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This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user’s point of view.

This letter presents an analysis along with a design method and pulsewidth modulation operation for the series-capacitor boost converter, which reduces the input current ripple without increasing the stored energy in inductors, that is, without increasing their physical size. The method can also be applied to the buck version of the converter. Experimental results are provided to evaluate the performance of the proposed approach.

The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.

Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can help improve the quality of people’s sleep at a low cost. We reviewed the sleep quality of a group of people by analyzing their good and bad sleeping habits. We take that information to feed a proposed algorithm for a non-invasive sensor network in the person’s room for monitoring factors that help them fall asleep. We analyze vital signs and health conditions in order to be able to relate these parameters to the person’s way of sleeping. We help people get valuable information about their sleep with technology to live a healthy life, and we get about a 15% improvement in sleep quality. Finally, we compare the implementations given by the network with wearables to show the improvement in the behavior of the person’s sleep.

Sensor networks are deployed in people’s homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users’ comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person’s development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users’ perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.

Over seven million people suffer from an impairment in Mexico; 64.1% are gait-related, and 36.2% are children aged 0 to 14 years. Furthermore, many suffer from neurological disorders, which limits their verbal skills to provide accurate feedback. Robot-assisted gait therapy has shown significant benefits, but the users must make an active effort to accomplish muscular memory, which usually is only around 30% of the time. Moreover, during therapy, the patients’ affective state is mostly unsatisfied, wide-awake, and powerless. This paper proposes a method for increasing the efficiency by combining affective data from an Emotiv Insight, an Oculus Go headset displaying an immersive interaction, and a feedback system. Our preliminary study had eight patients during therapy and eight students analyzing the footage using the self-assessment Manikin. It showed that it is possible to use an EEG headset and identify the affective state with a weighted average precision of 97.5%, recall of 87.9%, and F1-score of 92.3% in general. Furthermore, using a VR device could boost efficiency by 16% more. In conclusion, this method allows providing feedback to the therapist in real-time even if the patient is non-verbal and has a limited amount of facial and body expressions.

Este articulo presenta un novedoso sistema vestible destinado a asistir la movilidad de personas invidentes y debiles visuales en entornos urbanos mediante el simple uso de un telefono inteligente y retroalimentacion tactil. El sistema utiliza los datos de posicionamiento provistos por el sensor GPS del telefono inteligente para localizar en tiempo real al usuario en el espacio y determinar las direcciones hacia un destino. Las instrucciones de navegacion resultantes se codifican como patrones de vibracion y se despliegan al usuario a traves de una interfaz tactil integrada al zapato. Para validar la pertinencia del sistema propuesto, se realizaron dos experimentos con usuarios. El primero involucro la participacion de un grupo de 20 sujetos voluntarios visualmente sanos a los que se les pidio reconocer las instrucciones de navegacion desplegadas mediante el dispositivo de estimulacion podotactil. Los resultados muestran altas tasas de reconocimiento. El segundo experimento consistio en conducir a dos sujetos voluntarios invidentes a lo largo de espacios publicos urbanos hacia destinos previamente establecidos. Los resultados fueron exitosos y sugieren que el sistema incrementa la navegacion independiente y de forma segura de personas con discapacidad visual. A su vez, se muestra el potencial de los telefonos inteligentes y los dispositivos podotactiles en las tecnologias de asistencia. Palabras clave: tecnologias de asistencia, localizacion GPS, movilidad de personas invidentes, estimulacion podotactil, interfaz vibrotactil, sistema vestible.

