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Context-aware monitoring systems designed for e-Health solutions and ambient assisted living (AAL) play an important role in today’s personalized health-care services. The majority of these systems are intended for the monitoring of patients’ vital signs by means of bio-sensors. At present, there are very few systems that monitor environmental conditions and air quality in the homes of users. A home’s environmental conditions can have a significant influence on the state of the health of its residents. Monitoring the environment is the key to preventing possible diseases caused by conditions that do not favor health. This paper presents a context-aware system that monitors air quality to prevent a specific health problem at home. The aim of this system is to reduce the incidence of the Sudden Infant Death Syndrome, which is triggered mainly by environmental factors. In the conducted case study, the system monitored the state of the neonate and the quality of air while it was asleep. The designed proposal is characterized by its low cost and non-intrusive nature. The results are promising. [ABSTRACT FROM AUTHOR]

Optimization problems often require the use of optimization methods that permit the minimization or maximization of certain objective functions. Occasionally, the problems that must be optimized are not linear or polynomial; they cannot be precisely resolved, and they must be approximated. In these cases, it is necessary to apply heuristics, which are able to resolve these kinds of problems. Some algorithms linearize the restrictions and objective functions at a specific point of the space by applying derivatives and partial derivatives for some cases, while in other cases evolutionary algorithms are used to approximate the solution. This work proposes the use of artificial neural networks to approximate the objective function in optimization problems to make it possible to apply other techniques to resolve the problem. The objective function is approximated by a non-linear regression that can be used to resolve an optimization problem. The derivate of the new objective function should be polynomial so that the solution of the optimization problem can be calculated. [ABSTRACT FROM AUTHOR]

Nowadays, many citizens have busy days that make finding time for physical activity difficult. Thus, it is important to provide citizens with tools that allow them to introduce physical activity into their lives as part of the day's routine. This article proposes an app for an electric pedal-assist-system (PAS) bicycle that increases the pedaling intensity so the bicyclist can achieve higher and higher levels of physical activity. The app includes personalized assist levels that have been adapted to the user's strength/ability and a profile of the route, segmented according to its slopes. Additionally, a social component motivates interaction and competition between users based on a scoring system that shows the level of their performances. To test the training module, a case study in three different European countries lasted four months and included nine people who traveled 551 routes. The electric PAS bicycle with the app that increases intensity of physical activity shows promise for increasing levels of physical activity as a regular part of the day. [ABSTRACT FROM AUTHOR]

Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device. [ABSTRACT FROM AUTHOR]

E-nose systems are becoming increasingly important instruments across all industries, especially the fields of food and beverages and biomedicine. Given the inaccurate, unsafe and unreliable dependency on the human nose to detect smells that are highly risky and hazardous to human health, e-nose systems offer a tremendous advantage. E-noses are convenient, highly efficient and can be used in real life to detect various types of odors. This paper presents a virtual organization of agents that integrates different classification techniques and neural networks to perform information fusion from parameters retrieved by the E-nose. The integral brain in e-noses is the data processing system, which classifies odors that have been detected by the detection part of its system. The system mimics how a human brain classifies odors. [ABSTRACT FROM AUTHOR]

Among the most indispensable elements required by a city is electric power and the transmission towers used for its distribution. These towers have underground electrodes which must be reviewed on a regular basis by controlling different parameters to ensure that electrical resistance is in a secure range in order to avoid problems and risks. Using artificial intelligence, it is possible to ensure proper maintenance of the towers by estimating the required values and proposing a reduction in the size of the population sample, which will minimize the cost of operation. The use of an intelligent-agent virtual-organization based architecture is proposed within this working environment. By using mathematical estimation models and agreement based negotiations, the architecture is capable of maximizing estimations and minimizing associated costs. The proposed model has been evaluated with the developed software through a real case study, which permitted us to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Context-awareness in wireless sensor networks (WSNs) relies mainly on the position of objects and humans. The provision of this positional information becomes challenging in the harsh environmental conditions where WSNs are commonly deployed. With an antagonistic philosophy of design, fingerprinting and ranging have emerged as the key technologies underpinning wireless localization in harsh environments. Fingerprinting primarily focuses on accurate estimation at the expense of exhaustive calibration. Ranging mainly pursues an easy-to-deploy solution at the expense of moderate performance. In this paper, we present a resilient framework for sustained localization based on accurate fingerprinting in critical areas and light ranging in noncritical spaces. Such framework is conceived from the Bayesian perspective that facilitates the specification of recursive algorithms for real-time operation. In comparison to conventional implementations, we assessed the proposed framework in an indoor scenario with measurements gathered by commercial devices. The presented techniques noticeably outperform current approaches, enabling a flexible adaptation to the fluctuating conditions of harsh environments. [ABSTRACT FROM AUTHOR]

