carry out a thorough functional and performance comparison with different real topologies (wired and wireless) and using both homogeneous and heterogeneous clusters of SBC devices, showing their results. We conclude that with the collected experimental results, Docker Swarm orchestration platform outperforms its counterparts in the scenarios shown.

The evaluation of QoS in mobile multipath environments is an interesting topic in modern communications. The use of Raspberry Pi as a low cost microcomputer board developed in the United Kingdom by the Raspberry Pi Foundation can be helpful in onsite deployments. The Pi3 model has integrated WiFi and Bluetooth connectivity and also 4 USB ports to expand the device with all kinds of peripheral. Under the premise of offering the power of a computer at low prices, this device is one of the most affordable ways to have a particularly efficient and dynamic hardware accessible at a low price to develop a Wireless Sensor Netwok (WSN). The use of the Raspberry Pi3 permits the development of a powerful and hight efficient mote in the development of a WSN, where data is highly processed in each mote and transmissions between motes and the sink or base station (BS) can be keep at minimum. We develop an experimental setup to analyse the quality of transmission between these devices in order to a further modelization of the channel. We program the Raspberry Pi3 to evaluate the quality of the transmission between two devices within a controlled multipath environment, particularly within an anechoic chamber.

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These devices are constituted by a microcontroller, sensors, and they have the ability to communicate in the ISM frequency band using the IEEE 802.15.4 standard. They have their own power supply, AA or AAA batteries, processing unit, sensors and wireless communications. Usually, the motes exchange packets using a multihop routing, and the maximum communication distance emitter-receiver is around 100m. At least one mote acts as a gateway, and the data gathered by the sensors of each mote have to be sent to this mote that is named sink or Base Station (BS). In a WSN the BS is within the area of the WSN in order to receive the information. The distance between the BS and the nearest mote must be at the most 100m to be able to receive the packets. If the BS distance is farther than this distance, the communication fails, because the motes have limited power. Some techniques are proposed to overcome this distance in order to achieve a long range communication to reach the BS, for instance to communicate to a low orbit satellite. The difficulties are related to synchronization errors in the clocks of the motes. We propose an alternative technique based in a collaborative beamforming scheme, named stochastic collaborative transmission or stochastic beamforming, in which we take advantage of the synchronization errors of the clocks to achieve a large gain in a limited number of trials.

eHealth systems provide medical support to users and contribute to the development of mobile and quality health care. They also provide results on the prevention and follow-up of diseases by monitoring health-status indicators and methodical data gathering in patients. Telematic management of health services by means of the Internet of Things provides immediate support and it is cheaper than conventional physical presence methods. Currently, wireless communications and sensor networks allow a person or group to be monitored remotely. The aim of this paper is to develop and assess a system for monitoring physiological parameters to be applied in different scenarios, such as health or sports. This system is based on a distributed architecture, where physiological data of a person are collected by several sensors, next, a Raspberry Pi joins the information and makes a standardization process, then, these data are sent to the Cloud to be processed. Our Cloud system stores the received data and makes a data fusion process in order to indicate the athlete&rsquo, s fatigue status at every moment. This system has been tested in collaboration with a small group of voluntary tri-athletes. A network simulation has been performed to plan a monitoring network for a bigger group of athletes. Finally, we have found that this system is useful for medium-term monitoring of the sports activities.

The biasing requirements to obtain cross-well chaotic motion for in-plane electrostatically driven beam-shaped MEMS (micro-electromechanical systems) resonators are investigated for typical actuation/readout topologies. Practical applications such as signal chaotic generators require reasonable and wide enough range voltages (dc and ac) to assure a feasible control in current CMOS or nanotechnologies. We found that these conditions are only obtained when enabling cross-well motion operation instead of in-well operation. Moreover, in contrast to many works that take unfeasible design parameters, we provide for the first time the design rules and biasing ranges required for commercial CMOS technologies and nanotechnology when considering typical parameter values from various fabrication approaches.

We analyze the evolution of SRAM memory logic contents under irradiation by defining the memory state as the number of cells storing a given logic value (i.e. number of cells storing a logic-1). We find that the memory state evolution under irradiation follows an Ehrenfest urn model due to the constant effect of single event upsets, and that in large memories it can be associated to an Ornstein-Uhlenbeck process. Memory state transient analysis has been applied to determine the device Soft error rate for an SRAM fabricated in a 65 nm commercial CMOS process obtaining a very good correlation. Furthermore, our analysis shows that the technique is applicable to systems composed by various dissimilar memory components, providing–under certain circumstances–the specific Soft Error Rate of each component.

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system. Ingeniería, Industria y Construcción

Although much work has been done since wireless sensor networks appeared, there is not a great deal of information available on real deployments that incorporate basic features associated with these networks, in particular multihop routing and long lifetimes features. In this article, an environmental monitoring application (Internet of Things oriented) is described, where temperature and relative humidity samples are taken by each mote at a rate of 2 samples/min and sent to a sink using multihop routing. Our goal is to analyse the different strategies to gather the information from the different motes in this context. The trade-offs between ‘sending always’ and ‘buffering locally’ approaches were analysed and validated experimentally, taking into account power consumption, lifetime, efficiency and reliability. When buffering locally, different options were considered such as saving in either local RAM or FLASH memory, as well different alternatives to reduce overhead with different packet sizes. The conclusion is that in terms of energy and durability, the best option is to reduce the overhead. Nevertheless, sending larger packets is not worthy when the probability of retransmission is high. If real-time monitoring is required, then sending always is better than buffering locally.

