New intelligent network approach for monitoring physiological parameters : the case of Benin

Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients’ hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems’ characteristics and the patient physiological parameters’ monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter’s functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients’ care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

This paper contributes to building a systematic view of the mobility characteristics of smart cities by reviewing the lessons learned from the best practices implemented around the world. The main features of smart cities, such as smart homes, smart infrastructure, smart operations, smart services, smart utilities, smart energy, smart governance, smart lifestyle, smart business, and smart mobility in North America, Asia, and Europe are briefly reviewed. The study predominantly focuses on smart mobility features and their implications in newly built smart cities. As a case study, the modern city of Lusail located in the north of Doha, Qatar is considered. The provision of car park management and guidance, real-time traffic signal control, traffic information system, active-modes arrangement in promenade and busy urban avenues, LRT, buses, taxis, and water taxis information system, and multimodal journey planning facilities in the Lusail smart city is discussed in this study. Consequently, the implications of smart mobility features on adopting Intelligent Transportation Systems (ITS) will be studied. The study demonstrates that the implementation of Information and Communication Technologies (ICT) when supported by Intelligent Transportation Systems (ITS), could result in making the most efficient use of existing transportation infrastructure and consequently improve the safety and security, mobility, and the environment in urban areas. The findings of this study could be considered an initial step in the implementation of Mobility-as-a-Service (MaaS) in cities with advanced public transportation such as Doha, the capital of Qatar. Doi: 10.28991/CEJ-2022-08-10-09 Full Text: PD

Multi-UAV Allocation Framework for predictive crime deterrence and data acquisition

The recent decline in the number of police and security force personnel has raised a serious security issue that could lead to reduced public safety and delayed response to crimes in urban areas. This may be alleviated in part by utilizing micro or small unmanned aerial vehicles (UAVs) and their high-mobility on-board sensors in conjunction with machine-learning techniques such as neural networks to offer better performance in predicting times and places that are high-risk and deterring crimes. The key to the success of such operation lies in the suitable placement of UAVs. This paper proposes a multi-UAV allocation framework for predictive crime deterrence and data acquisition that consists of the overarching methodology, a problem formulation, and an allocation method that work with a prediction model using a machine learning approach. In contrast to previous studies, our framework provides the most effective arrangement of UAVs for maximizing the chance to apprehend offenders whilst also acquiring data that will help improve the performance of subsequent crime prediction. This paper presents the system architecture assumed in this study, followed by a detailed description of the methodology, the formulation of the problem, and the UAV allocation method of the proposed framework. Our framework is tested using a real-world crime dataset to evaluate its performance with respect to the expected number of crimes deterred by the UAV patrol. Furthermore, to address the engineering practice of the proposed framework, we discuss the feasibility of the simulated deployment scenario in terms of energy consumption and the relationship between data analysis and crime prediction

Digitalization in Buildings and Smart Cities on the Way to 6G

The energy turnaround created a high volatility in the energy production based on renewable energy. To integrate renewable energy economically in buildings and smart cities an additional concept of energy storage and energy supply based on energy management concepts must be claimed. The political views have changed during the last years and energy efficiency in buildings is seen important because 35% of greenhouse gas is produced by the final energy consumption. The deployment of local energy production concepts is an important step to energy turnaround. To generate and distribute energy effectively in buildings, digital components such as sensors, actuators, meters, and energy management systems must be installed in the buildings and the digital components must be able to communicate via communication networks. The paper describes systems for local energy generation, necessary communication networks for buildings and smart cities and digitization applications in industrial buildings. As an example of energy management, the Oktett64 system is presented, which is based on Enterprise IT technology and has implemented AI and blockchain technology. Digitalization with platforms such as Oktett64 are based on technologies that are superior to today's often commercially available Programmable Logic Controllers. The article also shows how the future mobile communications standards 5G beyond and 6G can offer special solutions for the digitization of buildings in their edge clouds.TU Berlin, Open-Access-Mittel – 202

Prediction of Housing Price and Forest Cover Using Mosaics with Uncertain Satellite Imagery

