Sensorization and intelligent systems in energetic sustainable environments

Sustainability is an important topic of discussion in our world. However, measuring sustainability and assessing behaviors is not always easy. Indeed, and in order to fulfill this goal, in this work it will be proposed a multi-agent based architecture to measure and assess sustainable indicators taken from a given environment. These evaluations will be based on past and present behaviors of the users and the particularities of the setting, leading to the evaluation of workable indicators such as gas emissions, energetic consumption and the users fitting with respect to the milieu. Special attention is given to user interaction and user attributes to calculate sustainable indicators for each type of structure, i.e., the aim of this scheme is to promote sustainability awareness and sustainable actions through the use of sustainable markers calculated in terms of the information gathered from the environment.The research presented is partially supported by a portuguese doctoral grant, SFRH/BD/78713/2011, issued by the Fundação da Ciência e Tecnologia (FCT) in Portugal

Gamification, social networks and sustainable environments

Intelligent environments and ambient intelligence enabled systems provide means to gather rich information from both environments and its users. With the help of such systems, it is possible to foster communities of ambient intelligence systems with community driven knowledge, which is created by individual actions and setups in each of the environments. Such arrangements provides the potential to build systems that promote better practices and more efficient and sustainable environments by promoting the community best examples and engaging users to adopt and develop proactive behaviors to improve their standings in the community. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from different environments, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process. Gamification concepts are used in order to keep users motivation and engage them actively to produce better results in terms of sustainability

Gamification, Social Networks and Sustainable Environments

Intelligent environments and ambient intelligence enabled systems provide means to gather rich information from both environments and its users. With the help of such systems, it is possible to foster communities of ambient intelligence systems with community driven knowledge, which is created by individual actions and setups in each of the environments. Such arrangements provides the potential to build systems that promote better practices and more efficient and sustainable environments by promoting the community best examples and engaging users to adopt and develop proactive behaviors to improve their standings in the community. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from different environments, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process. Gamification concepts are used in order to keep users motivation and engage them actively to produce better results in terms of sustainabilit

Social networks gamification for sustainability recommendation systems

10th International Symposium on Distributed Computing and Artificial Intelligence (DCAI), Spain, Salamanca, 22 - 24 May 2013Intelligent environments and ambient intelligence provide means to monitor physical environments and to learn from users, generating data that can be used to promote sustainability. With communities of intelligent environments, it is possible to obtain information about environment and user behaviors which can be computed and ranked. Such rankings are bound to be dynamic as users and environments exchange interactions on a daily basis. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from each environment, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process

Ambient sensorization for the furtherance of sustainability

Energy efficiency is regarded as an important objective in a world of limited resources. The sustainable use of energy is necessary for the continuity of life styles that do not jeopardize the future. Nevertheless, due to poor information about the impact of human actions on the environment, it is hard to promote and warn for sustainability. This work focuses on the use of ambient intelligence as a mean to constantly revise sustainability indicators in a way they may be used for user awareness and recommendation systems within communities. The approach in this research makes use of sustainable indicators monitored through ambient sensors which enable user accountability concerning their actions inside each environment. Also, it is possible to compare the effect of user actions in the environment, enabling decision making based on such comparison factors.(undefined

Information fusion for context awareness in intelligent environments

The development of intelligent environments requires handling of data perceived from users, received from environments and gathered from objects. Such data is often used to implement machine learning tasks in order to predict actions or to anticipate needs and wills, as well as to provide additional context in applications. Thus, it is often needed to perform operations upon collected data, such as pre-processing, information fusion of sensor data, and manage models from machine learning. These machine learning models may have impact on the performance of platforms and systems used to obtain intelligent environments. In this paper, it is addressed the issue of the development of middleware for intelligent systems, using techniques from information fusion and machine learning that provide context awareness and reduce the impact of information acquisition on both storage and energy efficiency. This discussion is presented in the context of PHESS, a project to ensure energetic sustainability, based on intelligent agents and multi-agent systems, where these techniques are applied

Ambient intelligence and affective computing: a contribute to energetic sustainability

