Ebook: Intelligent Environments 2019

Intelligent Environments (IEs) aim at empowering users by enriching their experience, raising their awareness and enhancing their management of such environments. IEs refer to the physical spaces where ICT and pervasive technologies are used to achieve specific objectives for the user and/or the environment. IEs’ growing community, from academia to practitioners, is working on the materialization of IEs driven by the latest technological developments and innovative ideas.

The 15th International Conference on Intelligent Environments focuses on the development of advanced intelligent environments, as well as newly emerging and rapidly evolving topics. In the present edition, we are pleased to include the proceedings of the following workshops, which emphasize multi-disciplinary and transversal aspects of IEs, as well as cutting-edge topics:

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   8th International Workshop on the Reliability of Intelligent Environments (WORIE’19)

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   9th International Workshop on Intelligent Environments Supporting Healthcare and Well-being (WISHWell’19)

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   5th Symposium on Future Intelligent Educational Environments and Learning (SOFIEE’19)

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   3rd International Workshop on Intelligent Systems for Agriculture Production and Environment Protection (ISAPEP’19)

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   3rd International Workshop on Legal Issues in Intelligent Environments (LIIE’19)

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   1st International Workshop on Intelligent Environments and Buildings (IEB’19)

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   3rd International Workshop on Citizen-Centric Smart Cities Services (CCSCS’19)

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   4th International Workshop on Smart Sensing Systems (IWSSS’19)

Well-known experts in the IEs field gave a number of advanced tutorials to introduce IEs to PhD students, early-career researchers and people developing an interest in the subject. The following list of tutorials, which were organized in conjunction with the IE’19 conference, provided a venue for different IEs stakeholders to engage in discussions that will strengthen future collaboration and engage in further research in IEs’ key areas:

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   The “Automation of Everything” Era

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   Wireless Communications with Unmanned Aerial Vehicles (UAVS): From Wi-Fi to 5G

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   Fundamentals of Indoor Location for Smart Environments Applications

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   Smart Environments ARCHitecture (SEARCH)

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   Hands-On Process Mining for Smart Environments

The proceedings contain a series of contributions reflecting the latest research developments in IEs and related areas, focusing on stretching the borders of the current state of the art and contributing to an ever increasing establishment of IEs in the real world. We would like to thank all the contributing authors, as well as the members of the Organizing Committees and Program Committees of the workshops for their highly valuable work, which contributed to the success of the Intelligent Environments 2019 Event.

The Workshops and Tutorial Chairs would like to take the opportunity to thank Professor Mounir Ghogho and Professor Sofie Pollin, the general chairs of IE’19, and Professor Francisco Javier Falcone and Professor Subhas Mukhopadhyay, the program chairs of IE’19, for their trust in our work, and all the other members of the IE’19 organization for the confidence they placed on us. We are also grateful to the local staff who worked thoroughly for the success of this event. Thank you for your esteemed help, without which this event would not have been possible.

June 2019

Andrés Muñoz, Catholic University of Murcia, Spain

Sofia Ouhbi, United Arab Emirates University, United Arab Emirates

Workshop Chairs of IE’19

Wolfgang Minker, University of Ulm, Germany

Loubna Echabbi, Institut National des Postes et Télécommunications, Morocco

Miguel Navarro-Cia, University of Birmingham, United Kingdom

Tutorial Chairs of IE’19

Indoor location is a topic that has attracted lot attention in the past two decades. This is mainly due to the success of outdoor location, typically GPS, and the provided services based on this technology. The goal of indoor location is to provide similar services that can be found in outdoor location where GPS signal can not penetrate (buildings such as airports, malls, museums and homes). This tutorial presents the different technologies available for indoor location, the algorithms used, example applications and future research lines. Special attention will be devoted to location systems based on Wi-Fi fingerprinting.

A software system managing a smart space takes, among its inputs, models of human behavior; such models are usually difficult to obtain and to validate. The employment of techniques from business process modeling and mining may represent a solution to both the problems, but a set of challenges need to be faced in order to cope with major differences between human activities and business processes. As a first step, this tutorial introduces basic concepts of business process management (BPM) and process mining. With these concepts in mind, the tutorial explains attendees how to apply process mining to sensor logs acquired from smart spaces.

Our research group (http://ie.cs.mdx.ac.uk/) has been developing an integrated system architecture combining different modules which are all interconnected and deal with complex cases of house services automation, behaviour recognition, user advice and emergencies (http://ie.cs.mdx.ac.uk/smart-spaces-lab/). The aim of the tutorial will be to explain how it works and how it may be used by other teams. The main sections to be explained and illustrated are: the basic system infrastructure (sensors, actuators, control) used to build the system bottom up, the overall architecture concept connecting various relevant areas, the reasoning module, the learning module, preferences and conflict handling. All these concepts will be illustrated through applications and case studies arising from our research projects (http://ie.cs.mdx.ac.uk/projects/). A first system description in a short paper is in press [1].

