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Abstract Background The increase in cases of mild cognitive impairment (MCI) underlines the urgency of finding effective methods to slow its progression. Given the limited effectiveness of current pharmacological options to prevent or treat the early stages of this deterioration, non-pharmacological alternatives are especially relevant. Objective To assess the effectiveness of a cognitive-motor intervention based on immersive virtual reality (VR) that simulates an activity of daily living (ADL) on cognitive functions and its impact on depression and the ability to perform such activities in patients with MCI. Methods Thirty-four older adults (men, women) with MCI were randomized to the experimental group (n = 17; 75.41 ± 5.76) or control (n = 17; 77.35 ± 6.75) group. Both groups received motor training, through aerobic, balance and resistance activities in group. Subsequently, the experimental group received cognitive training based on VR, while the control group received traditional cognitive training. Cognitive functions, depression, and the ability to perform activities of daily living (ADLs) were assessed using the Spanish versions of the Montreal Cognitive Assessment (MoCA-S), the Short Geriatric Depression Scale (SGDS-S), and the of Instrumental Activities of Daily Living (IADL-S) before and after 6-week intervention (a total of twelve 40-minutes sessions). Results Between groups comparison did not reveal significant differences in either cognitive function or geriatric depression. The intragroup effect of cognitive function and geriatric depression was significant in both groups (p

Abstract The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users’ well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users’ trust. As technology continues to advance, the refinement of IoT devices’ design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals’ health outcomes and the role of wearables in promoting a healthier lifestyle.

Smart contracts have gained significant attention in the software development community because they are the mechanism for building blockchain applications. This work provides a distributed software architecture and an implementation for accessing a smart contract, called NFT-Vehicle, implemented on the Ethereum blockchain platform. The main idea behind NFT-Vehicle is the digitization process of a motorized vehicle in a digital asset. The architecture outlines two security protocols, one from the stakeholders who connect towards the back-end, and the other internally. The back-end includes mainly two parts: an API-gateway and the smart contract located in a blockchain. We report the acquired experiences and a list of identified errors found during the unit tests that are difficult to notice with simple requests. Our solution can be used as a base for real implementations of tokenizing physical cars into digital assets.

In recent years, the focus on gait therapy research has intensified, driven by a desire to enhance session efficiency. Accelerating rehabilitation timelines is imperative to accommodate a larger patient population annually. For instance, the Centro de Rehabilitacion Infantil Teleton, a prominent rehabilitation center in Mexico, operates at total capacity, leading to patient rejections. The acceleration of rehabilitation relies on encouraging patients to engage in active muscle efforts. Significant strides have been made in this direction, leveraging motivational techniques like virtual reality. However, a crucial aspect that requires attention is the customization of the environment to adapt to the evolving motivational states of patients. This personalized approach is an essential dimension that remains to be fully explored. Our research addresses this gap by introducing a rehabilitation scenario with a patient in a walking machine while immersed in a virtual reality environment and having a brain-computer interface tracking his affective states encompassing engagement, boredom, meditation, frustration, and excitement. The virtual reality environment dynamically adjusts in real-time using affect to trigger motivational stimuli. A study involving 27 participants, with 13 in the control group and 14 in the experimental group, showed the potential of this approach. The results indicate a significant improvement in the active effort for subjects in the experimental group, yielding an efficiency increase of 54.25% with a p-value below 0.05.

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions.

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals’ overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

Genre categorization of published titles is a common practice in publishing houses, libraries, and bookstores, as well as a fundamental element of editorial marketing. However, assigning subject codes to each title proves to be an arduous task for both publishers and data aggregators. The problem with automatic genre categorization is that some publishers use more than 200 categories, making it a highly complex task. Moreover, even though these publishers based their categorization on standards, they ofthen alter the names of these standards as they consider to be too technical. In this paper, we proposed Thema-based categorization as a tool to facilitate editors’ work by advancing the categorization process, allowing them to focus on finer category granularity. This categorization has four key features: first, it clusters the most important categories for Latin American publishers. Second, it stops grouping when the number of thematic categories remains practical for the purposes of the publishing business. Third, we assign names to these categories that resonate with Latin American stakeholders. Finally, the number of categories is optimized to provide reasonable classification performance. We worked on the description of books in Spanish of two publishers, and mapped them to this proposed categorization. This allowed us to created a database for train a model to automate categorization. After conducting our analysis, we determined that 26 thematic categories were an appropriate number that fulfilled the three features mentioned earlier. However, we recognized that classifying into 26 categories was still a complex task, so to overcome this challenge, we decided to augment data by back-translating it into Spanish using the translation function, $T_{l}^{S}\left ({T_{S}^{l}\left ({S }\right) }\right)$ , where $T_{S}^{l}\left ({S }\right)$ is the translation function from Spanish, $s$ , to language, $l$ ; $T_{l}^{s}\left ({l }\right)$ is the translation function from language, $l$ , to Spanish, $S$ , and $T_{S}^{l}\left ({S }\right)$ and $T_{l}^{S}\left ({l }\right)$ are not-invertible functions. Experimental results, obtained using 5-fold cross-validation, were approximately 57%, 57%, 63.38%, and 65.26% for the F1-score of Support Vector Machine (SVM), Logist Regression (LR), BERT, and RoBERTa models, respectively. We utilized the F1-score metric because our categories were not perfectly balanced. The results achieved by RoBERTa outperform those reported in the literature. Furthermore, these results are built upon the foundation of the Thema standard for categorizing book genres. Additionally, the categories have been specifically designed to align with the preferences and needs of Latin American publishers.

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient’s state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient’s home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person’s sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network.

This work presents a study in which the cognitive concentration levels of participants were evaluated using electroencephalogram (EEG) measures while they were playing three different categories of virtual reality (VR) video games: Challenging Puzzlers, Casual Games, and Exergames. Thirty-one voluntary participants between the ages of 17 and 35 were recruited. EEG data were processed to analyze the brain’s electrical activity in the alpha band. The values of power spectral density (PSD) and individual alpha frequency (IAF) of each participant were compared to detect changes that could indicate a state of concentration. Additionally, frontal alpha asymmetry (FAA) between the left and right hemispheres of the brain was compared. The results showed that the Exergame category of video games elicited higher average cognitive concentration in players, as indicated by the IAF and FAA values. These findings contribute to understanding the cognitive effects of VR video games and their implications for designing and developing VR experiences to enhance cognitive abilities.

The sale of second-hand vehicles is a popular trade worldwide, and vehicle fraud is currently a common issue, mainly because buyers can lack a complete view of the historical transactions related to their new acquisition. This work presents a distributed architecture for stakeholders to register transactions over a vehicle’s life cycle in a blockchain network. The architecture involves a non-fungible token (NFT) linked to a physical motorized vehicle after a tokenization process, which denote as the NFT-Vehicle. The NFT-Vehicle is a hierarchical smart contract designed using an object-oriented paradigm and a modified version of the ERC721 standard. Every stakeholder engages with the NFT-Vehicle through distinct methods embedded within a smart contract. These methods represent internal protocols meticulously formulated and validated based on a finite-state machine (FSM) model. We implemented our design as a proof of concept using a platform based on Ethereum and a smart contract in the Solidity programming language. We carried out two types of proof: (a) validations, following the FSM model to ensure that the smart contract remained in a consistent state, and (b) proofs, to achieve certainty regarding the amount of ETH that could be spent in the life cycle of a vehicle. The results of the tests showed that the total transaction cost for each car throughout its life cycle did not represent an excessive cost considering the advantages that the system could offer to prevent fraud.

5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services.

The roads we travel daily are exposed to several energy sources (mechanical load, solar radiation, heat, air movement, etc.), which can be exploited to make common systems and apparatus for roadways (i.e., lighting, video surveillance, and traffic monitoring systems) energetically autonomous. For decades, research groups have developed many technologies able to scavenge energy from the said sources related to roadways: electromagnetism, piezoelectric and triboelectric harvesters for the cars’ stress and vibrations, photovoltaic modules for sunlight, thermoelectric solutions and pyroelectric materials for heat and wind turbines optimized for low-speed winds, such as the ones produced by moving vehicles. Thus, this paper explores the existing technologies for scavenging energy from sources available on roadways, both natural and related to vehicular transit. At first, to contextualize them within the application scenario, the available energy sources and transduction mechanisms were identified and described, arguing the main requirements that must be considered for developing harvesters applicable on roadways. Afterward, an overview of energy harvesting solutions presented in the scientific literature to recover energy from roadways is introduced, classifying them according to the transduction method (i.e., piezoelectric, triboelectric, electromagnetic, photovoltaic, etc.) and proposed system architecture. Later, a survey of commercial systems available on the market for scavenging energy from roadways is introduced, focusing on their architecture, performance, and installation methods. Lastly, comparative analyses are offered for each device category (i.e., scientific works and commercial products), providing insights to identify the most promising solutions and technologies for developing future self-sustainable smart roads.

Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN).

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.

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.

Wearable technology represents a new technological paradigm for promoting physical activity, enabling monitoring of performances and athletic gestures. In addition, they can be employed for remote health monitoring applications, allowing continuous acquisition of users’ vital signs directly at home, emergency alerting, and computer-assisted rehabilitation. Commonly, these devices depend on batteries which are not the better option since researchers aim for dispositive who need minimal human intervention. Energy harvesting devices can be useful to extract energy from the human body, especially by integrating them into the garments, giving health monitoring devices enough energy for their independent operation. This review work focuses on the main new wearable technologies and devices to scavenge energy from the human body. First, the most suitable energy sources exploitable for wearable applications are investigated. Afterward, an overview of the main harvesting technologies (piezoelectric, triboelectric, thermoelectric, solar fabrics, and hybrid solution) is presented. In detail, we focused on flexible and thin textiles with energy harvesting capability, allowing easy integration into clothes fabric. Furthermore, comparative analyses of each harvesting technology are proposed, providing useful insights related to the best technologies for developing future self-sustainable wearable devices. Finally, a comparison between our review work and similar ones is introduced, highlighting its strengths in completeness and specificity.

User experience (UX) is key in the immediate and future relationship between the client and business. Achieving a satisfying UX can only be achieved by understanding the wishes and user needs. The following study is carried out as an improvement tool for a Mexican coffee company. The objective is to achieve greater efficiency, attraction, and engagement on the part of the user. The main question is whether the new dynamic website design can directly increase the valence of user emotions compared to the static website design. To answer this question, 39 participants were exposed to the two different web page designs and elicited the following emotions using eye tracking and facial expression analysis (FEA) techniques: joy, anger, surprise, fear, contempt, disgust, sadness, neutral, positive, and negative. Through a Wilcoxon signed-rank test, the results showed a significant increase for the new dynamic design in the following emotions; joy, anger, surprise, disgust, fear and neutral. Thus, five of the seven basic emotions had a significant change that could lead to greater attraction and commitment on the part of the user and also influence, either consciously or unconsciously, their decision when interacting with the company.

Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated into power networks, high-voltage (HV) power-line communication has become attractive. The main constraint of conventional HV power-line carrier (PLC) systems is their unfeasibility for being migrated to wideband channels, even with a high signal-to-noise ratio (SNR). In this scenario, none of the current linear/non-linear equalizers used in single carrier schemes achieve the complete compensation of the highly dispersive conditions, which limits their operation to 4 kHz channels. In this paper, a new PLC-channel model is introduced for transmission lines incorporating the effects of the coupling equipment. In addition, the use of the single-carrier frequency-division modulation (SC-FDM) is proposed as a solution to operate PLC systems in a wide bandwidth, achieving transmission speeds above those of the conventional PLC system. The results presented in this paper demonstrate the superior performance of the SC-FDM-PLC over conventional PLC systems, obtaining a higher transmission capacity in 10 to 30 times.

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.

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.

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.

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.

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 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%.

The usage of wireless sensor devices in many applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in recent years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) is the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to intensify the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols.

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’ 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’ 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.

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running.

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.

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.

Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our Sensors 2014, 14 22812 simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.

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.

In this work, two new self-tuning collaborative-based mechanisms for jamming detection are proposed. These techniques are named (i) Connected Mechanism and (ii) Extended Mechanism. The first one detects jamming by comparing the performance parameters with respect to directly connected neighbors by interchanging packets with performance metric information, whereas the latter, jamming detection relays comparing defined zones of nodes related with a collector node, and using information of this collector detects a possible affected zone. The effectiveness of these techniques were tested in simulated environment of a quadrangular grid of 7 × 7, each node delivering 10 packets/sec, and defining as collector node, the one in the lower left corner of the grid. The jammer node is sending packets under reactive jamming. The mechanism was implemented and tested in AODV (Ad hoc On Demand Distance Vector), DSR (Dynamic Source Routing), and MPH (Multi-Parent Hierarchical), named AODV-M, DSR-M and MPH-M, respectively. Results reveal that the proposed techniques increase the accurate of the detected zone, reducing the detection of the affected zone up to 15% for AODV-M and DSR-M and up to 4% using the MPH-M protocol.

In this work, we present the design of a mitigation scheme for jamming attacks integrated to the routing protocols MPH, AODV, and DSR. The resulting protocols are named MPH-M (Multi-Parent Hierarchical - Modified), AODV-M (Ad hoc On Demand Distance Vector - Modified), and DSR-M (Dynamic Source Routing - Modified). For the mitigation algorithm, if the detection algorithm running locally in each node produces a positive result then the node is isolated; second, the routing protocol adapts their paths avoiding the isolated nodes. We evaluated how jamming attacks affect different metrics for all these modified protocols. The metrics we employ to detect jamming attack are number of packet retransmissions, number of CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) retries while waiting for an idle channel and the energy wasted by the node. The metrics to evaluate the performance of the modified routing protocols are the throughput and resilience of the system and the energy used by the nodes. We evaluated all the modified protocols when the attacker position was set near, middle and far of the collector node. The results of our evaluation show that performance for MPH-M is much better than AODV-M and DSR-M. For example, the node energy for MPH-M is 138.13% better than AODV-M and 126.07% better than DSR-M. Moreover, we also find that MPH-M benefits much more of the mitigation scheme than AODV-M and DSR-M. For example, the node energy consumption is 34.61% lower for MPH-M and only 3.92% and 3.42% for AODV-M and DSR-M, respectively. On throughput, the MPH protocol presents a packet reception efficiency at the collector node of 16.4% on to AODV and DSR when there is no mitigation mechanism. Moreover, MPH-M has an efficiency greater than 7.7% with respect to AODV-M and DSR-M when there is a mitigation scheme. In addition, we have that with the mitigation mechanism AODV-M and DSR-M do not present noticeable modification. However, MPH-M improves its efficiency by 8.4%. We also measure the resilience of these algorithms from the average packet re-transmissions perspective, and we find that MPH-M has around a 15% lower change rate than AODV-M and DSR-M. The MPH-M recovery time is 5 s faster than AODV-M and 2 s faster than DSR-M.