Inter-carrier interference (ICI) in vehicle to vehicle (V2V) orthogonal frequency division multiplexing (OFDM) systems is a common problem that makes the process of detecting data a demanding task. Mitigation of the ICI in V2V systems has been addressed with linear and non-linear iterative receivers in the past, however, the former requires a high number of iterations to achieve good performance, while the latter does not exploit the channel’s frequency diversity. In this paper, a transmission and reception scheme for low complexity data detection in doubly selective highly time varying channels is proposed. The technique couples the discrete Fourier transform spreading with non-linear detection in order to collect the available channel frequency diversity and successfully achieving performance close to the optimal maximum likelihood (ML) detector. When compared with the iterative LMMSE detection, the proposed system achieves a higher performance in terms of bit error rate (BER), reducing the computational cost by a third-part when using 48 subcarriers, while in an OFDM system with 512 subcarriers, the computational cost is reduced by two orders of magnitude.

This paper reports on the progress of a wearable assistive technology (AT) device designed to enhance the independent, safe, and efficient mobility of blind and visually impaired pedestrians in outdoor environments. Such device exploits the smartphone’s positioning and computing capabilities to locate and guide users along urban settings. The necessary navigation instructions to reach a destination are encoded as vibrating patterns which are conveyed to the user via a foot-placed tactile interface. To determine the performance of the proposed AT device, two user experiments were conducted. The first one requested a group of 20 voluntary normally sighted subjects to recognize the feedback provided by the tactile-foot interface. The results showed recognition rates over 93%. The second experiment involved two blind voluntary subjects which were assisted to find target destinations along public urban pathways. Results show that the subjects successfully accomplished the task and suggest that blind and visually impaired pedestrians might find the AT device and its concept approach useful, friendly, fast to master, and easy to use.

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the wellknown AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management.

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

This chapter presents the main concepts of metaheuristic schemes for Wireless Sensor Networks (WSNs). WSNs are multi-functional, low-cost, and low-power networks and rely on communications among devices, from sensor nodes to one or more sink nodes. Sink nodes, sometimes called coordinator nodes or root nodes, may be more robust and have larger processing capacity than the other nodes.

Hybrid metaheuristic methods combine approaches extracted from different techniques to build a single optimization method. The design of such systems represents a current trend in the metaheuristic optimization literature. In hybrid algorithms, the objective is to extend the potential advantages of the integrated approaches and eliminates their main drawbacks. In this chapter, a hybrid method for solving optimization problems is presented. The presented approach combines (1) the explorative characteristics of the invasive weed optimization (IWO) method, (2) the probabilistic models of the estimation distribution algorithms (EDA), and (3) the dispersion capacities of a mixed Gaussian-Cauchy distribution to produce its own search strategy. With these mechanisms, the method conducts an optimization strategy over search areas that deserve a special interest according to a probabilistic model and the fitness value of the existent solutions. In the presented method, each individual of the population generates new elements around its own location, dispersed according to the mixed distribution. The number of new elements depends on the relative fitness value of the individual regarding the complete population. After this process, a group of promising solutions is selected from the set compound by the (1) new elements and the (2) original individuals. Based on the selected solutions, a probabilistic model is built from which a certain number of members (3) is sampled. Then, all the individuals of the sets (1), (2), and (3) are joined in a single group and ranked in terms of their fitness values. Finally, the best elements of the group are selected to replace the original population. This process is repeated until a termination criterion has been reached. To test the performance of the method, several comparisons to other well-known metaheuristic methods has been conducted. The comparison consists of analyzing the optimization results over different standard benchmark functions within a statistical framework. Conclusions based on the comparisons exhibit the accuracy, efficiency, and robustness of the presented approach.

Clustering involves the process of dividing a collection of abstract objects into a number of groups, which are integrated with similar elements. Several clustering methods have been introduced and developed in the literature with different performance levels. Among such approaches, density algorithms present the best advantages, since they are able to find clusters from a dataset under different scales, shapes, and densities without requiring the number of groups as input. On the other hand, there are processes that humans perform much better than deterministic approaches or computers. In fact, humans can visually cluster data exceptionally well without the necessity of any training. Under this unique capability, clustering, instantaneous, and effortless for humans, represents a fundamental challenge for artificial intelligence. In this chapter, a simple clustering model inspired by the way in which the human visual system associates patterns spatially is presented. The model, at some abstraction level, can be characterized as a density grouping strategy. The approach is based on Cellular Neural Networks (CNNs), which have demonstrated to be the best models for emulating the human visual system. In the method, similar to the biological model, CNN is used to build especially groups while an automatic mechanism tries different resolution scales to find the best possible data categorization. Different datasets have been adopted to evaluate the performance of the algorithm. Their results are also compared with popular density clustering techniques from the literature. Computational results demonstrate that the CNN approach presents competitive results in comparison with other algorithms regarding accuracy and robustness.

