Search

In this paper, the automation of the evacuation process of a military ship is studied in real time. For this purpose, a scenario is reconfigured to produce a failure or damage. Then, an optimal network of alternative escape routes is computed. The resulting escape route map can be indicated by lighting the appropriate corridors on the ship. Through these corridors, the members of the embarked population and the entire process is monitored so that the crew can reach their lifeboats in the shortest possible time. To undertake this automated process, the dynamic ship evacuation monitoring system (SIMONE, from its acronym in Spanish: Sistema de Monitorización Dinámica de Evacuación de Buques) has been developed. This system integrates a communication gateway with the integrated platform control system (IPCS) and integrated lighting system that will be installed in new Spanish naval constructions.

Deficient air quality in industrial environments creates a number of problems that affect both the staff and the ecosystems of a particular area. To address this, periodic measurements must be taken to monitor the pollutant substances discharged into the atmosphere. However, the deployed system should also be adapted to the specific requirements of the industry. This paper presents a complete air quality monitoring infrastructure based on the IoT paradigm that is fully integrable into current industrial systems. It includes the development of two highly precise compact devices to facilitate real-time monitoring of particulate matter concentrations and polluting gases in the air. These devices are able to collect other information of interest, such as the temperature and humidity of the environment or the Global Positioning System (GPS) location of the device. Furthermore, machine learning techniques have been applied to the Big Data collected by this system. The results identify that the Gaussian Process Regression is the technique with the highest accuracy among the air quality data sets gathered by the devices. This provides our solution with, for instance, the intelligence to predict when safety levels might be surpassed.

In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature. This work was supported in part by the AEI/FEDER/UE [Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER), and Unión Europea (UE)] under Grant PID2020-116329GB-C22 [ARISE2: Future IoT Networks and Nano-networks (FINe)] and Grant PID2020-112675RB-C41 (ONOFRE-3), in part by the Fundación Séneca, Región de Murcia, under Grant 20889/PI/18 (ATENTO), and in part by the LIFE project (Fondo SUPERA COVID-19 through the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) through the Formación de Personal Investigador (FPI) Predoctoral Scholarship under Grant BES-2017-081061

Flow-guided nanocommunication networks comprise a new paradigm that enables among other things, new medical applications for monitoring, information gathering, and data transmission inside the human body. Although promising, this type of network is conceived to operate in a very hostile environment (e.g. blood circulatory system) where nano-node mobility, along with reduced coverage hinder successful transmissions. In addition to these concerns, it is necessary to take into account the scarce amount of energy available in nano-nodes and the important limitations to harvesting energy. Thus, it is essential to carefully select network parameters to tailor network design and improve performance or satisfy specific application requirements and objectives. To this end, this article presents an optimization framework to maximize throughput by selecting the appropriate frame size as a function of the available energy, nano-router location, and transmission rate. This optimization is conducted for different scenarios where the study can reveal whether a general setup that can work in almost all situations is desirable or, depending on the application, a pre-setting customization of network parameters before deployment is more advantageous in terms of performance. Maximizing throughput ensures information transmission and establishes the maximum amount of data that a nano-router can receive. These two questions are critical for the development of reliable medical applications that can operate within the capabilities that flow-guided nano-networks can offer. Thus, the main technical contributions of this paper are: i) the extension of the flow-guided nano-network analytical model, and ii) the derivation of a throughput optimization framework for this type of networks, which provides a suitable frame size as a function of different network parameters such as flow velocity, nano-router location, and transmission power.

LoRa-based networks exhibit good flexibility in terms of configurable parameters and adjustable modulation properties. Thanks to this, wireless nodes can be tuned to improve their communication behavior. In fact, optimal network-level transmission configurations ( $\mathrm {C_{opt}}$ ) can be derived in such a way that the global network performance is maximized. To derive this $\mathrm {C_{opt}}$ , one must know the radio-propagation behavior of each node beforehand. Traditionally, this has been pursued by using general, low-precision, propagation models due to the infeasibility (in terms of time and energy) of deriving each individual node propagation behavior. In this work we propose a straightforward bounding technique that reduces up to 73% the energy and time required to obtain the radio-propagation behavior of each individual node in the network, enabling the derivation of network-level optimal transmission configurations. Also, we provide mechanisms to keep this knowledge updated, swiftly reacting to changes in the environment and leading to network performance improvements of 15% when compared to traditional alternatives like LoRaWAN ADR. Furthermore, by means of a testbed we demonstrate that this mechanism can also provide resistance to Denial-of-service attacks. Finally, we incorporate the power consumption into the proposed $\mathrm {C_{opt}}$ formulation and provide a generalizable power-consumption determination methodology. This way we can limit the set of eligible transmission configurations to help extending LoRa network lifetimes more than 40%.