With the increase of intelligent systems based on Multi-Agent Systems (MAS) and the use of Wireless Sensor Networks (WSN) in context-aware scenarios, information fusion has become an essential part of this kind of systems where the information is distributed among nodes or agents. This paper presents a new MAS specially designed to manage data from WSNs, which was tested in a residential home for the elderly. The proposed MAS architecture is based on virtual organizations, and incorporates social behaviors to improve the information fusion processes. The data that the system manages and analyzes correspond to the actual data of the activities of a resident. Data is collected as the information event counts detected by the sensors in a specific time interval, typically one day. We have designed a system that improves the quality of life of dependant people, especially elderly, by fusioning data obtained by multiple sensors and information of their daily activities. The high development of systems that extract and store information make essential to improve the mechanisms to deal with the avalanche of context data. In our case, the MAS approach results appropriated because each agent can represent an autonomous entity with different capabilities and offering different services but collaborating among them. Several tests have been performed to evaluate this platform and preliminary results and the conclusions are presented in this paper. [ABSTRACT FROM AUTHOR]

There are currently different techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in different regions in the chromosome. Regions with gains or losses in pathologies are important for selecting relevant genes or CNVs (copy-number variations) associated with the variations detected within chromosomes. Information corresponding to mutations, genes, proteins, variations, CNVs, and diseases can be found in different databases and it would be of interest to incorporate information of different sources to extract relevant information. This work proposes a multiagent system to manage the information of aCGH arrays, with the aim of providing an intuitive and extensible system to analyze and interpret the results. The agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the final results and to extract relevant information from different sources of information by applying a CBR system. [ABSTRACT FROM AUTHOR]

The management of heterogeneous distributed sensor networks requires new solutions that can address the problem of automatically fusing the information coming from different sources in an efficient and effective manner. In the literature it is possible to find different types of data fusion and information fusion techniques in use today, but it is still a challenge to obtain systems that allow the automation or semiautomation of information processing and fusion. In this paper, we present a multiagent system that based on the organizational theory proposes a new model to automatically process and fuse information in heterogeneous distributed sensor networks. The proposed architecture is applied to a case study for indoor location where information is taken from different heterogeneous sensors. [ABSTRACT FROM AUTHOR]

Ambient intelligence has advanced significantly during the last few years. The incorporation of image processing and artificial intelligence techniques have opened the possibility for such aspects as pattern recognition, thus allowing for a better adaptation of these systems. This study presents a new model of an embedded agent especially designed to be implemented in sensing devices with resource constraints. This new model of an agent is integrated within the PANGEA (Platform for the Automatic Construction of Organiztions of Intelligent Agents) platform, an organizational-based platform, defining a new sensor role in the system and aimed at providing contextual information and interacting with the environment. A case study was developed over the PANGEA platform and designed using different agents and sensors responsible for providing user support at home in the event of incidents or emergencies. The system presented in the case study incorporates agents in Arduino hardware devices with recognition modules and illuminated bands; it also incorporates IP cameras programmed for automatic tracking, which can connect remotely in the event of emergencies. The user wears a bracelet, which contains a simple vibration sensor that can receive notifications about the emergency situation. [ABSTRACT FROM AUTHOR]

