12th International Conference on Sustainable Energy Information Technology (SEIT 2022)

A way to reduce carbon emissions in cities is through movement by bicycle or on foot. However, it sometimes means to pass through high-pollution zones and consequently breath low quality air. We then propose a green Intelligent Transportation System (ITS) for zero-emission mobility users, providing users with low-pollution routes to avoid the high-pollution zones. This proposal uses ITS to promote the use of alternative transportation to classical motor vehicles to reduce carbon emissions. This is based on Complex Event Processing (CEP) technology to gather and process real-time data, a Decision Support System designed as a Fuzzy Inference System (FIS) to make decisions about recommended transit zones, taking also into account the user experience level and specific weather data, and Colored Petri Nets (CPN) as a tool to compute the routes. This is therefore an all-in-one solution to provide green routes, with the benefits of each one of the technologies used

Digital Avatars for Older People’s Care

Es el preprint de: Bertoa M.F., Moreno N., Perez-Vereda A., Bandera D., Álvarez-Palomo J.M., Canal C. (2020) Digital Avatars for Older People’s Care. In: García-Alonso J., Fonseca C. (eds) Gerontechnology. IWoG 2019. Communications in Computer and Information Science, vol 1185. Springer, Cham. doi:10.1007/978-3-030-41494-8_6.The continuous increase in life expectancy poses a challenge for health systems in modern societies, especially with respect to older people living in rural low-populated areas, both in terms of isolation and difficulty to access and communicate with health services. In this paper, we address these issues by applying the Digital Avatars framework to Gerontechnology. Building on our previous work on mobile and social computing, in particular the People as a Service model, Digital Avatars make intensive use of the capabilities of current smartphones to collect information about their owners, and applies techniques of Complex Event Processing extended with uncertainty for inferring the habits and preferences of the user of the phone and building with them a virtual profile. These virtual profiles allow to monitor the well-being and quality of life of older adults, reminding pharmacological treatments and home health testings, and raising alerts when an anomalous situation is detected.This work has been funded by the Spanish Government under grant PGC2018-094905-B-100

Book of abstracts of the ICIEOM-CIO-IIIE International Conference 2015

BOOK OF ABSTRACTS OF THE ICIEOM-CIO-IIIE INTERNATIONAL CONFERENCE 2015: ENGINEERING SYSTEMS AND NETWORKS: The way ahead for industrial engineering and operations managemen

A Hybrid Context-aware Middleware for Relevant Information Delivery in Multi-Role and Multi-User Monitoring Systems: An Application to the Building Management Domain

Recent advances in information and communications technology (ICT) have greatly extended capabilities and functionalities of control and monitoring systems including Building Management Systems (BMS). Specifically, it is now possible to integrate diverse set of devices and information systems providing heterogeneous data. This data, in turn, is now available on the higher levels of the system architectures, providing more information on the matter at hand and enabling principal possibility of better-informed decisions. Furthermore, the diversity and availability of information have made control and monitoring systems more attractive to new user groups, who now have the opportunity to find needed information, which was not available before. Thus, modern control and monitoring systems are well-equipped, multi-functional systems, which incorporate great number and variety of data sources and are used by multiple users with their special tasks and information needs.In theory, the diversity and availability of new data should lead to more informed users and better decisions. In practice, it overwhelms user capacities to perceive all available information and leads to the situations, where important data is hindered and lost, therefore complicating understanding of the ongoing status. Thus, there is a need in development of new solutions, which would reduce the unnecessary information burden to the users of the system, while keeping them well informed with respect to their personal needs and responsibilities.This dissertation proposes the middleware for relevant information delivery in multi-role and multi-user BMS, which is capable of analysing ongoing situations in the environment and delivering information personalized to specific user needs. The middleware implementation is based on a novel hybrid approach, which involve semantic modelling of the contextual information and fusion of this information with runtime device data by means of Complex Event Processing (CEP). The context model is actively used at the configuration stages of the middleware, which enables flexible redirection of information flows, simplified (re)configuration of the solution, and consideration of additional information at the runtime phases. The CEP utilizes contextual information and enables temporal reasoning support in combination with runtime analysis capabilities, thus processing ongoing data from devices and delivering personalized information flows. In addition, the work proposes classification and combination principles of ongoing system notifications, which further specialize information flows in accordance to user needs and environment status.The middleware and corresponding principles (e.g. knowledge modelling, classification and combination of ongoing notifications) have been designed contemplating the building management (BM) domain. A set of experiments on real data from rehabilitation facility has been carried out demonstrating applicability of the approach with respect to delivered information and performance considerations. It is expected that with minor modifications the approach has the potential of being adopted for control and monitoring systems of discrete manufacturing domain

Rule-based preprocessing for data stream mining using complex event processing

Data preprocessing is known to be essential to produce accurate data from which mining methods are able to extract valuable knowledge. When data constantly arrives from one or more sources, preprocessing techniques need to be adapted to efficiently handle these data streams. To help domain experts to define and execute preprocessing tasks for data streams, this paper proposes the use of active rule-based systems and, more specifically, complex event processing (CEP) languages and engines. The main contribution of our approach is the formulation of preprocessing procedures as event detection rules, expressed in an SQL-like language, that provide domain experts a simple way to manipulate temporal data. This idea is materialized into a publicly available solution that integrates a CEP engine with a library for online data mining. To evaluate our approach, we present three practical scenarios in which CEP rules preprocess data streams with the aim of adding temporal information, transforming features and handling missing values. Experiments show how CEP rules provide an effective language to express preprocessing tasks in a modular and high-level manner, without significant time and memory overheads. The resulting data streams do not only help improving the predictive accuracy of classification algorithms, but also allow reducing the complexity of the decision models and the time needed for learning in some cases

Sustainable Industrial Engineering along Product-Service Life Cycle/Supply Chain

Sustainable industrial engineering addresses the sustainability issue from economic, environmental, and social points of view. Its application fields are the whole value chain and lifecycle of products/services, from the development to the end-of-life stages. This book aims to address many of the challenges faced by industrial organizations and supply chains to become more sustainable through reinventing their processes and practices, by continuously incorporating sustainability guidelines and practices in their decisions, such as circular economy, collaboration with suppliers and customers, using information technologies and systems, tracking their products’ life-cycle, using optimization methods to reduce resource use, and to apply new management paradigms to help mitigate many of the wastes that exist across organizations and supply chains. This book will be of interest to the fast-growing body of academics studying and researching sustainability, as well as to industry managers involved in sustainability management

Optimal planning of electrical appliance of residential units in a smart home network using cloud services

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method

New Approaches in Social, Environmental Management and Policy to Address SDGs

The book comprises a selection of papers addressing some of the most relevant challenges and opportunities for addressing SDGs from many different perspectives. Papers in this collection cover the most recent lines and approaches of research in addressing SDGs and are all novel propositions that deepen the analysis of environmental, social and governance strategies in the adaptation of the society to meet the 17 SDGs