This work reports the first amperometric immunosensor for the simultaneous determination of four fertility-related hormones in saliva: progesterone (P4), luteinizing hormone (LH), estradiol (E2), and prolactin (PRL). The immune platform involves direct competitive (P4 and E2), and sandwich (LH and PRL) assays implemented onto functionalized magnetic microbeads (MBs). The amperometric transduction was performed upon placing the MBs-immunoconjugates onto each of the four working electrodes of the SPCE array (SP4CEs) and applying a detection potential of -0.20 V (vs. Ag pseudo-reference electrode) using the H2O2/hydroquinone (HQ) system. The achieved analytical and operational characteristics of the developed multiplexed immunoplatform showed a sensitivity that allows the determination of these hormones in saliva, and an adequate selectivity to analyse complex clinical samples. The bioplatform was employed for the determination of the set of four hormones in human saliva samples collected from individuals with different hormonal profiles. The results obtained using a conventional potentiostat were compared with those provided employing a novel low-cost custom-designed and field-portable quadruple potentiostat. Similar results were found which also agreed with those obtained by applying ELISA methods for the determination of single hormones.

LoRa is an extremely flexible low-power wide-area technology that enables each IoT node to individually adjust its transmission parameters. Consequently, the average per-node throughput of LoRa-based networks has been mathematically formulated and the optimal network-level configuration derived. For end nodes to update their transmission parameters, this centrally computed global configuration must then be disseminated by LoRa gateways. Unfortunately, the regional limitations imposed on the usage of ISM bands—especially those related to the maximum utilization of the band—pose a potential handicap to this parameter dissemination. To solve this problem, a set of tools from the machine learning field have been used. Precisely, the updating process has been formulated as a reinforcement learning (RL) problem whose solution prescribes optimal disseminating policies. The use of these policies together with the optimal network configuration has been extensively analyzed and compared to other well-established alternatives. Results show an increase of up to 147% in the accumulated per-node throughput when our RL-based approach is employed.

By empowering the Internet of Things (IoT) with nanoscale communications, thousands of small devices, called nano-nodes, are enabled to monitor complex environments in a nonintrusive way, giving rise to the Internet of Nano-Things (IoNT) paradigm. It is in one of these very complex and critical environments, the human cardiovascular system, where IoNT might stand out. However, the supervision of human health poses significant challenges: first, to reduce costs and allow in vivo monitoring nano-sensor networks, the number of deployed nano-routers must be limited, which could cause nano-nodes to undergo long out-of-coverage periods. Lastly, nano-nodes rely on harvested energy from the medium, which severely reduces the number of potential transmissions. To overcome these two problems, smart policies that direct devices on how to proceed with sensed events are crucial. In this line, we propose a generic Markov decision process (MDP) model which can be exploited to derive optimal transmission policies that are easily employed by nano-nodes. These policies maximize nano-node throughput and cope with the energy and coverage problems. We have also run a set of simulations to validate our proposal and to compare it to other alternative policies. Results reveal that: 1) our policy systematically outperforms the rest of policies by a large margin and 2) precision in placing nano-routers and average distance between them strongly affect the nano-network performance. The Python code for the MDP model and simulations has been programmed to be easily adaptable to researchers’ needs, making it easier for future IoNT works to add intelligence to the network.

