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.

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.

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.

Wireless communications for vehicular applications in fifth generation cellular systems (5G) are required to be of low latency and high reliability. Among other factors, the amount of control information to be exchanged between each vehicle and the base station can penalize the communication latency. Several 5G vehicular use cases involve communications within groups of vehicles, for instance, vehicle platooning. This work is focused on exploiting the structure and characteristics of this particular group-based vehicular service to decrease the control information exchange related to the channel estimation stage necessary for vehicle to infrastructure cellular communications. A scheme based on channel spatial interpolation is proposed, where the real channel is only available at a subset of vehicles, and subsequent spatial interpolation of the channel provides estimates of the large-scale channel parameters for the rest of vehicles in the group. In the paper, communication cost expressions are derived for centralized and distributed topologies, considering a scenario with a macro base station serving a vehicle platoon. The evaluation results show that the distributed topology is more efficient in terms of communication cost, while the centralized architecture is more robust against inter-vehicle distance variations.

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.

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.

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.

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.

Currently wireless sensor networks (WSN) have become one of the most innovative and promising technologies. This kind of networks has been successfully applied in a large number of applications of great importance for the society, such as: environmental monitoring, military applications, traffic control, patient monitors and healthcare devices, etc. One of the most important matters in the development of a WSN is the embedded software that runs in the network nodes. This software implements the protocols and services and carries out the data gathering process. In this paper we propose a new methodology for the development of this embedded software based on a graphical interface that facilitates a rapid and easy prototyping of different applications. Using this programming environment it is not necessary to have a previous knowledge of any programming language, since the final code is automatically generated using an integrated development environment based on LabVIEW.