Abstract: When working in conjunction with multiagent systems, virtual organizations attempt to simulate the functions and interactions of entities in different environments. Recent studies have addressed the problem of assigning roles to agents that form part of the organization, and incorporating new agents to carry out certain tasks. However, these studies are limited to defining the norms and rules that determine the behavior of the organization. The present study proposes a virtual organization model for egovernment environments to assign resources and minimize the required personnel by forecasting workloads. To this end, a neural network, queuing theory, and CBR are used to obtain an efficient distribution. Queuing theory can establish the number of agents with a specific role that are necessary to maximize profits, while the network distributes roles among agents according to their respective efficiency. The final part of the paper is focused on validating the plan developed inside a case study centered on e-government in order to obtain empirical results. [Copyright &y& Elsevier]

Abstract: This paper presents a planning model integrated within a TB-CBP-BDI real-time intelligent agent that provides special abilities for planning in a predictable time, which makes its use especially appropriate in systems where certain temporal constraints must be satisfied. The proposed TB-CBP-BDI real-time agent is the core of a multi-agent system that manages security issues in industrial environments, where time constraints are a key factor. The proposed planning model facilitates the automatic temporal bounded reorganization of tasks to provide the system with adaptation abilities to the changes that occur in the environment. The planning mechanism focuses on optimizing industrial and manufacturing processes, specifically the tasks performed by the available security entities in these environments. Additionally, several Ambient Intelligence technologies such as QR-CODES, GPS, Wi-Fi and HSDPA are used to develop the intelligent environment that was tested and analyzed in this study. [Copyright &y& Elsevier]

Abstract: The use of architectures based on services and multi-agent systems has become an increasingly important part of the solution set used for the development of distributed systems. Nevertheless, these models pose a variety of problems with regards to security. This article presents the Adaptive Intrusion Detection Multi-agent System (AIDeMaS), a mechanism that has been designed to detect and block malicious SOAP messages within distributed systems built by service based architectures. AIDeMaS has been implemented as part of FUSION@, a multi-agent architecture that facilitates the integration of distributed services and applications to optimize the construction of highly-dynamic multi-agent systems. One of the main features of AIDeMaS is that is employs case-based reasoning mechanisms, which provide it with great learning and adaptation capabilities that can be used for classifying SOAP messages. This research presents a case study that uses the ALZ-MAS system, a multi-agent system built around FUSION@, in order to confirm the effectiveness of AIDeMaS. The preliminary results are presented in this paper. [Copyright &y& Elsevier]

The exchange of CO_{2} between the atmosphere and the ocean surface is a problem that has become increasingly important due to its impact on climatic behavior. Given the large quantity of sources of information available for studying the CO_{2} problem, it is necessary to provide innovative solutions that facilitate the automation of certain tasks and incorporate decision support systems to obtain a better understanding of this phenomenon. This paper presents a multiagent architecture aimed at providing solutions for monitoring the interaction between the atmosphere and the ocean. The ocean surface and the atmosphere exchange carbon dioxide. This process is can be modeled by a multiagent system with advanced learning and adaption capabilities. The proposed multiagent architecture incorporates CBR-agents that integrate novel strategies that both monitor the parameters that affect the interaction, and facilitate the creation of models. The system was tested and this paper presents the results obtained. [ABSTRACT FROM AUTHOR]