The IoT is the cornerstone of many innovating processes such as those behind Smart Cities and Smart Industries. As more and more wireless IoT devices are deployed, a newer, more congestion-resilient communication infrastructure is required to absorb the traffic from the 50 billion IoT nodes expected by the year 2020. Although 5G is said to be a key technology for the future IoT, it is not a silver bullet. Therefore, providing nodes with different Radio Access Technologies (RAT) would allow them to reap the various benefits offered by each RAT. However, the process of determining which technology should be used at any given time should not be based on uninformed intuition, but on mathematically educated choices. By making use of the mathematical framework of Reinforcement Learning, we have allowed IoT nodes to learn from previous real world data in order to derive optimal RAT-selection policies. These policies, which are implemented as Artificial Neural Networks (ANN), maximize a predefined reward closely related to classic throughput, while maintaining power consumption and operational costs below a certain limit. To allow hardware-constrained IoT nodes to use these ANNs, we have proposed the application of a quantization technique that reduces computation and memory requirements and have validated it by its implementation in a real IoT device. Finally, to evaluate the proposal, we have simulated a network of 1000 devices deployed in the city of Chicago. The obtained results are compared to those achieved with other intuitive policies to further highlight the benefits of our proposal.

Terahertz-based nano-networks are emerging as a groundbreaking technology able to play a decisive role in future medical applications owing to their ability to precisely quantify figures, such as the viral load in a patient or to predict sepsis shock or heart attacks before they occur. Due to the extremely limited size of the devices composing these nano-networks, the use of the Terahertz (THz) band has emerged as the enabling technology for their communication. However, the characteristics of the THz band, which strictly reduce the communication range inside the human body, together with the energy limitations of nano-nodes make the in-body deployment of nano-nodes a challenging task. To overcome these problems, we propose a novel in-body flow-guided nano-network architecture consisting of three different devices: i) nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this type of nano-network has not been previously explored, a theoretical framework capturing all its particularities is derived to properly model its behavior and evaluate its feasibility in real medical applications. Employing this analytical model, a thorough sensitivity study of its key parameters is accomplished. Finally, we analyze the terahertz flow-guided nano-network design to satisfy the requirements of several medical applications of interest.

Vehicular ad-hoc communications rely on periodic broadcast beacons as the basis for most of their safety applications, allowing vehicles to be aware of their surroundings. However, an excessive beaconing load might compromise the proper operation of these crucial applications, especially regarding the exchange of emergency messages. Therefore, congestion control can play an important role. In this article, we propose joint beaconing rate and transmission power control based on policy evaluation. To this end, a Markov Decision Process (MDP) is modeled by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques. This MDP characterization, denoted as MDPRP (indicating Rate and Power), leverages the trade-off between beaconing rate and transmission power allocation. Moreover, MDPRP operates in a non-cooperative and distributed fashion, without requiring additional information from neighbors, which makes it suitable for use in infrastructureless (ad-hoc) networks. The results obtained reveal that MDPRP not only balances the channel load successfully but also provides positive outcomes in terms of packet delivery ratio. Finally, the robustness of the solution is shown since the algorithm works well even in those cases where none of the assumptions made to derive the MDP model apply. This work was supported in part by the AIM Project [Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2016-76465-C2-1-R, in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) for the FPI Grant BES-2017-081061.

Flow-guided electromagnetic nanonetworks will enable innovative medical applications for monitoring, information gathering, and data transmission inside the human body. These nanonetworks will have to operate under extreme computational and powering-related constraints, and in very hostile environments inside human vascular systems. Under these circumstances, successful transmissions between in-body nanonodes and an on-body nanorouter rarely occur, thus requiring new approaches to improve the network throughput in this scenario. Along this view, in classical flow-guided nanonetworks the nanonodes are envisioned to transmit packets if they have enough energy for the transmission, regardless of their vicinity to the nanorouter. In this paper, we propose a nanorouter awareness model that can provide significant throughput gains compared to the baseline based on blind transmissions, facilitating the roll-out of nanocommunication-supported medical applications. This work was supported by project “AriSe2: Future IoT Networks and Nano-networks (FINe)”, ref. PID2020-116329GB-C22 (AEI/FEDER, UE). This work was supported in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE project (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The author Filip Lemic was supported by the EU Marie Curie Actions Individual Fellowship project entitiled “Scalable Localization-enabled In-body Terahertz Nanonetwork” (ScaLeITN), grant nr. 893760.