[EN] We present the results from a measurementbasedperformance evaluation of wireless networks basedon IEEE 802.11ac standard in an indoor environment, withthe aim to analyze their performance under high definitionstreaming video applications. We focus our study on analyzingthe highest performance of these standards using off-theshelfequipment as well as the behavior of Quality of Servicevariables and how they affect to the video quality. Thus, wehave analyzed and measured these variables and have applieda multivariate statistical technique, called Factor Analysis,and finally discuss their behavior., This work was supported by the Universitat de Valencia under the projects UV-INV-PRECOMP14-207134, UVINVAE15-339582, by the Generalitat Valenciana under the project GV-2016-002 and by the Ministry of Economy under the project BIA2016-76957-C3-1-R

We present the results from a measurement-based performance evaluation of wireless networks based on IEEE 802.11n and IEEE 802.11ac standards in an indoor environment, with the aim to analyze their performance under high definition streaming video applications. We focus our study on analyzing the highest performance of these standards using off-the-shelf equipment, both with synthetic TCP and UDP traffic to measure the saturation throughput as well as high definition video streams. The measurements have been conducted in the student labs of our university and show good performance for streaming purposes in high definition and also ultra-high definition from a subjective video quality point of view. IEEE 802.11ac performance is very sensitive to the channel conditions. According to our experiments, these results heavily depend on the implementation of the IEEE 802.11ac standard of the commercial access points.

Recent studies about the use of cloud platforms indicate that more than three-fourths of healthcare institutions apply these technologies in their daily lives. However, in most cases the cloud is based on “Infrastructure as a Service”, instead of developing new applications using the benefits that can bring us this type of technology. For these reasons, this paper shows a private cloud-based system to provide a new application based on thermographic video streaming. In this case, the cloud architecture is divided into three zones. The first zone is where the patient is under medical supervision through body temperature analysis using a global thermal camera. The second zone is the cloud itself, where the video is encoded, but taking into account that the received video must ensure a correct measurement of the temperature. The third zone is the access network, where people or processes receive and analyze these videos. Finally, a performance evaluation using several video codecs and bitrates is done in order to see which one is more accurate and fits better medical requirements. Finally, we conclude that the system works properly, indicating which video codecs are more suitable for each scenario.

The increasing adoption of smart phones, the increased access to mobile broadband networks and the availability of public cloud infrastructures are aligning to the next generation of truly ubiquitous multimedia services, known as Cloud Mobile Media (CMM) services. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience of the delivered video. Thus, to keep client's satisfaction at good levels in terms of Mean Opinion Score (MOS), we propose an adaptive Software Defined Networking (SDN)-based architecture running on CMM. We estimate MOS using an statistical method based on Factor Analysis (FA) and then the SDN controller is able to schedule different actions. The MOS estimation is based on different measured variables throughout the CMM infrastructure. Finally, we compare the accuracy of the estimated MOS against well known publicly available video quality algorithms as well as we propose an algorithm to run at the SDN controller to meet these requirements.

We analyze the benefits of replacing selected MOSFET transistors by nanoelectromechanical relays within conventional CMOS six transistor SRAM cells. Specifically, we evaluate a potential implementation that uses a cantilever designed with a 65 nm standard CMOS technology. The impact on various reliability metrics like static noise margin and write noise margin are evaluated from circuit simulations using a nanomechanical relay Verilog-A compact model. Comparisons are performed between a 65 nm CMOS 6T conventional SRAM cell and various hybrid memory cells constructed by replacing selected MOSFET transistors with cantilever relays. The impact on other important memory cell parameters such as area, timing performance and power consumption is also discussed.

Wireless acoustic sensor networks (WASNs) are known for their potential applications in multiple areas, such as audio-based surveillance, binaural hearing aids or advanced acoustic monitoring. The knowledge of the spatial position of a source of interest is usually a requirement for many of these applications. Therefore, source localization is an important problem to be addressed in WASNs. Unfortunately, most localization algorithms need costly signal processing stages that prevent them from being implemented in low-cost sensor networks, requiring additional modules for signal acquisition and processing. This paper presents a low-complexity method for acoustic event detection and localization considering a change detection statistical framework. Two possible implementation approaches based on the efficient cumulative sum (CUSUM) algorithm are presented and discussed. Results from simulations and a real deployment show that the proposed techniques can be easily implemented in low-cost sensor networks, providing good localization accuracy and making good use of the available node resources.