Tese de Doutoramento em Informática.economy and the citizen behaviours are putting stress on resources at increasing scales. Society demands sustainable solutions for these problems. However, these solutions need to compromise restrictions enforced by either society, physics and resources. This leads to the traditional dimensions of sustainability: economic, environmental and social, which need to be addressed as a whole in order to find sustainable configurations. Although not as old as sustainability itself, computational sustainability provides methods to specify and intervene in sustainability problems. The most used approaches to computational sustainability systems target constraint conditions, computer simulation and machine learning to solve sustainability problems. Computer science can leverage computational sustainability to acquire relevant information from environment and users, plan and predict approaches to problems and act upon physical systems. This thesis presents an archetype platform, the People Help Energy Savings and Sustainability (PHESS), which results from experiments upon computational sustainability problems with the aid of action-research methodology. It is aimed at intelligent environments such as smart cites and ambient assisted living, and makes use of ubiquitous technologies, such as the Internet of Things (IoT) and pervasive computing. More than just measuring and reporting tool, the archetype aims to promote behavioural change and continuous improvement through techniques taken from fields such as intelligent environments, gamification and affective computing which help improve sustainability scenarios. This archetype enabled the implementation of case studies where the platform was used to assess energy consumption to manage and monitor user environments, user comfort and urban transportation to demonstrate the adaptability of the archetype to different kinds of scenarios.A sociedade depara-se, muitas vezes, com problemas de sustentabilidade. É um facto que a evolução económica e os comportamentos dos cidadãos estão a colocar pressão sobre os recursos naturais numa escala cada vez maior. A sociedade exige soluções sustentáveis para estes problemas. No entanto, estas soluções devem harmonizar restrições impostas pela sociedade, a física e os recursos. Estes fatores conduzem às dimensões tradicionais da sustentabilidade: económica, ambiental e social, que precisam ser tratadas como um todo, com o intuito de encontrar configurações sustentáveis. Embora não tão antiga quanto a própria sustentabilidade, a sustentabilidade computacional fornece métodos para especificar e intervir nos problemas de sustentabilidade. As abordagens mais usadas para sistemas computacionais de sustentabilidade abordam restrição de condições, simulação por computador e aprendizagem máquina para resolver problemas de sustentabilidade. A ciência da computação pode melhorar o desempenho da sustentabilidade computacional através da criação de informação relevante a partir do ambiente e seus utilizadores, planear e prever abordagens para os problemas e agir sobre sistemas físicos. Esta tese de doutoramento apresenta um arquétipo, o Pessoas Ajudam na Economia de Energia e na Sustentabilidade (PHESS People Help Energy Savings and Sustainability), que é o resultado de experiências sobre problemas de sustentabilidade computacional com o aUXIlio da metodologia de action-research. É destinada a ambientes inteligentes, como por exemplo cidades inteligentes e ambientes de vida assistida e faz uso de tecnologias ubíquas, tais como a Internet das Coisas (IoT - Internet of Things) e computação pervasiva. Mais do que apenas medir e elaborar relatórios, o arquétipo tem como objetivo promover a mudança de comportamentos e a melhoria contínua através de técnicas de ramos como ambientes inteligentes, gamification e computação afetiva que ajudam a melhorar cenários de sustentabilidade. Este arquétipo possibilitou a implementação de diversos casos de estudo onde a plataforma foi usada para gerir e monitorizar ambientes e utilizadores, o conforto dos utilizadores e transportes urbanos, para demonstrar a capacidade de adaptação do arquétipo a diferentes cenários reais

Study of an onboard wired-wireless health monitoring system equipped with power save algorithm for freight railway wagons

