In this paper, the performance of Low-Cost Sensors (LCS) for Air Quality (AQ) monitoring is analyzed. These sensors are unstable, unreliable and cannot replace the official ones. They suffer from deviations, drifts and errors that make them invalid for direct use, but through Machine Learning (ML) techniques, they can increase their overall accuracy. These sensors can be used in smart cities, increasing the sampling density of the AQ monitoring network, which could enable the development of new applications for citizens to plan healthy routes. After a review of the different AQ LCS, we focused on the Winsen’s ZPHS01B multisensor module because it embeds 11 different sensors in the same module. Since the information and the experimental data from field tests for this module is limited, we carry out different experiments and perform a thorough analysis, evaluating the sensor reading differences that could appear in a batch of them. We note that some of the integrated sensors are more reliable than others, but in practice they can improve their reading by using the ML models mentioned above, as they show correlation. Of all the modules tested, we observed that most of the sensors showed similar performance. However, a certain percentage of sensors in certain modules performed worse than their counterparts, which shows that at least 80% of the tested multi-sensor modules had similar levels of performance.

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.

Modern applications of wireless sensor networks such as the conduction of collaborative tasks, the distributed detection of events or the alignment of data samples gathered by several nodes, require a precise time coordination of the nodes composing the network. Ultra Wide Band (UWB) technology has enabled the development of precise time synchronization protocols. The intrinsic properties of this technology allow an accurate estimation of the time of flight (ToF) of the radio signal in scenarios covering short distances. The integration of UWB transceivers in Wireless Sensor Networks (WSN) nodes is nowadays possible due to the availability of commercial chips implementing this technology. This combination facilitates the development of accurate time synchronization protocols that establish a global common clock throughout the network. In this context, the main contribution of this work is a protocol that allows the synchronization of the local clocks of these nodes with a common global reference kept in the master node. The master periodically broadcasts synchronization packets to keep the rest of the nodes synchronized. However, since the precision achieved in the time synchronization of the local clocks with UWB technology is comparable to the ToF of the RF signal for distances in the order of several meters, some considerations to estimate this delay are required. In this paper, a new algorithm is proposed that performs the estimation of the ToF of the UWB signal as well as the implementation of linear models to synchronize the local clocks of the network node, eliminating the dependence on the ToF. The experiments conducted with real devices reveal that an accuracy of tens of nanoseconds can be achieved regardless of the distance separating the network nodes.

Room acoustical parameters have been widely used to describe sound perception in indoor environments, such as concert halls, conference rooms, etc. Many of them have been standardized and often have a high computational demand. With the increasing presence of deep learning approaches in automatic monitoring systems, wireless acoustic sensor networks (WASNs) offer great potential to facilitate the estimation of such parameters. In this scenario, convolutional neural networks (CNNs) offer significant reductions in the computational requirements for in-node parameter predictions, enabling the so-called Artificial Intelligence-Internet of Things (AI-IoT). In this article, we describe the design and analysis of a CNN trained to predict simultaneously a set of common room acoustical parameters directly from speech signals, without the need for specific impulse response measurements. The results show that the proposed CNN-based prediction of room acoustical parameters and speech intelligibility achieves a relative error rate of less than a 5.5%, accompanied by a computational speedup factor close to 250 with respect to the conventional signal processing approach.

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 emergence of low cost Ultra Wide Band (UWB) transceivers has enabled the implementation of Wireless Sensor Networks (WSN) based on this communication technology. These networks are composed of distributed autonomous low cost nodes (also known as motes) with their own processing unit, memory and communications. Usually these nodes are power-limited and due to the poor performance and quality of their clocks, time synchronization is in the order of milliseconds and in some specific scenarios till microseconds. The integration of commercial UWB transceivers in these nodes can improve the synchronization accuracy. In particular, we focus on WSN nodes based on off-the-shelf commercial products and commodity hardware. In this paper we analyze step by step, from a practical and experimental point of view, the different elements involved in the time synchronization using UWB technology on WSN with static nodes. From our experimental results, with timestamps captured during the packet exchanges, we analyze and discuss the application of different communication schemes and simple statistical methods (in order to be run in WSN nodes). The results obtained with timestamps captured at the UWB transceivers and by using linear regression show that the lowest time synchronization error achieved between two nodes is 0.14 ns. Employing the same setup and performing the synchronization with the timestamps captured internally at the microcontrollers of the nodes, the error rises to 31 ns, due to the higher time period of the microcontrollers’ timers and the inaccuracies that affect the acquisition of the timestamps. Nevertheless, the synchronization of the microcontrollers’ clocks allows the setting up of a common time reference at the network nodes, enabling the implementation of applications with tight synchronization requirements, such as collaborative beamforming and ranging.

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.

Abstract 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.

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.

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.

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.

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.

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.

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.

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.

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.

There are many areas where Wireless Sensor Networks (WSN) can offer innovative solutions for environmental monitoring applications and the meteorological community, but due to their complexity in terms of energy constraints and low performance devices, very few real deployments can be found. In this paper we will explain in detail the design, implementation and performance evaluation of a sparse WSN that has been working maintenance-free for over 12 months. The network has been designed for environmental monitoring purposes, and several motes, attached to lampposts, accurately measure the Temperature and Relative Humidity at various locations in a local street. Our contribution is an enhanced data gathering application for environmental applications. We will present the different arguments and tradeoffs to be taken into account when designing a network of this type, such as the type of sensors and their calibration, choice of mote and operating system, power supply, special housing required for the mote, data sampling frequency, requirements for the data sink, the structure of the database at the central site, and different issues relating to the exploitation of the meteorological results. We show that a reliable, low cost WSN deployment for environmental monitoring that takes into account the main properties of these networks, such multi-hop routing, low maintenance, low cost and long mote lifetimes, is achievable.