Detection and treatment of diseases might be one of the most promising fields of application of flow-guided nanocommunication networks. This type of networks can enable new medical developments for health monitoring, vital sign information gathering, and data transmission inside the human body. As these nano-networks might require the introduction of nano-devices in the human circulatory system, it is crucial to investigate feasible network setups that meet the requirements of custom medical applications. Reducing body invasiveness by minimizing the number of nano-devices needed is also an important factor. This paper contributes by formulating and solving a nano-node minimization problem for some medical applications, thus demonstrating the feasibility of this technology.

In this paper, we propose a flow-guided nano-network deployed inside of a human vascular system. The network consists of bio-sensors reporting medical events, small nano-nodes working as information carriers, and nano-routers communicating with external systems. In order to evaluate the performance and feasibility, a preliminary analytical model for this type of flow-guided nano-network is derived and validated through simulations. Results reveal that the network can meet medical requirements in terms of reported events per minute with a feasible number of nano-nodes.

Ascorbic Acid (AA) is a natural and powerful water-soluble antioxidant associated with long-lasting food products. As time passes, the AA content in products sharply decreases, and they become increasingly degraded. There are several techniques to precisely quantify AA concentrations. However, most of them employ costly laboratory instruments, such as High-Performance Liquid Chromatography (HPLC) or complex electrochemical methods, which make unfeasible recurrent AA measurements along the entire supply chain. To address this issue, we contribute with an in-field and real-time voltammetric method, carried out with a low-cost, easy-to-use, and portable device. An unmodified Screen-Printed Electrode (SPE) is used together with the device to achieve short reading times. Our method has been extensively tested in two multifruit juices using three different SPEs. Calibration curves and Limit of Detection were derived for each SPE. Furthermore, periodic experiments were conducted to study the shelf life of juices under consideration. During the analysis, a set of assays for each SPE were implemented to determine the remaining AA amount per juice and compare it with that obtained using HPLC under the same conditions. Results revealed that our cost-effective device is fully comparable to the HPLC equipment, as long as the juice does not include certain interferents, a scenario also contemplated in this article.

The European Telecommunications Standards Institute (ETSI) establishes a Decentralized Congestion Control (DCC) which triggers the so-called Cooperative Awareness Message (CAM) depending on vehicle kinematics. However, this algorithm hence called CAM-DCC represents a challenge in the triggering rules of these messages. In particular, it lacks (i) awareness of the neighboring vehicles and (ii) efficient use of the channel bandwidth. Consequently, information gaps related to the road environment might give rise to non-compliances in the application layer requirements of the vehicles, which could potentially threaten the drivers’ safety, most particularly in hazardous roads. To overcome these flaws, we first study the CAM generation trigger focused on the vehicle heading in risky curves or winding roads. Then, we evaluate both scenarios tuning different triggering thresholds and including additional mechanisms such as the comparison of the current speed respect to the estimated advisory speed over time. Different computer simulations have been conducted in two real road sections to validate our proposal. Results reveal significant better performance in terms of awareness and channel usage. This work has been supported by the by the projects AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE), and ATENTO, ref. 20889/PI/18 (Fundación Séneca, Región de Murcia). In particular, Juan Aznar-Poveda thanks the Spanish MECD for an FPI predoctoral fellowship (ref. BES-2017-081061).

This paper evaluates the performance of the Cooperative Awareness Message (CAM) dissemination stated in the European Standard EN 302 637-2 in risky sharp bends and winding roads. We propose a novel triggering condition based on the dynamic estimation of the road radius, used as a risk metric. So as lower the radius, the higher the beaconing rate. As a case study, two real road sections, with different lengths and angles, have been simulated to prove that both a better awareness and responsiveness are achieved in the vehicles, to later ensure a proper application layer functioning. Finally, congestion constraint is also tested to check that no significant interferences are found in the described behavior. This work has been supported by the project AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE). In particular, Juan Aznar-Poveda thanks the Spanish MECD for an FPI predoctoral fellowship (ref. BES-2017-081061).