In the last years, medical image processing has become an important tool for health care. In particular, the analysis of retinal vessel images has become crucial for achieving a more accurate diagnosis and treatment for several cardiovascular and ophthalmological deceases. However, this task is extremely hard and time-consuming, often requiring human supervised segmentation of fundus images as well as some degree of professional skills. An automatic yet accurate procedure for retinal vessel segmentation is essential to assist ophthalmologists with illness detection. Several retinal vessel segmentation methods have been developed with satisfactory results. Nevertheless, the image preprocessing techniques implemented in this kind of procedure is known to have poor performance, mainly due to the complex nature of retinal vessel imaging. To improve the image preprocessing stage, a fast and accurate approach for retinal vessel segmentation is presented. This approach aims to enhance the contrast between retinal vessels and the image’s background by implementing a natural inspired technique called Lateral Inhibition (LI). Additionally, a cross-entropy minimization procedure based on Differential Evolution (DE) is applied to find the appropriate threshold and then used to define whether an image pixel is a vessel or non-vessel. The described method has been tested by considering two well-known image databases: DRIVE and STARE. The obtained results are compared against those of other related methods in order to prove the accuracy, effectiveness, and robustness of the presented approach.

Corner detection represents one of the most important steps to identify features in images. Due to their powerful local processing capabilities, Cellular Nonlinear/Neural Networks (CNN) are commonly utilized in image processing applications such as image edge detection, image encoding and image hole filling. CNN perform well for locating corner features in binary images. However, their use in grayscale images has not been considered due to their design difficulties. In this chapter, a corner detector based on CNN for grayscale images is presented. In the approach, the original processing scheme of the CNN is modified to include a nonlinear operation for increasing the contrast of the local information in the image. With this adaptation, the final CNN parameters that allow the appropriate detection of corner points are estimated through the Differential evolution algorithm by using standard training images. Different test images have been used to evaluate the performance of the presented corner detector. Its results are also compared with popular corner methods from the literature. Computational simulations demonstrate that the presented CNN approach presents competitive results in comparison with other algorithms in terms of accuracy and robustness.

Almost all industries and businesses make purchases; they usually have more than one possible supplier; in most cases, they have many possible suppliers, since suppliers have different prices, policies, different levels of quality. Making wise decisions may save money, which has an impact on the final price of products, and therefore, on the company’s competitiveness. The problem can be solved using mathematical models to represent the cost of the inventory management, and finally, applying techniques to minimize the cost. Mathematical models of the inventory management problem may be complex and NP-hard, and as a result, evaluating all possible solutions to find the cheapest one may be unfeasible, even with a computer. When that happens, metaheuristic algorithms may be used to find a reasonable solution in a reasonable amount of time. This chapter deals with this topic. This chapter presents an example problem, in order to illustrate the complexity of the inventory management and how a large number of possible solutions may arise, then it solves the problem with traditional and metaheuristic algorithms to demonstrate the advantages of metaheuristic algorithms. Finally, other problems are discussed, and further information about algorithms is provided.

Animals have demonstrated clever hunting strategies when they collaborate with each other. These techniques involve swarm intelligence and have been applied to develop bio-inspired algorithms, a research field for solving optimization problems. In the last years, several methods arise from inspiration in nature to model collective animal behavior.