Summary: Objective: Recent advances in the field of biomedicine, specifically in the field of genomics, have led to an increase in the information available for conducting expression analysis. Expression analysis is a technique used in transcriptomics, a branch of genomics that deals with the study of messenger ribonucleic acid (mRNA) and the extraction of information contained in the genes. This increase in information is reflected in the exon arrays, which require the use of new techniques in order to extract the information. The purpose of this study is to provide a tool based on a mixture of experts model that allows the analysis of the information contained in the exon arrays, from which automatic classifications for decision support in diagnoses of leukemia patients can be made. The proposed model integrates several cooperative algorithms characterized for their efficiency for data processing, filtering, classification and knowledge extraction. The Cancer Institute of the University of Salamanca is making an effort to develop tools to automate the evaluation of data and to facilitate de analysis of information. This proposal is a step forward in this direction and the first step toward the development of a mixture of experts tool that integrates different cognitive and statistical approaches to deal with the analysis of exon arrays. The mixture of experts model presented within this work provides great capacities for learning and adaptation to the characteristics of the problem in consideration, using novel algorithms in each of the stages of the analysis process that can be easily configured and combined, and provides results that notably improve those provided by the existing methods for exon arrays analysis. Material and methods: The material used consists of data from exon arrays provided by the Cancer Institute that contain samples from leukemia patients. The methodology used consists of a system based on a mixture of experts. Each one of the experts incorporates novel artificial intelligence techniques that improve the process of carrying out various tasks such as pre-processing, filtering, classification and extraction of knowledge. This article will detail the manner in which individual experts are combined so that together they generate a system capable of extracting knowledge, thus permitting patients to be classified in an automatic and efficient manner that is also comprehensible for medical personnel. Results and conclusion: The system has been tested in a real setting and has been used for classifying patients who suffer from different forms of leukemia at various stages. Personnel from the Cancer Institute supervised and participated throughout the testing period. Preliminary results are promising, notably improving the results obtained with previously used tools. The medical staff from the Cancer Institute considers the tools that have been developed to be positive and very useful in a supporting capacity for carrying out their daily tasks. Additionally the mixture of experts supplies a tool for the extraction of necessary information in order to explain the associations that have been made in simple terms. That is, it permits the extraction of knowledge for each classification made and generalized in order to be used in subsequent classifications. This allows for a large amount of learning and adaptation within the proposed system. [Copyright &y& Elsevier]

Microarray technology can measure the expression levels of thousands of genes in an experiment. This fact makes the use of computational methods in cancer research absolutely essential. One of the possible applications is in the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays, but there is a growing need for new and effective solutions. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow filtering, classification, and knowledge extraction. The system has been tested and the results obtained are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent environment developed for monitoring patients’ health care in execution time in hospital environments. The CBPMP (case-based planner for monitoring patients) is an autonomous deliberative case-based planner designed to plan the nurses’ working time dynamically, to maintain the standard working reports about the nurses’ activities, and to guarantee that the patients assigned to the nurses are given the right care. The planner operates in wireless devices and is integrated with complementary software into an intelligent environment, named AmI-P (Ambient Intelligence for patients). CBPMP description, its relationship with the complementary technology, and preliminary results of system prototype in a real environment are presented. [Copyright &y& Elsevier]

Gene selection (or feature selection) from DNA-microarray data can be focused on different techniques, which generally involve statistical tests, data mining and machine learning. In recent years there has been an increasing interest in using hybrid-technique sets to face the problem of meaningful gene selection; nevertheless, this issue remains a challenge. In an effort to address the situation, this paper proposes a novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments. For this purpose, the framework above deals with approaches such as statistical significance tests, cluster analysis, evolutionary computation, visual analytics and boundary points. The latter is the core technique of our proposal, allowing the framework to define two methods of gene selection. Another novelty of this work is the inclusion of the age of patients as an additional factor in our analysis, which can leading to gaining more insight into the disease. In fact, the results reached in this research have been very promising and have shown their biological validity. Hence, our proposal has resulted in a methodology that can be followed in the gene selection process from DNA-microarray data. [ABSTRACT FROM AUTHOR]

Precision breeding techniques have been widely used to optimize expenses and increase livestock yields. Notwithstanding, the joint use of heterogeneous sensors and artificial intelligence techniques for the simultaneous analysis or detection of different problems that cattle may present has not been addressed. This study arises from the necessity to obtain a technological tool that faces this state of the art limitation. As novelty, this work presents a multi-agent architecture based on virtual organizations which allows to deploy a new embedded agent model in computationally limited autonomous sensors, making use of the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA). To validate the proposed platform, different studies have been performed, where parameters specific to each animal are studied, such as physical activity, temperature, estrus cycle state and the moment in which the animal goes into labor. In addition, a set of applications that allow farmers to remotely monitor the livestock have been developed. [ABSTRACT FROM AUTHOR]