Nanoscale communications is a new paradigm encompassing all concerns related to the exchange of information among devices at the nanometer scale. A network infrastructure consisting of a huge quantity of nano-devices is envisaged to ensure robust, reliable, and coordinated data transmission. This will enable a plethora of forthcoming applications and services in many different research fields, such as personalized medicine, synthetic biology, environmental science, and industry, which will lead to outstanding and unprecedented advances. The IEEE 1906.1 standard provides a conceptual and general framework to set the starting point for future developments in nanoscale communication networks. This article reviews the latest IEEE 1906.1 recommendations, observing their main features when applied to the electromagnetic (EM) nanocommunication area. We contribute by identifying the standard achievements and indicating future guidelines for the development of the upcoming standardization of EM nanoscale communications.

IoT is rapidly expanding to all productive sectors; however, as thousands of devices are envisioned to be deployed, a careful study of the radio spectrum (and its interferences) must be considered to predict and improve the network performance. In this sense, radio-channel characterization is essential; in particular, path-loss characterization allows to mathematically predict the propagation behavior and interferences of IoT devices with precision. In this paper, we show that by means of a set of mathematical corrections and measuring procedures, we can soundly employ the received signal strength indicator (RSSI), available in most IoT motes, to derive accurate path-loss models of industrial infrastructures. Experimental results reveal that the proposed scheme substantially improves the characterization accuracy of a real smart grid environment. This, in turn, is proven to have a measurable positive impact on the planning stage of an IoT network, whose life-of-time and cost analysis can be better anticipated.

Wireless Sensor Networks (WSN) pose several problems in terms of scalability and energy consumption. Many of them have been treated in the literature by means of optimization techniques. Depending on the optimization goal and constraints, a great number of these problems are solved using Linear Programming (LP) techniques whenever possible or the network model is further simplified to fit into this category, albeit accuracy may be degraded. In many other cases, simplifying the network model is not feasible and optimization requires more complex techniques. For these cases, non-linear optimization becomes an interesting alternative. Non-linear optimization is beneficial provided that more accurate results are needed or linear optimization is not achievable or yields unreliable results. This paper surveys recent and significant contributions regarding non-linear optimization problems in WSN, selecting the most relevant objectives to optimize and classify the problems under consideration into several categories. Furthermore, this work provides insights in many important problems requiring non-linear optimization and, finally, discusses current open questions in this area.

The smart grid (SG) represents the present and the future of electric power grids. Wireless networks (WNs) are a key technology for this updating process, empowering electric networks to be easily managed in a flexible and cost-effective way. However, as with any wireless technology and especially in a harsh multipath environment such as the SG, reckoning with an extensive channel characterization is crucial. This paper presents an exhaustive characterization of two representative scenarios, both in the transport and distribution segment of an SG: a 400-kV outdoor substation and a main power room. The 2.4-GHz ISM band—in which most wireless communication technologies applied to SG operate—is examined in depth to characterize the radio-propagation phenomena. Large and small-scale fading, RMS delay, coherence bandwidth, and electromagnetic interferences are studied under different line-of-sight, polarizations, and frequency conditions to derive empirical models allowing to estimate the aforementioned parameters in other SGs of a similar nature. Finally, to further evaluate and compare our channel characterization, a WN was simulated in a substation to examine the channel modeling impact on its suitable design and operation.

Implicit Error Correction (IEC) is a near Video-on-Demand (nVoD) scheme that trades bandwidth utilization for initial playback delay to potentially support an infinite number of users. Additionally, it provides error protection without any further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. However, IEC packet loss protection is weaker at the beginning of the playback due to the lack of implicit redundancy and lower decoding efficiency, resulting in worse subjective playback quality. In tackling this issue, this paper contributes with an extension of the original nVoD architecture, enhancing its performance by adding a new element namely, subchannels. These subdivisions of the original channels do not provide further packet loss protection but significantly improve the decoding efficiency, which in turn increases playback quality, especially at the beginning. Even for very high packet loss probabilities, subchannels are designed to obtain higher decoding efficiency which results in greater packet loss protection than that provided by IEC. The proposed scheme is especially useful in wireless cooperative networks using techniques such as network coding, as content transmissions can be split into different subchannels in order to maximize network efficiency. This work was supported by the AEI/FEDER, UE Project AIM under Grant TEC2016-76465-C2-1-R.

