Treatment of effluents from Food Services Establishment (FSEs) by physico-chemical processes: a case study for Trinidad & Tobago.

BACKGROUND: Effluents from Food Services Establishments (FSEs) contain primarily Fats, Oil and Grease (FOG) which severely impact on sewers and the environment when released in high concentrations. In Trinidad & Tobago, it is estimated that approximately 231,304 kg/day of unaccounted for FOG bearing wastewaters from FSEs, are released into the environment with no viable treatment in the country. This research explored the optimization of physico-chemical processes for the treatment of FOGs for subsequent release into sewers. RESULTS: Bench-scale studies analysed the characteristics of FSE's effluents from three popular sources, conducted the treatment of these effluents using Jar Tests, and subsequently confirm results via a pilot plant study. Characterization showed the mean concentration of the parameters examined to be; FOG (511 mg/l ± 116 mg/l), Suspended Solids (446 mg/l ± 146 mg/l), Chemical Oxygen Demand (2229 mg/l ± 963 mg/l) and pH (6 ± 0.3). Jar Tests were conducted using Poly-aluminium Chloride (PACl) as coagulant, anionic and cationic polyelectrolytes as flocculant aids with suitable pH adjustments of samples to determine the isoelectric point for the coagulant. Effluent results showed FOG removal levels of 99.9% and final effluent concentration of 0.17 mg/l. This was attained using PACl concentration of 250 mg/l, a 0.1% low cationic polyelectrolyte (CP 1154) at 4 mg/l with the pH of sample adjusted to 8. The pilot plant achieved a 97.4% removal of FOG (residual of 16.8 mg/l) using the same coagulant dosing, and pH value, but increasing the strength of the flocculant aid to 0.1% medium cationic (CP1156) at 5 mg/l. CONCLUSION: Experimentation showed high concentrations of emulsified FOG can be efficiently removed to levels below the permissible requirements (20 mg/l) for entry into sewer systems in Trinidad and Tobago using coagulation, flocculation and sedimentation techniques. Pilot scale study also revealed that a higher strength and/or dose of the cationic polyelectrolyte and increased times in primary and final tanks were required to attain the desired results as in the bench level study, where equipment limitations in the flocculation tank were faced. This is in alignment with theory where factors critical for agglomeration is equipment type and density charge. It is, concluded that the optimum combination of chemicals and the respective dosages attained at the bench level study should prove effective should the right equipment be made available.

Fog controls biological cycling of soil phosphorus in the Coastal Cordillera of the Atacama Desert.

Soils in hyper-arid climates, such as the Chilean Atacama Desert, show indications of past and present forms of life despite extreme water limitations. We hypothesize that fog plays a key role in sustaining life. In particular, we assume that fog water is incorporated into soil nutrient cycles, with the inland limit of fog penetration corresponding to the threshold for biological cycling of soil phosphorus (P). We collected topsoil samples (0-10 cm) from each of 54 subsites, including sites in direct adjacency (<10 cm) and in 1 m distance to plants, along an aridity gradient across the Coastal Cordillera. Satellite-based fog detection revealed that Pacific fog penetrates up to 10 km inland, while inland sites at 10-23 km from the coast rely solely on sporadic rainfall for water supply. To assess biological P cycling we performed sequential P fractionation and determined oxygen isotope of HCl-extractable inorganic P δ 18 O HCl - P i $$ \mathrm{P}\ \left({\updelta}^{18}{\mathrm{O}}_{\mathrm{HCl}-{\mathrm{P}}_{\mathrm{i}}}\right) $$ . Total P (Pt ) concentration exponentially increased from 336 mg kg-1 to a maximum of 1021 mg kg-1 in inland areas ≥10 km. With increasing distance from the coast, soil δ 18 O HCl - P i $$ {\updelta}^{18}{\mathrm{O}}_{\mathrm{HCl}-{\mathrm{P}}_{\mathrm{i}}} $$ values declined exponentially from 16.6‰ to a constant 9.9‰ for locations ≥10 km inland. Biological cycling of HCl-Pi near the coast reached a maximum of 76%-100%, which could only be explained by the fact that fog water predominately drives biological P cycling. In inland regions, with minimal rainfall (<5 mm) as single water source, only 24 ± 14% of HCl-Pi was biologically cycled. We conclude that biological P cycling in the hyper-arid Atacama Desert is not exclusively but mainly mediated by fog, which thus controls apatite dissolution rates and related occurrence and spread of microbial life in this extreme environment.