The growing world is more expensive to estimate land use, road length, and forest cover using a plant-scaled ground monitoring system. Satellite imaging contains a significant amount of detailed uncertain information. Combining this with machine learning aids in the organization of these data and the estimation of each variable separately. The resources necessary to deploy Machine learning technologies for Remote sensing images, on the other hand, restrict their reach ability and application. Based on satellite observations which are notably underutilised in impoverished nations, while practical competence to implement SIML might be restricted. Encoded forms of images are shared across tasks, and they will be calculated and sent to an infinite number of researchers who can achieve top-tier SIML performance by training a regression analysis onto the actual data. By separating the duties, the proposed SIML solution, MOSAIKS, shapes SIML approachable and global. A Featurization stage turns remote sensing data into concise vector representations, and a regression step makes it possible to learn the correlations which are specific to its particular task which link the obtained characteristics to the set of uncertain data

Spatial modelling of air pollution for open smart cities

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the world’s population are expected to further move into urban areas. This urban growth leads to various environmental, social and economic challenges in cities, hampering the Quality of Life (QoL). Although recent trends in technologies equip us with various tools and techniques that can help in improving quality of life, air pollution remains the ‘biggest environmental health risk’ for decades, impacting individuals’ quality of life and well-being according to World Health Organisation (WHO). Many efforts have been made to measure air quality, but the sparse arrangement of monitoring stations and the lack of data currently make it challenging to develop systems that can capture within-city air pollution variations. To solve this, flexible methods that allow air quality monitoring using easily accessible data sources at the city level are desirable. The present thesis seeks to widen the current knowledge concerning detailed air quality monitoring by developing approaches that can help in tackling existing gaps in the literature. The thesis presents five contributions which address the issues mentioned above. The first contribution is the choice of a statistical method which can help in utilising existing open data and overcoming challenges imposed by the bigness of data for detailed air pollution monitoring. The second contribution concerns the development of optimisation method which helps in identifying optimal locations for robust air pollution modelling in cities. The third contribution of the thesis is also an optimisation method which helps in initiating systematic volunteered geographic information (VGI) campaigns for detailed air pollution monitoring by addressing sparsity and scarcity challenges of air pollution data in cities. The fourth contribution is a study proposing the involvement of housing companies as a stakeholder in the participatory framework for air pollution data collection, which helps in overcoming certain gaps existing in VGI-based approaches. Finally, the fifth contribution is an open-hardware system that aids in collecting vehicular traffic data using WiFi signal strength. The developed hardware can help in overcoming traffic data scarcity in cities, which limits detailed air pollution monitoring. All the contributions are illustrated through case studies in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under the broader framework of the open smart city and for urban health research

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

The Need of Multidisciplinary Approaches and Engineering Tools for the Development and Implementation of the Smart City Paradigm

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art

Towards Smarter Management of Overtourism in Historic Centres Through Visitor-Flow Monitoring

Historic centres are highly regarded destinations for watching and even participating in diverse and unique forms of cultural expression. Cultural tourism, according to the World Tourism Organization (UNWTO), is an important and consolidated tourism sector and its strong growth is expected to continue over the coming years. Tourism, the much dreamt of redeemer for historic centres, also represents one of the main threats to heritage conservation: visitors can dynamize an economy, yet the rapid growth of tourism often has negative effects on both built heritage and the lives of local inhabitants. Knowledge of occupancy levels and flows of visiting tourists is key to the efficient management of tourism; the new technologies—the Internet of Things (IoT), big data, and geographic information systems (GIS)—when combined in interconnected networks represent a qualitative leap forward, compared to traditional methods of estimating locations and flows. A methodology is described in this paper for the management of tourism flows that is designed to promote sustainable tourism in historic centres through intelligent support mechanisms. As part of the Smart Heritage City (SHCITY) project, a collection system for visitors is developed. Following data collection via monitoring equipment, the analysis of a set of quantitative indicators yields information that can then be used to analyse visitor flows; enabling city managers to make management decisions when the tourism-carrying capacity is exceeded and gives way to overtourism.Funded by the Interreg Sudoe Programme of the European Regional Development Funds (ERDF

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio