The main contribution is the design, implementation and validation of a complete AI-driven electronic nose architecture to perform the classification of whiskey and acetones. This classification is of paramount important in the distillery production line of whiskey in order to predict the quality of the final product. In this work, we investigate the application of an e-nose (based on arrays of single-walled carbon nanotubes) to the distinction of two different substances, such as whiskey and acetone (as a subproduct of the distillation process), and discrimination of three different types of the same substance, such as three types of whiskies. We investigated different strategies to classify the odor data and provided a suitable approach based on random forest with accuracy of 99% and with inference times under 1.8 seconds. In the case of clearly different substances, as subproducts of the whiskey distillation process, the procedure presented achieves a high accuracy in the classification process, with an accuracy around 96%.

Weeds are a major threat to agricultural productivity, as they have a significant influence on crops. Their presence leads to increased production costs, derived mainly from crop wastage, which negatively affects the global agricultural economy. Aware of the seriousness of this problem, the research community is actively exploring technological solutions to help farmers quickly identify and tackle weed infestations. Automatic identification and location of these weeds can help farmers to increase the productivity and efficiency of their crops.

Air Quality (AQ) is an important issue in our cities, as it has an impact on the health of citizens. Nowadays with advanced communications and a high degree of digitisation, smart cities can integrate AQ monitoring networks based on wireless Internet of Things (IoT) sensor nodes, which for large deployments are often built with low-cost and less accurate components. In this paper, we focus on the calibration process of ozone Low-Cost Sensors (LCS) in order to improve their accuracy by using machine learning ensemble techniques and reduce their error compared with official measurements of this parameter in a real deployment. We have tested and compared different techniques, reducing the error of the reading values of these LCSs by around 90%, with the best results given by the Gradient Boosting algorithm.

Identification of animal species is a crucial aspect of biology and ecology, particularly in ornithology, where collaboration with other disciplines aims to develop effective methods for bird protection and environmental quality assessment. Leveraging artificial intelligence (AI) and Internet of Things (IoT) technologies, advancements in birdsong identification have been achieved. Machine learning and deep learning techniques, including Imagenet-based Convolutional Neural Networks (CNNs) like EfficientNet and MobileNet, have been employed for image feature comparison. Spectrograms of birdsongs have been analyzed, with Deep CNNs (DCNNs) proving effective in reducing model size for birdsong classification.

Industry 4.0 is currently viewed as the future era in which a new generation of wireless communications will enable the widespread connectivity between machines, objects, and users, promising significant advancements in industrial automation. Industrial scenarios are highly dynamic environments that require the ability to adapt to changes in operating conditions. Reliable wireless communications can be challenging in industrial environments, where the communications signal is often blocked due to the presence of industrial machinery, with possible intermittent and/or unpredictable blockages. This paper analyzes the wireless coverage provided by a low-cost IoT system in an industrial environment. A measurement campaign in different points of an industrial warehouse were carried out, showing substantial differences in terms of coverage between a totally empty area and an area with machinery and workers. The results show that the IoT system is able to provide enough coverage around the whole area, even with mobility or blockages.

Internet of Things (IoT) is a network made up of different types of devices integrated into the Internet. For the use of IoT in cities to assist humans beings in their daily activities, it is necessary to extract and process as much information as possible and therefore, semantic web technologies are a key point. This new environment is known as the Semantic Web of Things (SWoT). The application of semantic techniques to IoT can improve interoperability, effective access to data, discovery of integration resources, reasoning and processing of knowledge extraction from data, opening up opportunities for new applications. We show an overview of the most relevant semantic technologies, focusing on SWoT and well-accepted ontologies for IoT and we stress that considering users’ preferences, interests and needs are of vital importance, as a complement to the semantic information extracted. This new scenario enables the development of advanced applications and services to meet user requirements and needs, in particular for smart city applications.