Dos nuevos registros para la flora agrostológica de Perú / Two new records for the agrostological flora of Peru

Resumen Se reporta por primera vez la presencia de Piptochaetium bicolor (Vahl) É. Desv. y Trichachne californica var. villosissima (Henrard) Wipff & Shaw para Perú, basada en colecciones realizadas en el ecosistema de formación de lomas costeras del departamento de Arequipa. Se presentan descripciones, fotografías, ilustraciones y una clave taxonómica para cada taxón.

Abstract The presence of Piptochaetium bicolor (Vahl) É. Desv. and Trichachne californica var. villosissima (Henrard) Wipff & Shaw are recorded for the first time from Peru, based upon collections made in the coastal lomas formations ecosystem of the department of Arequipa. Descriptions, photographs, illustrations and a taxonomic key are presented for each taxon.

La niebla mental, una manifestación neurocognitiva del síndrome post-COVID-19 / Brain fog, a neurocognitive manifestation of the post-COVID-19 syndrome

Introducción: La COVID-19, infección causada por el SARS-CoV-2, ocasiona daños a diferentes órganos y sistemas, como el sistema nervioso central. Entre las alteraciones neurológicas se describe la niebla mental como manifestación neurocognitiva frecuente en el síndrome post-COVID-19, con un impacto negativo en la calidad de vida de los pacientes. Se revisaron 104 artículos publicados desde junio 2020 a octubre del 2022, en las bases de datos Pubmed, Medline, Lilacs y Cumed. Objetivo: Actualizar conocimientos sobre las manifestaciones neurocognitivas de niebla mental en el síndrome post-COVID-19. Desarrollo: Se describen alteraciones neurocognitivas de niebla mental, trastornos de atención, concentración y memoria, asociados a otros síntomas neurológicos, como cefalea, insomnio, anosmia, ageusia, ansiedad, depresión, y otros síntomas persistentes, que caracterizan al síndrome post-COVID-19. Se hace referencia a elementos de la etiopatogenia, resaltando la respuesta inmune sistémica exagerada, generada por la liberación de citoquinas, aspectos a tener presentes para la conducta diagnóstica y terapéutica de los pacientes post-COVID-19. Conclusiones: Los síntomas neurocognitivos de niebla mental, constituyen las alteraciones neurológicas frecuentes del síndrome post-COVID-19, son variados, con combinación de diferentes síntomas en cada enfermo, más frecuentes en mujeres y en pacientes que presentaron enfermedad grave(AU)

Introduction: COVID-19, infection caused by SARS-CoV-2, causes damage to different organs and systems, such as the central nervous system. Among the neurological alterations, brain fog is described as a frequent neurocognitive manifestation in post-COVID-19 syndrome, with a negative impact on patients' quality of life; 104 articles published were reviewed from June 2020 to October 2022, in Pubmed, Medline, Lilacs and Cumed databases. Objective: To update knowledge on the neurocognitive manifestations of brain fog in post-COVID-19 syndrome. Development: Neurocognitive alterations of mental fog, attention, concentration and memory disorders, associated with other neurological symptoms, such as headache, insomnia, anosmia, ageusia, anxiety, depression, and other persistent symptoms, which characterize post-COVID-19 syndrome, are described. Reference is made to elements of the etiopathogenesis, highlighting the exaggerated systemic immune response, generated by the release of cytokines, aspects to keep in mind for the diagnostic and therapeutic conduct of post-COVID-19 patients. Conclusions: The neurocognitive symptoms of brain fog are frequent neurological alterations of post-COVID-19 syndrome, they are varied, with a combination of different symptoms in each patient, more frequent in women and in patients who presented severe disease(AU)

Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments.

At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning.

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.

Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud.

Purpose: Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully. Methods: This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing. Results: Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities. Conclusions: This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.

Captadores de niebla y recolección de agua en un ecosistema de páramo colombiano

Los captadores de niebla son usados para interceptar agua contenida en la niebla y abastecer de agua a comunidades que habitan en lugares donde este recurso escasea. Se evaluó el uso de captadores de niebla para la captación de agua en un área ubicada en el páramo Pan de Azúcar, Duitama-Boyacá. Se instalaron 60 captadores de niebla, 24 de ellos, con dispositivos para medir los volúmenes de agua interceptados. Los volúmenes de agua captados, se midieron en periodos de 24 horas, por 26 días, durante un año y se usó el modelo geométrico para diferenciar el agua proveniente de la niebla. La precipitación registrada fue mayor a la reportada en la literatura. La precipitación mensual osciló entre 51 y 1198 mm y la temperatura media mensual entre los 6 y 8 °C. Los volúmenes de agua promedio colectados por los 24 captadores de niebla estuvieron entre los 0,02 Lm-2dia-1 hasta los 4,4Lm-2dia-1. Los aportes de agua provenientes de la niebla oscilaron entre los 0,02 y 1,77 mmdía-1. La dirección del viento no afectó la captación de agua y aún se presenta incertidumbre al separar el aporte real de agua proveniente de la niebla a partir de la lluvia orográfica, lo cual, sigue siendo un desafío en los ecosistemas de páramo, por lo que se debe ampliar la investigación, para mejorar los diseños y las eficiencias de los captadores de niebla.

