HIDE: User centred Domotic evolution toward Ambient Intelligence

Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other. The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make today’s heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on. The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones. The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated

A Bayesian Packet Sharing Approach for Noisy IoT Scenarios

International audience—Cloud computing and Internet of Things (IoT) represent two different technologies that are massively being adopted in our daily life, playing a fundamental role in the future Internet. One important challenge that need to be handled is the enormous amount of data generated by sensing devices, that make the control of sending useless data very important. In order to face with this challenge, there is a increasing interest about predictive approaches to avoid to send high spatio-temporal correlated data. Belief Propagation (BP) algorithm is a method of performing approximate inference on arbitrary graphical models that is becoming increasingly popular in the context of IoT. By exploiting BP, we can derive effective methods to drastically reduce the number of transmitted messages, while keeping high the data throughput in the global information system. In this paper, we propose a BP approach in a hierarchical architecture with simple nodes, gateways and data centers. We evaluate the error bounding and propose a corrective mechanism to keep a certain quality of the global information in the architecture considered

Towards building mobile smart-IoT service system

The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.Peer ReviewedPostprint (author's final draft

Optimal Domotic Systems Based on Archival Data Trend Analysis

Domotics is the integration of technology into building systems. Due to the rapid growth in the use of domotic systems in recent years, the industry is struggling to establish consistency and standardization. The purpose of this archival-based qualitative case study was to identify current trends and patterns in scholarly domotic research to create an instrument to evaluate domotic systems and domotic interrelationships using bibliometric searches. The facilities management and modeling system provided the framework for the study. Archival research data were examined to identify trends and patterns in domotic research and provide visualization of domotic relationships through technology trajectory mapping and technology s-curve charts. Text-mining techniques were used to explore trends and patterns in recent scholarly domotic research. The technology s-curve was used to determine trends and patterns in domotic systems design. The results included a tool for the evaluation of domotic systems, which may provide domotic designers with a tool to evaluate the progress of domotic systems. The study also provided results on trends in domotic technologies, which may be used to improve building design development

Hadoop-Based Intelligent Care System (HICS) : Analytical Approach for Big Data in IoT

The Internet of Things (IoT) is increasingly becoming a worldwide network of interconnected things that are uniquely addressable, via standard communication protocols. The use of IoT for continuous monitoring of public health is being rapidly adopted by various countries while generating a massive volume of heterogeneous, multisource, dynamic, and sparse high-velocity data. Handling such an enormous amount of high-speed medical data while integrating, collecting, processing, analyzing, and extracting knowledge constitutes a challenging task. On the other hand, most of the existing IoT devices do not cooperate with one another by using the same medium of communication. For this reason, it is a challenging task to develop healthcare applications for IoT that fulfill all user needs through real-Time monitoring of health parameters. Therefore, to address such issues, this article proposed a Hadoop-based intelligent care system (HICS) that demonstrates IoT-based collaborative contextual Big Data sharing among all of the devices in a healthcare system. In particular, the proposed system involves a network architecture with enhanced processing features for data collection generated by millions of connected devices. In the proposed system, various sensors, such as wearable devices, are attached to the human body and measure health parameters and transmit them to a primary mobile device (PMD). The collected data are then forwarded to intelligent building (IB) using the Internet where the data are thoroughly analyzed to identify abnormal and serious health conditions. Intelligent building consists of (1) a Big Data collection unit (used for data collection, filtration, and load balancing); (2) a Hadoop processing unit (HPU) (composed of Hadoop distributed file system (HDFS) and MapReduce); and (3) an analysis and decision unit. The HPU, analysis, and decision unit are equipped with a medical expert system, which reads the sensor data and performs actions in the case of an emergency situation. To demonstrate the feasibility and efficiency of the proposed system, we use publicly available medical sensory datasets and real-Time sensor traffic while identifying the serious health conditions of patients by using thresholds, statistical methods, and machine-learning techniques. The results show that the proposed system is very efficient and able to process high-speed WBAN sensory data in real time

Innovative Technologies and Services for Smart Cities

A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

Integración de herramienta de analítica en el modelo AMI-IOT Quysqua

El problema central del presente trabajo de grado se basa en el desarrollo de un modelo ex-tendido que integre los modelos de solución AmI-IoT y SICOBIO. Esta integración está dividida en dos dimensiones; la primera dimensión se enfoca no solo en el cuidado y bienestar de personas de la tercera edad, sino también adultos mayores enfermos que necesitan cuidado médico en casa. Con lo que se pretende tener un marco común, que de la posibilidad a tener un impacto mayor en la calidad de vida, bienestar y cuidado médico remoto de la persona de la tercera edad en su hogar; la segunda dimensión se enfoca en el ámbito informático, incorporando y profundizando el componente de herramientas genéricas para analítica.The central problem of the present degree work is based on the development of an extended model that integrates the AmI-IoT and SICOBIO solution models. This integration is divided into two dimensions; The first dimension focuses not only on the care and well-being of the elderly, but also on parents who need medical care at home. Which means that it has a com-mon framework, which can have a major impact on the quality of life, well-being and remote medical care of the elderly person in their home; the second dimension focuses on the com-puter field, incorporating and deepening the component of generic tools for analysis.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí

The making of smart cities : borders, security and value in New Town Kolkata and Cape Town

The making of smart cities transforms not only infrastructures and practices but also the techniques of urban government and security, and economic processes. This thesis draws on analysis conducted in two research sites: Cape Town, in South Africa and New Town Rajarhat, a satellite township on the outskirts of Kolkata, to present three key arguments. Firstly, and as opposed to mainstream narratives that describe smart cities as seamlessly connected environments, this thesis suggests that urban digitalisation is linked to bordering processes. Whereas critical literature has comprehensively discussed the political implications and risks associated with smart city projects, such as corporatisation and technocratic governance, the specific relations between digital infrastructures and borders, within the urban space, have not yet been discussed. Secondly, this thesis argues that smart cities are inherently security projects, insofar as the deployment of a computing infrastructure of sensing initiates a preemptive apparatus. In security systems, such as the Emergency Policing and Incident Command (EPIC) program in Cape Town, or the Xpresso software for social media monitoring in New Town, algorithms are continuously modelling and acting upon future scenarios; from traffic jams to wildfires, from crime hotspots to citizens’ moods. My third argument is that the computing apparatus of security also serves as an infrastructure of value extraction. Recently, there has been much theorising and debate about security platforms’ economic operations, but the situated modalities in which they extract value from the urban environment remain to be examined. Overall, this thesis points to the socio-spatial, governmental and economic relations that computing infrastructures are generating, or reconfiguring, in the urban environment. These relations articulate distinct processes, including the hierarchisation and control of the urban space, preemptive policies and extractive strategies. Critically analysing these processes allows the registration of the political implications of smart city projects