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection. This research was funded by projects AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE), e-DIVITA, ref.20509/PDC/18 (Proof of Concept, 2018) and it is the result of the activity performed under the program Groups of Excellence of the Region of Murcia (Spain), the Fundación Séneca, Science and Technology Agency of the region of Murcia project under grant 19884/GERM/15 and ATENTO, ref. 20889/PI/18. All remaining errors are our responsibility.

Flow-guided nanocommunication networks have recently emerged as a promising solution to monitor, gather information, and data communication inside the human body. This paper presents an analytical study on the behavior of these networks with the objective of evaluating the maximum channel utilization and determining an optimal frame length.

The application of long-range communication technologies such as LoRa to certain environments like smart cities/industries is a natural step forward towards the future IoT. However, the strict limitations imposed in some countries on the duty cycle of ISM bands can jeopardize this technological progress. To overcome this and, at the same time, improve IoT nodes’ transmission and power efficiency, a novel mathematical model has been derived. Its numerical analysis leads to a closed-form formula that characterizes node performance in terms of its transmission policy. The maximization of this formula—required to increase node performance—is then posed as an optimization problem whose solution yields smart transmission policies that maximize throughput while limiting energy consumption. The results achieved under this approach are compared to those obtained using the ADR algorithm (the most extended mechanism for choosing transmission parameters in LoRaWAN networks) and to random/conservative policies to further highlight the benefits of the proposal. Results reveal that the proposed method outperforms ADR by 33.20%, the conservative policy by 91.81%, and the random policy by 238.8%.

Private transportation by bicycle or other non-motorized means is increasingly common in urban centers. Not only these modes of transport have clear economic and environmental benefits, but they are also often faster than motorized vehicles due to frequent traffic jams in congested cities. Non-motorized transportation should be as comfortable as possible to promote it further. This article investigates how smartphones carried by drivers of non-motorized vehicles can collect data about the environment and exploit these data to improve drivers' comfort. A comparative survey on different proposals that take a Vehicle-to-Infrastructure (V2I) communication approach is presented.

Vehicular wireless communications require both congestion control to guarantee the availability of a fraction of the bandwidth for safety-related event-driven messages in emergency cases, and awareness control to adapt the beaconing activity to the application needs and surrounding traf_c situation. Most current approaches either ignore the traf_c situation and only adapt the beaconing rate to the channel congestion state or override the congestion control limits, leading to questionable results in both cases. In this paper, we conceive and validate a novel approach, combining both aspects. Based on distributed Network Utility Maximization (NUM), our algorithm satis_es the constraints on channel availability, whereas the safety of the surrounding traf_c situation is captured with a time-to-collision metric, used to assign priorities in the optimal allocation problem. The performance of the proposed approach is validated and compared to other popular algorithms. Results show that our proposal automatically anticipates a potential increase in rate due to a critical safety situation, but does not interfere with the reserved bandwidth for safety applications. This work was supported in part by Spanish MECD/AEI/FEDER under Grant TEC2016-76465-C2-1-R, in part by Spanish MINECO/AEI/FEDER under Grant TEC2017-84423-C3-2-P (ONOFRE-2), in part by e-DIVITA under Grant 20509/PDC/18 (Proof of Concept, 2018), and in part by ATENTO Projects under Grant 20889/PI/18 (Fundación Seneca, Región de Murcia). The work of J. Aznar-Poveda was supported by the MECD under Grant BES-2017-081061.

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol, a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.

The deployment of wireless sensor networks in smart cities for environmental monitoring is a complex issue. One of the main problems is to determine the most appropriate places for these tasks. This paper proposes the use of information from crowdsourcing to identify places of interest from the environmental point of view to deploy the sensor network.

A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

Continuous progress of nanocommunications and nano-networking is opening the door to the development of innovative yet unimaginable services, with a special focus on medical applications. Among several nano-network topologies, flow-guided nanocommunication networks have recently emerged as a promising solution to monitoring, gathering information, and data communication inside the human body. In particular, flow-guided nano-networks display a number of specific characteristics, such as the type of nodes comprising the network or the ability of a nano-node to transmit successfully, which significantly differentiates them from other types of networks, both at the nano and larger scales. This paper presents the first analytical study on the behavior of these networks, with the objective of evaluating their metrics mathematically. To this end, a theoretical framework of the flow-guided nano-networks is developed and an analytical model derived. The main results reveal that, due to frame collisions, there is an optimal number of nano-nodes for any flow-guided network, which, as a consequence, limits the maximum achievable throughput. Finally, the analytical results obtained are validated through simulations and are further discussed.

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules, namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring

Smart Cities and Smart Industries are the flagships of the future IoT due to their potential to revolutionize the way in which people live and produce in advanced societies. In these two scenarios, a robust and ubiquitous communication infrastructure is needed to accommodate the traffic generated by the 10 billion devices that are expected by the year 2020. Due to its future world-wide presence, 5G is called to be this enabling technology. However, 5G is not a perfect solution, thus providing IoT nodes with different Radio Access Technologies (RATs) would allow them to exploit the various benefits offered by each RAT (such as lower power consumption or reduced operational costs). By making use of the mathematical framework of Reinforcement Learning, we have formulated the problem of deciding which RAT should an IoT node employ when reporting events. These so-called transmission policies maximize a predefined reward closely related to classical throughput while keeping power consumption and operational costs below a certain limit. A set of simulations are performed for IoT nodes provided with two RATs: LoRa and 5G. The results obtained are compared to those achieved under other intuitive policies to further highlight the benefits of our proposal.

This paper describes the reliable determination of progesterone (P4) in undiluted saliva making use of a disposable amperometric immunosensors implemented on low-cost and portable device/potentiostat constructed with commercial-off-the-shelf (COTS) components. The immunosensor allows the fast (45 min), selective and sensitive determination (5 pg mL−1 LOD) of P4 using amperometry in stirred solutions. The immunosensor was coupled to the COTS-based potentiostat and amperometry was made into drops of quiescent solutions. No significant differences were apparent between the analytical performance achieved with the immunosensor for P4 using both a conventional and the COST-based potentiostats. The practical applicability of the immunosensor coupled with the COTS-based potentiostat was demonstrated by determining the endogenous P4 content in different undiluted saliva samples with highly variable endogenous contents of the target hormone. The obtained results were in good agreement with those provided by the conventional ELISA methodology and with the contents reported in the literature for samples with similar characteristics. This validated the combined device for the reliable and minimally invasive determination of the target hormone involving a very simple protocol and taking only 45 min.

Low-power wide-area network (LPWAN) technologies enable Internet of Things (IoT) devices to efficiently and robustly communicate over long distances, thus making them especially suited for industrial environments. However, the stringent regulations on the usage of certain industrial, scientific, and medical bands in many countries in which LPWAN operate limit the amount of time IoT motes can occupy the shared bands. This is particularly challenging in industrial scenarios, where not being able to report some detected events might result in the failure of critical assets. To alleviate this, and by mathematically modeling LPWAN-based IoT motes, we have derived optimal transmission policies that maximize the number of reported events (prioritized by their importance) while still complying with current regulations. The proposed solution has been customized for two widely known LPWAN technologies: 1) LoRa and 2) Sigfox. Analytical results reveal that our solution is feasible and performs remarkably close to the theoretical limit for a wide range of network activity patterns.

The aim of this study was to implement an outlier marking and analysis methodology to optimize CT examination protocols. CT Head examination data, including dose metrics along with technical parameters, were stored in an automatic dose registry system. Reference dose metrics distribution was obtained throughout a 1-year period. Outlier thresholds were calculated taking into account the specific shape of the distribution, by using a robust measure of the skewness; the medcouple parameter. Subsequently, outliers from a 4-month period were marked and Cause-and-Effect analysis was carried out by a multidisciplinary dose committee. Reference Dose metrics distributions were obtained from 3690 CT Head examinations. Both CTDIvol and DLP showed a certain degree of skewness, with a medcouple value of 0.05 and 0.11, respectively. All of the upper-outliers fell within 3 identifiable groups of causes, ordered by relative importance: i) inadequate protocol selection, ii) arms or objects in the field-of-view, and iii) abnormal scanning region diameter. Regarding the lower-outliers, 90% were attributable to the inclusion of additional series in the original head protocol and the remaining 10% to unknown causes. Also, a general Cause-and-Effect diagram for outliers was elaborated. While the Dose Reference Level method applies to the general performance of a CT protocol and allows comparison with other centers, the outlier method represents a step further in the optimization process. The proposed method focuses on detecting incorrect utilization of the CT, which mainly arises from inadequate knowledge of CT technology.