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

One of the most significant benefits of the Internet of Things (IoT) is its efficiency. Many companies use this system to automate business and manufacturing processes, remotely monitor and control operations, optimize supply chains, and conserve resources. Smart devices agglomerate a huge amount of IoT data that needs to be analyzed in real-time. Most of the sensors are in remote areas or difficult for people to access, for this reason, it is necessary to analyze in detail the type of technology used as well as the installation site to optimize the use of energy. This work presents a real application of IoT in the monitoring of a building, in addition, an algorithm is used that optimizes the energy used, increasing the battery lifetime. In this way, it is possible to have an intelligent and energy-saving system. This work analyzes the main wireless technologies such as Zigbee, WiFi and LoRa in order to propose the ideal routing protocol depending on performance metrics in a building on a university campus.

This paper presents a contactless environment mapping system for autonomous navigation of mobile robots. Based on ultrasonic sensing, the proposed system obtains reliable and accurate maps of unknown environments without the need of physical contact and regardless of the scene’s illumination. These maps include the location of obstacles and objects in the environment; therefore, they can be used for autonomous navigation and trajectory planning of mobile robots. This paper introduces the design, implementation, and preliminary results showing feasibility of the proposed system.

Electric vehicles EV are attracting attention due to their environmentally friendly operation; they market penetration may require the installation of a large number of charging stages, which is expensive and challenging from the logistic point of view. Hybrid electric vehicles HEV, may get charged from different sources which makes a flexible operation, the combination of a fuel cell, a battery, and an ultra-capacitor represents a plausible solution, ultracapacitors provide the power density required for the acceleration, and the fuel cell provides the energy density required to have a good autonomy. This manuscript proposes a fussy-logic-based energy administration strategy for an HEV feed by a set of a fuel cell, a battery, and an ultra-capacitor. The proposed strategy controls the state of charge of the ultracapacitor, considering their superior energy storage capability. Experimental results demonstrate that the proposed strategy reduces the waste of energy by 14%. This leads to energy savings, the vehicle consumed 218 Wh per km without the proposed strategy against 192 Wh per km without it, for the same example of the driving path; the estimated fuel efficiency increased 96 to 109 miles per gallons.

This chapter presents the main concepts of metaheuristic schemes. The objective of this chapter is to introduce the characteristics and properties of these approaches. An important propose of this chapter is also to recognize the importance of metaheuristic methods to solve optimization problems in the cases in which traditional techniques are not suitable.

The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers&rsquo, usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information, a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products&rsquo, traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system.

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

One of the microorganisms used in the industry to generate high value products is the microalga. Due to its nonlinear dynamics and variation in time, bioprocesses that involve the use of microalgae pose challenges in modeling, controlling, and increasing their production. Normally, at the laboratory level, closed cultures of microalgae are carried out in photobioreactors. To produce biomass, the culture conditions can be controlled using a photobioreactor. This paper describes the design of an intelligent Proportional-Integral controller applied in a continuous culture of Chlorella vulgaris microalga, where the controlled variable is the dilution rate D(t). This controller is characterized by formulating the dynamics of the system with an ultra-local model. In this ultra-local model, both the uncertainties of the system dynamics and the disturbances are handled in one unified way. The performance of the intelligent Proportional-Integral controller was tested at simulation level using Matlab EnvironmentTM. In this research, the accomplishment of the controller was contemplated against disturbances, under nominal conditions and front variation of parameters. The simulations showed that the intelligent Proportional-Integral controller ensures excellent reference tracking capability. Finally, it is important to highlight the robustness of the strategy.

This work compares two perspectives related to the production and imaging of a videogame called Shovel Knight, which was released in 2014. The purpose is to produce a greater impact on the user through the audiovisual pro- duction of the videogame. An analysis of attention foci and colors based on a perception algorithm are studied and compared for this videogame and two versions of it. We study the visual impact of videogames according to characteristics such as: color focuses, types of color with the intention of creating the videogame, player’s emotional state in front of the images, and so on, obtaining a resembling to the reality of the software of 5%. Finally, as a case of study, we make a videogame done with real environment landscapes and characters and we contrast the same videogame made digitally in its original version. We obtained that the first version has a greater acceptance and enjoyment regarding the second one, with 95% of higher impact of attention for 35 people. This case analyzed the visual impact according to the criteria that videogames can generate in players.

Wireless Sensor Networks (WSNs) base their performance on the routing protocol, which is in charge of managing information and making the network as resilient as possible. This work is based on performance metrics comparisons of three types of protocols (reactive, proactive and hybrid). In order to analyze wireless networks behavior and energy performance, simulations and real scenarios are ran on a university campus in Guadalajara, Mexico. The environment recreates the necessary conditions to evaluate and extrapolate the characteristics of a Smart Campus. With the implemented methodology, we can understand which performance metrics are impacting directly in a greater or lesser extent on three examples of well-known routing protocols. It is concluded that the hybrid protocol has 10% better performance than the others because it has the appropriate complexity and design to manage the packets under parameters such as: route validity and response time, overhead, packet re-transmissions, complexity, delay, resilience and energy. The simulation tool has an accuracy of 93 %, similar to the results thrown in a real environment where sensors are dispersed in 300 square meters of the campus.

Haptic technology allows producing computergenerated signals that a perceiver experiences through touch. In general, haptic devices can be classified as active and passive. In this paper, we present a performance evaluation of a group of 10 voluntary subjects in a task of shape perception when both active and passive kinesthetic haptic feedback are provided. The results of a series of psychophysical experiments show that active haptics is superior for shape recognition: active haptics enhances the perception of surfaces while passive haptics conveys the sensation of being guided along pathways. In addition, the guidance nature of passive haptics makes the pathway direction easy to recognize. This last observation can be effectively exploited in more challenging tasks such as navigation/mobility assistance.

The vehicle-to-vehicle (V2V) communications require to transmit over doubly selective channels (DSC) with high intercarrier interference (ICI). These characteristics degrade the performance significantly in terms of bit error rate (BER) of orthogonal frequency division multiplexing (OFDM) systems. State-of-theart solutions involve receivers with high computational complexity or a reduced system spectral efficiency. This paper investigates the BER performance of OFDM system with virtual carriers (VC) employing the maximal ratio combining (MRC)-based low-complexity detector. The results obtained show that the use of VC provides diversity in the data received, which allows to obtain a gain of ≥ 5 dB compared to the OFDM system with linear/non-linear detector. In addition, the proposed detector has linear complexity, which makes it an attractive solution for the implementation of realtime vehicular systems.

Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.

Imagine being able to connect to the work network from a public park and then meet a friend for coffee or shopping. Imagine finding everything a tourist needs, such as bus schedules, nearby restaurants and other entertainment options in touch screen kiosks conveniently located throughout the city. Most applications of IoT depend on a battery for their operation and they are designed to reduce or even avoid the human intervention in the sensing process. Most IoT projects are motivated by a need to reduce operating energy costs or increase revenue. This paper presents and analyses the energy model of a wireless sensor using four different routing protocols: Multi-Parent Hierarchical (MPH), On Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and ZigBee Tree Routing (ZTR). In these applications, the energy consumption is a key factor, sensors can be located in remote zones difficult to access, so it is not possible to replace the battery continuously. Due to the limitations of battery life, the nodes are designed to save as much energy as possible, and most of the time they are in sleep mode (low power consumption). Finding energy sources for difficult-to-connect device has become a priority for technology, in large part due to the rise of the IoT concept. This is why the network itself must provide energy saving mechanisms and a good solution could be in charge of the packets administration in the network. required format.

Real-time haptic interactions occur under two exploration modes: active and passive. In this paper, we present a series of experiments that evaluate the main perceptual characteristics of both exploration modes. In particular, we focus on haptic shape recognition as it represents a fundamental task in many applications using haptic environments. The results of four experiments conducted with a group of 10 voluntary subjects show that the differences in motor activity between active and passive haptics ease the perception of surfaces for the first case and the perception of pathways for the latter. In addition, the guidance nature of passive haptics makes the pathway direction easy to recognize. This work shows that this last observation could find application in more challenging tasks such as navigation in space.

Power electronics grid integrated devices such as Flexible Alternating Current Transmission Systems (FACTS) and Custom Power Devices are a very active research field. In most of the cases, research must be supported by experimental results. In that cases, a three phase programmable voltage source and a three-phase programmable electronic load may be used to validate the device under test, but that equipment are relatively expensive for most laboratories. If a laboratory has only the programmable voltage source, non-energy-recycling experiments may be performed with a passive load, with the respective cost and environmental sacrifice (the energy will be dissipated in the load). This paper presents a design and a laboratory implementation of a three-phase energy recycling test bench for power flow experiments, the proposed design can be built with equipment available in most laboratories at low cost.

Railway is a critical application; hence, all systems that compose the railway infrastructure must meet two conditions: availability and safety. The availability ensures continuous operation of the system; on the other hand, safety is achieved when the device works properly regardless of the environmental or operating conditions. In addition, Wireless Sensor Networks (WSN) are used to perform tasks previously performed manually. However, it is necessary to analyse what protocol is appropriate for the railway industry, since availability and safety are the required attributes. In this work, a recently proposed routing protocol, the Multi-Parent Hierarchical (MPH), has been compared with a well-known protocol, the Ad-hoc On-Demand Distance Vector (AODV), in order to find the most suitable one for the railway applications. For this purpose, a simulator has been developed, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad-hoc network given by the number and location of participants, and general network conditions.

Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols.

The Internet of Things has introduced a new era of devices capable of sensing different parameters from an environment. Unfortunately, these sensors use multiple standards and are not always designed to work together in a Wireless Body Area Network (WBAN). We studied various applications of the combination of these devices to try and establish an ideal environment which reduces interference and improves data delivery reliability. In this paper we propose a graphical network simulator capable of scaling multiple scenarios, which was tested with a real environment, simulating a WBAN, using five nodes in a ZigBee network where one of them acted as the coordinator and with an approximate overall error rate of ±13.5% in performance metrics such as Packet Delivery Ratio, Link Quality Indicator and Received Signal Strength Indication. Finally, we were able to extrapolate the number of nodes to predict the scalability and the interference influence on a network.

Made available in DSpace on 2018-11-26T16:01:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-06-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) CONACyT EU Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Science Foundation Ireland (SFI) European Regional Development Fund Survivability is a critical requirement of optical communication networks that is typically addressed implementing path diversity. However, due to the elevated cost of fiber installation, this approach may prove prohibitively expensive in optical access networks. In this paper, a novel cost-efficient photonic millimeter (mm)-wave bridge is proposed, which converts passive optical network (PON) signals to radiofrequency signals at mm-wave bands. The performance of the mm-wave photonic bridge is numerically tested, revealing its feasibility to transmit a 2.5-Gbps PON with -55.6 dB wireless link gain (WLG) using the 81-86 GHz band and 10-Gbps PON with -35 dB at the 102-109.5 GHz band. The effect of fiber is also analyzed, showing that fiber cuts closer to the optical network unit degrades more the system performance. (C) 2018 Elsevier B.V. All rights reserved. Sao Paulo State Univ, Campus Sao Joao da Boa Vista, Sao Paulo, SP, Brazil Univ Panamer, Fac Ingn, Prolongac Calzada Circunvalac, Poniente 49, Zapopan 45010, Jalisco, Mexico Tecnol Monterrey, Sch Engn, Monterrey, Mexico Dublin City Univ, Sch Elect Engn, Dublin 9, Ireland Sao Paulo State Univ, Campus Sao Joao da Boa Vista, Sao Paulo, SP, Brazil FAPESP: 2015/04113-0 EU Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie: 713567 European Regional Development Fund: 13/RC/2077

In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad hoc network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routes update, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results.