We are at the beginning of an era of profound transformation and human progress — a new industrial revolution. This “Automation of Everything” era will be brought about by digital interfaces, data analysis and control of the physical world through “smart” networks. These networks will support the digitalization and connection of everything and everyone with the goal of automating much of life.

This major industrial revolution promises to unlock trillions of dollars of economic value in the next decade by driving massive improvements in productivity in physical and digital industries alike, enhancing quality of life in safer, healthier and more sustainable communities.

The growing use of Unmanned Aerial Vehicles (UAVs) for various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground terminals. Depending on the application, UAV-mounted wireless equipment can either be an aerial user equipment (AUE) that co-exists with the terrestrial users, or it can be a part of wireless infrastructure providing a range of services to the ground users. For instance, AUE can be used for real-time search and rescue and Aerial Base Station (ABS) can enhance coverage, capacity, and energy efficiency of wireless networks. We will start with discussing the open challenges of communication with UAVs. To give answers to the posed questions, we will focus on the UAV communication basics, providing the channel modeling background and giving guidelines on how various channel models should be used. Next, theoretical, simulation- and measurement-based approaches to address the key challenges for AUE usage will be presented. Moreover, we will provide a comprehensive overview on how UAV-mounted equipment (e.g. ABS) can be used as a part of the communication network. Based on the theoretical analysis, we will show how various network parameters (for example coverage area of ABSs, power efficiency, or user localization error) can be optimized. Finally, we will discuss how to ensure the safe use of UAVs via various RF-based techniques for detecting the presence of UAVs in the airspace (including Machine Learning and Passive Coherent Location techniques).

Recent studies reveal that there are different methodologies for developing Intelligent Environments. Thus, it has become essential to scrutinize and evaluate the methodologies to increase our understanding of their strengths, weaknesses and features. However, these concerns have not been the target of recent research efforts. This paper presents an evaluation framework for qualitative evaluation of Intelligent Environment methodologies. It is a step towards standardization of current Intelligent Environments methodologies. The framework has been defined through studying, abstracting and unifying best practices from systems engineering. It is based on a generic life cycle model. As an initial validation, we evaluated the User Centred Intelligent Environment Development Process against the proposed framework. We note that this methodology at its current state presents some limitations which will be addressed in future works.

The importance of sleep in people’s lives and concern about their quality have both been gaining an increasing relevance and awareness. Nowadays, the number of people who use mobile and wearable devices to monitor their sleep and day activity is quite revealing. But while some of those recent devices may even provide reliable measurements of some of the sleep parameters and sleep structure itself, they do not provide yet a justification for what has led to that situation and to those values. With the present study, we intend to verify and establish relationships between some environmental factors, such as temperature, humidity, luminosity, noise or air quality and between sleep performance and activity during the day. Several time series machine learning models were used to predict sleep stages and to estimate the level of day activity of a person.

The adequacy of interbank capital is affected by many factors, including the tightness of the entire financial market and the ups-and-downs of interest rates. It has great significance for commercial banks and other non-bank institutions to diagnose the disturbance factors of interbank capital, and predict future capital adequacy level in advance to deploy appropriate countermeasures accordingly. This paper attempts to analyze the relevant factors affecting the interbank capital and to predict the adequacy level of interbank capital based on structured and unstructured financial data. For unstructured data, we crawl the texts from Sina Financial News and then make pre-processing, including word segmentation, emotional word extraction and word-to-vector transformation. For structured data the preprocessing includes padding missing value, data normalization, feature selection and data dimensionality reduction. The prediction models we tried include GBDT, XGBoost, LSTM, SVM, and Perceptron. Experiments show that two-category (loose and tight) average accuracy of the overall adequacy level of interbank capital can achieve more than 94.5%.

Disability itself is deeply associated with mobility conditioning and isolation. The combination of the two conditions rises a chain reaction of exclusion that affects a significant number of women in a lot of European countries, with a particular focus on elderly and disabled people. Therefore, inclusive and accessible tourism need to be integrated into the future vision for tourism, especially in the context of Smart Cities. To this end, in this short paper, we report our ongoing work for enabling decision making for accessible and inclusive tourism by using open data and relevant information as a way of improving social and leisure experiences for people with disabilities.

As a major entity of smart cities, smart buildings are intelligent environments equipped with computing technologies to improve inhabitants daily life. Such systems are complex and have to take account several requirements of the targeted environment and users needs for design and implementation. The semantic aspect is important to take into account to model such systems. However, research about smart buildings development remains limited. In this paper, we define a smart building system architecture. Then, we present our methodology for smart building design and implementation and we conduct a case study to demonstrate our proposal with a validation and verification phase on systems by model transformations.

Electricity demand forecasting is becoming an essential tool for energy management, maintenance scheduling and investment planning. In small and isolated electric systems like in many islands, the power system is usually not extensively interconnected with enough number of electric generators and loads, hence it provokes an electric load shedding or forces the electric utility to take control actions such as temporary power outage. Despite this trouble, isolated territories are fortunate to have the possibility of estimating the population pressure in a very accurate manner, even at a daily level. This is possible thanks to the fact that entries to these territories are limited to ports and airports which in turn facilitate keeping the record of in/out flows to/from the territory. Investigating this problem, using the most classical and standard prediction techniques applied to the case of Balearic Islands (Spain) authors demonstrate how daily arrivals and people stocks improve accuracy of forecast.