Goods transport is an essential factor for the European market for its significant contribution to economic growth and thus to the creation of new employment. Nowadays, approximately 75% of goods are transported by road within the European Union. The use of more efficient and sustainable modes of transportation, such as rail transport and inland waterways, would reduce oil imports and pollution abatement. The growth of rail goods transport must be accompanied by an increasing introduction of tools and technologies that make possible to constantly monitor the European rolling stock. The introduction of monitoring technologies that allow to constantly know the status of the wagon would bring real and concrete benefits to the world of rail transport enabling to optimize the maintenance of rolling stock thus reducing costs but ensuring at the same time a maximization of the safety. Currently, the only information available are provided by the equipment installed along the railway network, separated by tens of kilometers. However, to identify and intervene on an incipient failure, it is necessary to have continuous monitoring and a communication system that can warn the train conductor and the maintenance staff of wagon’s owners. A good monitoring system has to be: cheap, energy autonomous, wireless and reliable. Currently monitoring systems can be divided into two large groups. The former are those developed by universities or research centres within projects financed by third parties, while the latter are monitoring systems developed individually by companies operating in the logistics sector. In light of the existing research projects and products already available on the market, the following thesis work aims to develop a monitoring system demonstrator dedicated to freight wagons that can demonstrate the effectiveness of these devices. The results of preliminary literature and market analyses served as the base for the realization of a first wired demonstrator. All the subsystems of the first demonstrator were long tested in laboratory in order to guarantee the maximum reliability of the device and maximum repeatability of the recorded data. The parameters monitored were the pressures of the pneumatic braking system, the temperature of the cast-iron brake blocks and the dynamics of the body frame. The second demonstrator developed was significantly more complex. In fact, it consists of two wireless units: a base station which represents the further development of the first demonstrator and a completely new axle box node monitoring system. From the analysis of the brake block temperature data two fundamental aspects emerge. The first is the need and importance of maintaining the braking system always in good conditions, doing maintenance in line with the regulations. The second is related to the adoption of new brake blocks in synthetic material. In fact, in addition to the complete review of the brake system as prescribed by the regulation, also the material of the wheelsets must be suitable for the use of new type of brake blocks. Another aspect subject to monitoring in this work is the vibration monitoring. Vibrations of particular interest for freight wagon monitoring are those along the vertical axis and the longitudinal axis. The accelerations along the vertical axis in fact describe the stability of the vehicle and its interaction with the rails. Vertical acceleration is a parameter that allows to determine if the wagon is traveling safely or not. In fact, this parameter makes it possible to identify a possible derailment, if the acceleration level recorded is anomalous. The longitudinal acceleration is a parameter monitored by all the railway monitoring devices present on the market. It is important to know the longitudinal accelerometric levels both in the phases of train composition and during the braking operations in order to identify possible incorrect behaviour. The second demonstrator allowed to monitor the external temperature of the axle box cover and verified the correct behaviour of bearings. The most important result of the second demonstrator was the creation of a wireless network that makes it possible to monitor any quantities without invasive wiring. The creation of a wireless network has also required the development of power saving algorithms for the reduction of energy consumption in order to obtain the maximum operating time. In both prototypes developed, the monitored parameters were very numerous and were sampled with a very high frequency, especially those related to temperatures and pressures. This is a typical feature of the demonstrators. Instead, in order to monitor and study the phenomena related to the dynamics of the wagon it is necessary a sampling frequency as the one adopted. The developed prototypes, even if marked by a strong manual activity, have shown a very high reliability. Monitoring all these parameters for such a long distance led to the creation of a large database. Generally, only large industrial groups can boast such prolonged tests. The prototypes made, thanks to their hardware and software effectiveness, were the basis for the most complex monitoring system that we have set ourselves to achieve with the SWAM Rail project. In conclusion, the project carried out in these three years has therefore obtained as results the realization of demonstrators of monitoring devices, the collection of data that would allow to understand and study the operation of a wagon in optimal maintenance conditions, the development of thermal models and the identification of threshold parameters for delimiting conditions of normal operation by fault conditions

Smart Environments Design on Industrial Automated Greenhouses

Greenhouse automation carried out with monitoring and control technologies optimizes the cultivation processes in industrial scenarios. In recent years, new trends and technologies have emerged in the agricultural sector. The application of Information and Communication Technologies has clear benefits. Embedded hardware systems development, new communication protocols over the Internet and applied Artificial Intelligence paradigms have increased the services’ capabilities. These technologies can be installed both in new facilities and in facilities that are already functioning. This paper analyses the integration of these paradigms into automated greenhouses. An integration model is proposed and developed in the plant experimental unit installed at the University of Alicante. This unit already has an automated system that controls air conditioning, soil conditions, and irrigation, but these control subsystems are not integrated. In this work, new processing nodes with integrated data are designed to develop new detection, prediction and optimization services. These services increase the performance of the installation and create smart environments in agricultural production.This research was funded by the Industrial Computers and Computer Networks program (Informatica Industrial y redes de Computadores I2RC) (2018/2019) funded by the University of Alicante, Wak9 Holding BV company under the eo-TICC project, and the Valencian Innovation Agency under scientific innovation unit (UCIE Ars Innovatio) of the University of Alicante at https://web.ua.es/es/ars-innovatio/unidad-cientifica-deinnovacion-ars-innovatio.html