Fog collectors are used to intercept water contained in fog and supply water to communities that live in places where this resource is scarce. We evaluated the use of mist collectors to collect water in an area located in the Pan de Azúcar paramo, Duitama-Boyacá. We installed 60 mist collectors, 24 of them with devices to measure the volumes of water intercepted. The volumes of water captured were measured in periods of 24 hours for 26 days during one year and we used the geometric model to differentiate the water from the fog. The recorded precipitation was higher than that reported in the literature. Monthly rainfall ranged between 51 and 1198mm and mean monthly temperature ranged between 6 and 8°C. The average volumes of water collected by the fog collectors were below 0.5Lm-2day-1 with a maximum of 4.4Lm-2day-1. The contributions of water from the mist ranged between 0.02 and 1.77 mmday-1. The direction of the wind did not affect the capture of water and there is still uncertainty when separating the real contribution of water from the fog from the orographic rain, which continues to be a challenge in the paramo ecosystems, for which it is necessary to expand research, to improve the designs and efficiencies of fog collectors.

A Comprehensive Survey on Resource Allocation Strategies in Fog/Cloud Environments.

The growing number of connected objects has allowed the development of new applications in different areas. In addition, the technologies that support these applications, such as cloud and fog computing, face challenges in providing the necessary resources to process information for different applications due to the highly dynamic nature of these networks and the many heterogeneous devices involved. This article reviews the existing literature on one of these challenges: resource allocation in the fog-cloud continuum, including approaches that consider different strategies and network characteristics. We also discuss the factors influencing resource allocation decisions, such as energy consumption, latency, monetary cost, or network usage. Finally, we identify the open research challenges and highlight potential future directions. This survey article aims to serve as a valuable reference for researchers and practitioners interested in the field of edge computing and resource allocation.

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors' Communication Optimization in an IoT-Fog Ecosystem.

With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

On the Response to Aging of OPEFB/Acrylic Composites: A Fungal Degradation Perspective.

Biological agents and their metabolic activity produce significant changes over the microstructure and properties of composites reinforced with natural fibers. In the present investigation, oil palm empty fruit bunch (OPEFB) fiber-reinforced acrylic thermoplastic composites were elaborated at three processing temperatures and subjected to water immersion, Prohesion cycle, and continuous salt-fog aging testing. After exposition, microbiological identification was accomplished in terms of fungal colonization. The characterization was complemented by weight loss, mechanical, infrared, and thermogravimetric analysis, as well as scanning electron microscopy. As a result of aging, fungal colonization was observed exclusively after continuous salt fog treatment, particularly by different species of Aspergillus spp. genus. Furthermore, salt spray promoted filamentous fungi growth producing hydrolyzing enzymes capable of degrading the cell walls of OPEFB fibers. In parallel, these fibers swelled due to humidity, which accelerated fungal growth, increased stress, and caused micro-cracks on the surface of composites. This produced the fragility of the composites, increasing Young's modulus, and decreasing both elongation at break and toughness. The infrared spectra showed changes in the intensity and appearance of bands associated with functional groups. Thermogravimetric results confirmed fungal action as the main cause of the deterioration.

Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.

New Proposal of Epiphytic Bromeliaceae Functional Groups to Include Nebulophytes and Shallow Tanks.

The Bromeliaceae family has been used as a model to study adaptive radiation due to its terrestrial, epilithic, and epiphytic habits with wide morpho-physiological variation. Functional groups described by Pittendrigh in 1948 have been an integral part of ecophysiological studies. In the current study, we revisited the functional groups of epiphytic bromeliads using a 204 species trait database sampled throughout the Americas. Our objective was to define epiphytic functional groups within bromeliads based on unsupervised classification, including species from the dry to the wet end of the Neotropics. We performed a hierarchical cluster analysis with 16 functional traits and a discriminant analysis, to test for the separation between these groups. Herbarium records were used to map species distributions and to analyze the climate and ecosystems inhabited. The clustering supported five groups, C3 tank and CAM tank bromeliads with deep tanks, while the atmospheric group (according to Pittendrigh) was divided into nebulophytes, bromeliads with shallow tanks, and bromeliads with pseudobulbs. The two former groups showed distinct traits related to resource (water) acquisition, such as fog (nebulophytes) and dew (shallow tanks). We discuss how the functional traits relate to the ecosystems inhabited and the relevance of acknowledging the new functional groups.