We report and analyze the dependence of complex gates delay with the sensitization vector and its variation-that gets up to 40% in 65-nm CMOS technologies-and include its effect in the path delay estimation-that can be in the order of 16%. The gate delay is computed from a simple polynomial analytical description that requires a one-time library parameter extraction process, making it highly scalable. An STA tool based on a single-pass true path computation is used to determine the critical path list. Since it does not rely on a two-step process, it can be programmed to find efficiently the N true paths from a circuit. Results from various benchmark circuits synthesized for three commercial technologies (130, 90, and 65 nm) provide better results in number of paths reported and delay estimation for these paths compared to a commercial tool. The impact of delay variation with the sensitization vector for paths with complex gates reveals as a significant mechanism that must be considered as it is comparable to the impact of parameter variations or interconnect-induced delay.

Finding a parameter which allows establishing if a concert room is good for orchestral classical music performances or not is one of the main interests in room acoustics. However, rooms are usually characterized by average values of some acoustic parameters. From the optimal values mostly recognized, specialists can consider a room adequate or not for a certain use. In recent works, we have obtain we have obtained a model for classifying a room for classical music performances, from the reverberation time (T30), the lateral energy factor coefficient (LFC) and the listener envelopment (LEV). From this model, we can determine the quality maps for these rooms. In this paper, we show the quality maps for five rooms. From these results we can deduce some basic rules to define a good acoustic design.

The cell static noise margin (SNM) is widely used as a stability criterion for static random-access memory cells design. This parameter is typically determined through electrical simulations since direct experimental characterization of SNM is not achievable. In this work, we present a methodology that provides an indirect measurement of the SNM on a per-cell basis for six-transistor SRAMs. It is based on combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) with circuit-level cell experimentally measurable parameters as input variables to the tool. We show that it is possible to obtain the SNM for individual memory cells using the same experimental setup and data than that required for shmoo plot measurements. Results confirm that the SNM can be experimentally estimated with a relative error compared with electrical simulations that is below 0.5%. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless Sensor Networks (WSNs) is a group of spatially dispersed autonomous sensor devices, named motes. These motes have a microcontroller, sensors, are powered by AA or AAA batteries, and mainly have the ability to communicate using the IEEE 802.15.4 standard. The motes communicate between them inside the WSN exchanging packets using a multi-hop routing. They use a very low amount of power (below 100 mW). This limits the maximum communication distance between motes within the WSN. Usually, one mote acts as a gateway to other networks and this mote is also called sink or simply Base Station (BS), and the data collected by the sensors of each mote are sent to this mote. The maximum distance between the BS and the nearest mote is below 100 m because of the power limitations of the motes. If the WSN-BS distance is above this boundary, the communication will surely fail. We propose a new technique in order to achieve a long range communication from the WSN, for instance to communicate to a Low Earth Orbit (LEO) satellite. Many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications are found, however the synchronization of clocks is an almost impossible task given the simplicity and cheapness of the architecture of the motes. To overcome this problem, we propose a new technique, named Stochastic Collaborative Beamforming (SCB), in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. This gain is obtained from interfering independent signals coming from each mote of the WSN, using a repetition scheme. Although it does not get all the nominal gain that could be obtained in case of a perfect synchronization, it does get a sufficient gain to reach the BS with limited power consumption.

This article combines a Neural Network (NN) algorithm with the Finite Difference Time Domain (FDTD) technique to estimate the eigenfunctions in quantum devices. A NN based on the Least Mean Squares (LMS) algorithm is combined with the FDTD technique to provide a first approach to the confined states in quantum wires. The proposed technique is in good agreement with analytical results and is more efficient than FDTD combined with the Fourier Transform. This technique is used to cal- culate a numerical approximation to the eigenfunctions associated to quan- tum wire potentials. The performance and convergence of the proposed technique are also presented in this article. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2017-2022, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop. 24562

We present an accurate model to estimate the energy and delay of domino CMOS gates derived from a detailed description of internal capacitance switching and discharging currents. The delay dependence with the position of the switching transistor in the gate is accurately described with the advantage that it does not include additional empirical parameters, thus providing the propagation delay in terms of foundry-provided MOSFET parameters. Results show a very high accuracy with a relative error lower than a 3% with respect to HSPICE for a 0.18-/spl mu/m CMOS technology providing up to three orders of magnitude of speed improvement. The analytical nature of the model makes it suitable for circuit optimization and is the basis for a quick estimation of ULSI circuits power and delay when used in circuit simulation tools.

This article discusses the major gate oxide failure modes, reliability modeling, burn-in, and qualification testing. We present a typical method to calculate failure rates.

We present two versions of a radiation dosimeter, they are CMOS circuits built around a PMOSFET radiation sensor, compatible with standard technology. These dosimeters are intended to be used as built-in sensors in integrated circuits to signal misfunction danger due to radiation exposure, and so they have a binary output which changes its state when the total radiation dose exceeds a prefixed level. The first circuit is an improved version of a previously developed dosimeter which has less power consumption and sharper transfer characteristics. It has been built in ES2 1.0 µm technology and we present experimental results. The second circuit is under development, it is a clocked dosimeter which samples the dose only during a very short time at each clock period and then remains at rest, to further reduce the power consumption.