The Stochastic Cooperative Beamforming (SCB) concept is analyzed from the point of view of maximum simplicity of implementation. The mismatches of the motes’ clocks after synchronization and their effect on the signal phase shift are analyzed. The dispersion of the clock errors with deviations of the order of a hundred nanoseconds introduce random shifts that allow the signal to be sent in several directions randomly. This multi-directional pointing multi-CB is achieved with a gain that is lower than the maximum expected for an optimal delay distribution. However, although with a low probability, high transmission gains of up to 50% of the maximum theoretical gain are achieved. It is observed that the gain conforms to a Rayleigh distribution function, and that the SCB transmission procedure allows sending to distant receivers in any direction. In SCB transmission a percentage of packets would be lost, and this would result in a higher number of transmissions, with the consequent energy consumption and decrease in effective transmission speed.

The environmental psycho-acoustic annoyance is an important metric in the Smart City with Next Generation technologies. The use of such technologies can help to rapidly deploy large quantity of elements, which can dynamically be used for different applications. Also, the psycho-acoustic annoyance is usually based on the Zwicker’s model, but this model does not consider tonality of sounds to weight the subjective nuisance produced. In this work, we show a on-going work for the implementation of the nodes and EDGE/Fog to determine a modified version of the Zwicker’s model, to consider sound tonality (based on Aures’ method). This implementation has been designed to consider two options for offloading, one with sampling and computation on the EDGE and another with sampling in 5G-nodes and computation on the EDGE.

The emergence of commercial Ultra Wide Band (UWB) transceivers has enabled the development of Wireless Sensor Networks based on this communication technology. The implementation of time synchronization protocols is of great importance in these networks, as they allow distributed and collaborative data acquisition. This paper shows the improvement in the synchronization accuracy that can be achieved applying long term synchronization protocols to time stamps acquired with UWB transceivers. Provided results show that an accuracy of tens of nanoseconds can be achieved using the internal microprocessor timers, and a sub-nanosecond time accuracy between the timers onboard the UWB transceiver.

The Finite Difference in Time Domain numerical (FDTD) method is a well know and mature technique in computational electrodynamics. Usually FDTD is used in the analysis of electromagnetic structures, and antennas. However still there is a high computational burden, which is a limitation for use in combination with optimization algorithms. The parallelization of FDTD to calculate in GPU is possible using Matlab and CUDA tools. For instance, the simulation of a planar array, with a three dimensional FDTD mesh 790x276x588, for 6200 time steps, takes one day -elapsed time- using the CPU of an Intel Core i3 at 2.4GHz in a personal computer, 8Gb RAM. This time is reduced 120 times when the calculation is parallelized and carried out in a Graphics Processing Unit (GPU) NVIDIA GeForce GTX 1080 Ti 11264 MB GDDR 5X. The elapsed time is reduced substantially, but also the simplicity of calculation and usefulness of a Matlab implementation. The elapsed time reduction is so substantial that the FDTD-Matlab-CUDA can be combined with optimization algorithms.

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.

Lightnings are violent natural phenomena and can generate many expenditures, specially when they strike in urban areas. 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 aim, this paper proposes a design, prototype and validation of a distributed network of Internet of Things (IoT) devices to enable detection and location of lightning strikes. 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 alone device has a limited range of effective detection, but when it is embedded in a IoT mesh network, the accuracy and performance is increased up to good levels, in the order of kilometres. A fully operational IoT network has been deployed and a functional validation and empirical measurements are provided.

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 València 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.

Wireless Sensor Networks (WSNs) are based on tiny autonomous devices, usually named motes or nodes, and are used in many monitoring applications, such as environment, habitat, precision horticulture, etc. These motes are power-limited and most energy is wasted through the communication process when exchanging data packets. Because these motes are quite simple, with low power transmission and simple antennas, these networks usually run multihop routing protocols in order to forward packets between one mote to the sink, or the collector node, using the other motes as relaying motes. Basically, these routing protocols try to find out new routes within these wireless network, optimizing different criteria, such as number of hops, network life time, throughput, etc. However, if something is going wrong within the network (big interferences, problems in one node, unstable routes, etc.), due to the lack of information retrieved related to management purpose, it is difficult to fix it and many times the outcome is critical, leaving the network out of service. To avoid these issues, we show in this paper a web tool for path monitoring and analysis within the WSN in order to improve their performance and to plan ahead this kind of problems. In this paper, we show the different alternatives in the design of this tool and the different rules applied when trying to find out the inner problems in the network. In this kind of network, using the routing packets, we can get further information of the inner problems and infer them based on the statistics and rules applied.

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.

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.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring by Global Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced.