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.

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 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.

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.

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.

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.

This paper introduces an extension of the reverse priority framework for the kinematic control of redundant robots. It integrates, in a unified framework, the treatment of multiple tasks, multiple kinematic chains, different joint priorities and hard constraints. The management of multiple tasks is based on the reverse priority method, that has been modified so that it makes possible the assignment of different priorities to each joint in order to accomplish the tasks. This framework is also suitable for robotic systems with multiple kinematic chains, which could share several joints. Moreover, it can deal with bilateral and unilateral constraints, that can be defined either at joint or cartesian space. Hard constraints are considered at each priority level, instead of treating them separately at the highest priority level. The proposed framework has been evaluated in simulation and in real experiments with a redundant underwater vehicle-manipulator system at sea.

We present a new method for fusing scores corresponding to different detectors (two-hypotheses case). It is based on alpha integration, which we have adapted to the detection context. Three optimization methods are presented: least mean square error, maximization of the area under the ROC curve, and minimization of the probability of error. Gradient algorithms are proposed for the three methods. Different experiments with simulated and real data are included. Simulated data consider the two-detector case to illustrate the factors influencing alpha integration and demonstrate the improvements obtained by score fusion with respect to individual detector performance. Two real data cases have been considered. In the first, multimodal biometric data have been processed. This case is representative of scenarios in which the probability of detection is to be maximized for a given probability of false alarm. The second case is the automatic analysis of electroencephalogram and electrocardiogram records with the aim of reproducing the medical expert detections of arousal during sleeping. This case is representative of scenarios in which probability of error is to be minimized. The general superior performance of alpha integration verifies the interest of optimizing the fusing parameters.