Possible Application of Melatonin in Long COVID.

Clinical sequelae and symptoms for a considerable number of COVID-19 patients can linger for months beyond the acute stage of SARS-CoV-2 infection, "long COVID". Among the long-term consequences of SARS-CoV-2 infection, cognitive issues (especially memory loss or "brain fog"), chronic fatigue, myalgia, and muscular weakness resembling myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are of importance. Melatonin may be particularly effective at reducing the signs and symptoms of SARS-CoV-2 infection due to its functions as an antioxidant, anti-inflammatory, and immuno-modulatory agent. Melatonin is also a chronobiotic medication effective in treating delirium and restoring the circadian imbalance seen in COVID patients in the intensive care unit. Additionally, as a cytoprotector, melatonin aids in the prevention of several COVID-19 comorbidities, including diabetes, metabolic syndrome, and ischemic and non-ischemic cardiovascular diseases. This narrative review discusses the application of melatonin as a neuroprotective agent to control cognitive deterioration ("brain fog") and pain in the ME/CFS syndrome-like documented in long COVID. Further studies on the therapeutic use of melatonin in the neurological sequelae of SARS-CoV-2 infection are warranted.

On the Efficient Delivery and Storage of IoT Data in Edge-Fog-Cloud Environments.

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge-fog-cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge-fog-cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.

HOsT: Towards a Low-Cost Fog Solution via Smart Objects to Deal with the Heterogeneity of Data in a Residential Environment.

With the fast and unstoppable development of technology, the amount of available technological devices and the data they produce is overwhelming. In analyzing the context of a smart home, a diverse group of intelligent devices generating constant reports of its environment information is needed for the proper control of the house. Due to this demand, many possible solutions have been developed in the literature to assess the need for processing power and storage capacity. This work proposes HOsT (home-context-aware fog-computing solution)-a solution that addresses the problems of data heterogeneity and the interoperability of smart objects in the context of a smart home. HOsT was modeled to compose a set of intelligent objects to form a computational infrastructure in fog. A publish/subscribe communication module was implemented to abstract the details of communication between objects to disseminate heterogeneous information. A performance evaluation was carried out to validate HOsT. The results show evidence of efficiency in the communication infrastructure; and in the impact of HOsT compared with a cloud infrastructure. Furthermore, HOsT provides scalability about the number of devices acting simultaneously and demonstrates its ability to work with different devices.

An Edge-Fog Architecture for Distributed 3D Reconstruction and Remote Monitoring of a Power Plant Site in the Context of 5G.

It is well known that power plants worldwide present access to difficult and hazardous environments, which may cause harm to on-site employees. The remote and autonomous operations in such places are currently increasing with the aid of technology improvements in communications and processing hardware. Virtual and augmented reality provide applications for crew training and remote monitoring, which also rely on 3D environment reconstruction techniques with near real-time requirements for environment inspection. Nowadays, most techniques rely on offline data processing, heavy computation algorithms, or mobile robots, which can be dangerous in confined environments. Other solutions rely on robots, edge computing, and post-processing algorithms, constraining scalability, and near real-time requirements. This work uses an edge-fog computing architecture for data and processing offload applied to a 3D reconstruction problem, where the robots are at the edge and computer nodes at the fog. The sequential processes are parallelized and layered, leading to a highly scalable approach. The architecture is analyzed against a traditional edge computing approach. Both are implemented in our scanning robots mounted in a real power plant. The 5G network application is presented along with a brief discussion on how this technology can benefit and allow the overall distributed processing. Unlike other works, we present real data for more than one proposed robot working in parallel on site, exploring hardware processing capabilities and the local Wi-Fi network characteristics. We also conclude with the required scenario for the remote monitoring to take place with a private 5G network.

Single-Pixel Near-Infrared 3D Image Reconstruction in Outdoor Conditions.

In the last decade, the vision systems have improved their capabilities to capture 3D images in bad weather scenarios. Currently, there exist several techniques for image acquisition in foggy or rainy scenarios that use infrared (IR) sensors. Due to the reduced light scattering at the IR spectra it is possible to discriminate the objects in a scene compared with the images obtained in the visible spectrum. Therefore, in this work, we proposed 3D image generation in foggy conditions using the single-pixel imaging (SPI) active illumination approach in combination with the Time-of-Flight technique (ToF) at 1550 nm wavelength. For the generation of 3D images, we make use of space-filling projection with compressed sensing (CS-SRCNN) and depth information based on ToF. To evaluate the performance, the vision system included a designed test chamber to simulate different fog and background illumination environments and calculate the parameters related to image quality.

A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets' quality and map the areas with the most significant anomalies.