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. [ABSTRACT FROM AUTHOR]

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising. [ABSTRACT FROM AUTHOR]

The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network. [ABSTRACT FROM AUTHOR]

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost. [ABSTRACT FROM AUTHOR]

Abstract: The following article proposes the implementation of a system based on neural networks of CounterPropagation type and intelligent agents for analysis and assessment of the risk of flood caused by rain, in addition to the implementation of agents in mobile devices for the presentation of alerts. Because as is known, natural phenomena have always existed, but in recent years as a result of global warming we have seen that floods are becoming more frequent, which has forced the creation of specialized agencies and intelligent mechanisms to prevent the loss of human lives due to these phenomena. [Copyright &y& Elsevier]

Abstract: Business Intelligence has gained relevance during the last years to improve business decision making. However, there is still a growing need of developing innovative tools that can help small to medium sized enterprises to predict risky situations and manage inefficient activities. This article present a multi-agent system especially created to detect risky situations and provide recommendations to the internal auditors of SMEs. The core of the multi-agent system is a type of agent with advanced capacities for reasoning to make predictions based on previous experiences. This agent type is used to implement a evaluator agent specialized in detect risky situations and an advisor agent aimed at providing decision support facilities. Both agents incorporate innovative techniques in the stages of the CBR system. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented. [Copyright &y& Elsevier]

Abstract: During the last years the use of Web Service-based applications has notably increased. However, the security has not evolved proportionally, which makes these applications vulnerable and objective of attacks. One of the most common attacks requiring novel solutions is the denial of service attack (DoS), caused for the modifications introduced in the XML of the SOAP messages. The specifications of existing security standards do not focus on this type of attack. This article presents the S-MAS architecture as a novel adaptive approach for dealing with DoS attacks in Web Service environments, which represents an alternative to the existing centralized solutions. S-MAS proposes a distributed hierarchical multi-agent architecture that implements a classification mechanism in two phases. The main benefits of the approach are the distributed capabilities of the multi-agent systems and the self-adaption ability to the changes that occur in the patterns of attack. A prototype of the architecture was developed and the results obtained are presented in this study. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper. [Copyright &y& Elsevier]

• A distributed model predictive control with fuzzy negotiation has been developed. • The controller has been developed using a multi-agent software platform. • The fuzzy negotiation allows for the inclusion of economic or operating criteria. • The validation has been performed in a simulation of the four coupled tanks system. In this work, a multi-agent distributed model predictive control (DMPC) including fuzzy negotiation has been developed. A novel fuzzy inference system is introduced as a negotiation technique between agents in a cooperative game algorithm, allowing for the consideration of economic criteria and process constraints within the negotiation process, providing an easier interpretation of the available knowledge. The fuzzy negotiation produces smoother control actions than where the negotiation is based only on costs evaluation, because both agents provide their best to generate the final control action. The results show good tracking and disturbance rejection in the case study proposed. The methodology has been implemented in a JAVA based platform with a friendly user interface to deploy the multi-agent system (MAS), and it has been validated in the water level control in a four coupled tanks system. [ABSTRACT FROM AUTHOR]

Abstract Automatic vehicle tracking systems ease the completion of numerous tasks in different fields. Moreover they can automatically capture information, this feature allows to perform location tasks. These systems can be implemented at airports, in shopping centers and in other large buildings; in this way, wireless network scans will serve as a basis for the creation of signal maps that can be used in indoor location systems. This work proposes an automatic people tracking system which also allows to map Wi-Fi networks in order to localize people indoor. In order to operate the system, information on vehicle movement was used to capture signal maps, with the aim of reducing the need to perform manual calibration and thus, improving the updating of information. The final location is determined by combining information provided by wireless networks, Bayesian networks are employed for this task. [ABSTRACT FROM AUTHOR]