The RANS model takes into account all modes of urban heat transfer at higher spatial and time resolution. It is based on dynamic coupling between heat balance and k-epsilon (turbulence) modelling. The effect of complex urban spatial data is considered. The model assesses environmental information such as micro-climates, building energy and thermal comfort at neighbourhood and city scales. This paper presents details of the model with some tests against measured data along with static versus dynamic coupling and some results in real urban settings.

Energy savings in the building construction phase focus mainly on embedded CO2 reduction, but it is necessary to identify specific solutions and apply measures to reduce energy consumption during the entire lifespan of the building. Particularly in the Mediterranean area, given the high number of sunlight hours and the high solar radiation levels, a decisive factor to maximize energy savings is to reduce the building solar energy uptake and analyse the use of materials of low thermal conductivity to improve heat isolating capability. Two techniques are analysed in this work: the use of cold paints to reduce surface temperatures and, the use of lime mortar, which has some advantageous and peculiar properties. Both techniques have some advantages in terms of energy savings, in the Mediterranean area to construct more efficient buildings.

Hotels, in which belong to the tertiary building sector, rank among the highest in the energy consumption sectors. The Balearic Islands are a representative example of tourist islands, with more than 2.500 Hotel and touristic industries, which implies with its approximately a half million available beds, an increase in 50% of the population at high season. High energy consumption in hours with low Renewable energy production are additional problems in this case. From the information of 250 Hotels were studied in order to investigate the levels of energy consumption and sustainability among them with future zero emissions scenarios. This paper analyses available information concerning energy consumption in hotels, with a focus on Electric demand (most of them from HVAC systems). The aim is to determine a model for predict the energy consumption, and coupling it as much as possible, to the RES of energy demand. For arrive to this goal smart buildings can foster energy efficiency and tackle the reduction of CO2 emissions within the atmosphere. The Hotels can guarantee good lighting, ventilation, cooling rates, along with other aspects of building demands in real-time. Monitoring and sensing combined solutions with simple and efficient methods are the protagonists of advanced sustainable building technology, for instance, the implementation of smart strategies such as thermal storage or pumping systems.

Water is an essential material for the operation of factories. It is threatened by difficult circumstances and causes the disruption of different ecosystems especially in aquatic environments. The main problems characterizing water situation in Morocco today are linked to shortage’s aggravation through over-exploitation, and on the other hand to the degradation of the quality of water resources such as a lack of wastewater treatment. However, the depollution of these discharges requires several steps starting with the identification of different pollutants until their treatments. As a result, we carried out this first physicochemical characterization of the effluents of 5 industrial units along with the final collector at the exit of the industrial zone of Settat, and we discussed pollution load generated by each type of industry. The results show that these discharges were heavily loaded with organic matter and with high conductivity, compared with tolerances based on national and international standards for activities-related releases. Our approach aims to set up new methods on smart water management in Morocco based on techniques like regular monitoring and automatic sampling.

The International Center for Agricultural Research in the Dry Areas (ICARDA) has a unique germplasm collection of barley, among many other crops that it holds in its genebank. This collection contains landraces and barley wild relatives and most of them are georeferenced. Distribution of genetic resources is a core genebank activity aiming at responding to requests from various users including breeders, researchers, farmers, etc. ICARDA has developed over the last decade an efficient approach for better targeting adaptive traits called the Focused Identification of Germplasm Strategy (FIGS). FIGS approach links adaptive traits to environments (and associated selection pressures) through filtering and machine learning and it focuses on accessions that are most likely to possess trait specific genetic variation. In this paper, we present a work of predictive characterization on ICARDA barley collection using the FIGS approach and its algorithms combining several machine learning methods, and using several characterization traits. Most of the studied traits have shown a high predictability. Outcomes from this analysis are then used to make a predictive characterization of the entire ICARDA barley collection by assigning probabilities of each trait to the non-evaluated accessions.

Precision agriculture adopts a set of techniques capable of increasing productivity, yield and efficiency in work related to agriculture, producing a greater benefit for farmers. In this study we focus on the problem of predicting weather conditions, specifically the prediction of low temperatures. Temperature prediction is a major problem in agriculture. Farmers can lose their crops if frost control techniques are not activated in time. The threshold for activating such techniques depends on the type of crop. A first preliminary study using deep learning is proposed to predict temperature, particularly a Long Short-Term Memory Network (LSTM) is used. The LSTM has been trained using real temperature data provided by an the Internet of Things (IoT) system, deployed in several plots and currently in operation. The results obtained after testing the model created with this neural network are quite satisfactory obtaining a determination coefficient (R²) of 99% and an average quadratic error of less than 0.8 degrees Celsius. Given the goodness of the model this can be implemented as an intelligent component of the IoT system, thus complementing its functionality.