Poor air quality in urban environments is one of the major problems of modern cities. This paper presents a crowd-sensing approach that leverages the mobility of public transportation to monitor air quality in different inner-city areas over the course of a day. To meet this objective, we present a system architecture for the compilation of environmental data using low-cost mobile sensor devices equipped in public transport vehicles. Thus, the network topology has been designed to be robust and reliable, tackling connectivity problems through the use of delay-tolerant networks and Bluetooth ad-hoc gateways, and to be flexible, dealing with changing topologies through the use of mobile ad-hoc networks. The tests carried out in the public transportation of Cartagena (Spain) show the feasibility of the proposed architecture for this vehicle to infrastructure service and the effectiveness of deploying a mobile measurement system instead of fixed stations, which is the usual solution for many smart cities.

Electromagnetic nanocommunications, understood as the communication between electronic nanoscale devices through electromagnetic waves in the terahertz band, has attracted increasing attention in recent years. In this regard, several solutions have already been proposed. However, many of them do not sufficiently capture the significance of the limitations in nanodevice energy-gathering and storing capacity. In this paper, we address key factors affecting the energy consumption of nanodevices, highlighting the effect of the communication scheme employed. Then, we also examine how nanodevices are powered, focusing on the main parameters governing the powering nanosystem. Different mathematical expressions are derived to analyze the impact of these parameters on its performance. Based on these expressions, the functionality of a nanogenerator is evaluated to gain insight into the conditions under which a wireless nanosensor network (WNSN) is viable from the energetic point of view. The results reveal that a micrometer-sized piezoelectric system in high-lossy environments (exceeding 100 dB/mm) becomes inoperative for transmission distances over 1.5 mm by its inability to harvest and store the amount of energy required to overcome the path loss.

Ionizing radiation is one of the main risks affecting healthcare workers and patients worldwide. Special attention has to be paid to medical staff in the vicinity of radiological equipment or patients undergoing radioisotope procedures. To measure radiation values, traditional area meters are strategically placed in hospitals and personal dosimeters are worn by workers. However, important drawbacks inherent to these systems in terms of cost, detection precision, real time data processing, flexibility, and so on, have been detected and carefully detailed. To overcome these inconveniences, a low cost, open-source, portable radiation measurement system is proposed. The goal is to deploy devices integrating a commercial Geiger-Muller (GM) detector to capture radiation doses in real time and to wirelessly dispatch them to a remote database where the radiation values are stored. Medical staff will be able to check the accumulated doses first hand, as well as other statistics related to radiation by means of a smartphone application. Finally, the device is certified by an accredited calibration center, to later validate the entire system in a hospital environment This research has been supported by the project AIM, ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE). This work is dedicated to the memory of Felipe Garcia-Sanchez.

Traditionally, electrochemical procedures aimed at determining substance concentrations have required a costly and cumbersome laboratory environment. Specialized equipment and personnel obtain precise results under complex and time-consuming settings. Innovative electrochemical-based sensors are emerging to alleviate this difficulty. However, they are generally scarce, proprietary hardware and/or software, and focused only on measuring a restricted range of substances. In this paper, we propose a portable, flexible, low-cost system, built from commercial off-the-shelf components and easily controlled, using open-source software. The system is completed with a wireless module, which enables the transmission of measurements to a remote database for their later processing. A well-known PGSTAT100 Autolab device is employed to validate the effectiveness of our proposal. To this end, we select ascorbic acid as the substance under consideration, evaluating the reliability figure and obtaining the calibration curves for both platforms. The final outcomes are shown to be feasible, accurate, and repeatable.