The early detection and monitoring of kidney disease continues being an important problem in medicine. The diagnosis and treatment of patients with this disease usually require expensive medical equipment that is difficult to install. Patients or medical centers may not always be able to afford such equipment. This work proposes the creation of a wireless sensor network for medical environments; it will assist medical professionals in the diagnosis and monitoring of patients with renal symptomatology. This work will focus on the analysis of symptoms that accompany this disease and the design of a system which will help determine types of kidney diseases. The proposed system will incorporate new hardware mechanisms and an intelligent system. It will be designed through a multi-agent architecture based on virtual organizations. This architecture will include a new model of agents, specifically designed to be incorporated into computationally limited devices. This hardware will be characterized by its low cost and ease of use. A case study has been carried out in order to validate the proposed architecture. In order to validate the proposed architecture, we designed a case study that aims to provide a technological tool for medical professionals and makes it possible to determine any diseases related to diuresis. The initial results are promising. [ABSTRACT FROM AUTHOR]

Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet performance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our approach. We carried out some experiments, which showed our proposed algorithm outperform the most common assignment method both in waiting times of clients and taxi costs. [ABSTRACT FROM AUTHOR]

In our day to day life, the environmental conditions, and especially the temperature and humidity of the air that surrounds us, go unnoticed. However, in many cases, these parameters play an important role in the use of materials since they modify their electrical properties. It is necessary to predict what this behaviour will be as these environmental conditions can introduce or improve desirable properties in the material, especially of textiles. The nature of these is to be dielectric, and therefore have a minimal DC electrical conductivity that is currently impossible to measure directly, so a methodology has been proposed to obtain the DC electrical resistivity through the method of discharging a condenser. For this purpose, a system was developed based on a static voltmeter, a climatic chamber and a control and data capture units. In order to validate the proposed system and methodology a study using both is described in this work. The study made it possible to verify that the most influential factor in establishing the values of the electrical parameters of a textile material is the nature of the fibres of which it is composed, although the influence of environmental conditions in fibres is also significant. [ABSTRACT FROM AUTHOR]

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context. [ABSTRACT FROM AUTHOR]

The European Union Establishes that for the next few years, a cleaner and more efficient energy system should be used. In order to achieve this, this work proposes an energy optimization method that facilitates the achievement of these objectives. Existing technologies allow us to create a system that optimizes the use of energy in homes and offers some type of benefit to its residents. Specifically, this study has developed a recommendation system based on a multiagent system that allows to obtain consumption data from electronic devices in a home, obtain information on electricity prices from the Internet, and provide recommendations based on consumption patterns of users and electricity prices. In this way, the system recommends new hours in which to use the appliances, offering the economic benefit that it would propose recommendations for the user. In this way, it is possible to distribute and optimize the use of energy in homes and reduce the peaks in electricity consumption. The system provides encouraging results in order to resolve the problem proposed by the European Union by optimizing the use of energy among different hours of the day and saving money for the customer. [ABSTRACT FROM AUTHOR]

People who suffer from any kind of motor difficulty face serious complications to autonomously move in their daily lives. However, a growing number research projects which propose different powered wheelchairs control systems are arising. Despite of the interest of the research community in the area, there is no platform that allows an easy integration of various control methods that make use of heterogeneous sensors and computationally demanding algorithms. In this work, an architecture based on virtual organizations of agents is proposed that makes use of a flexible and scalable communication protocol that allows the deployment of embedded agents in computationally limited devices. In order to validate the proper functioning of the proposed system, it has been integrated into a conventional wheelchair and a set of alternative control interfaces have been developed and deployed, including a portable electroencephalography system, a voice interface or as specifically designed smartphone application. A set of tests were conducted to test both the platform adequacy and the accuracy and ease of use of the proposed control systems yielding positive results that can be useful in further wheelchair interfaces design and implementation. [ABSTRACT FROM AUTHOR]