Piercosimo Rametta

A novel architecture enabling the visual implementation of web of Things applications

The concept of Internet of Things is becoming one of the key aspects for the future Internet, where embedded devices will be responsible for collecting data from the surrounding environment and making them available to useful mash-up applications. In order to guarantee a high feasibility of this scenario, it would be appropriate to have a flexible and intuitive architecture for the implementation of such applications without knowing specific details about the constrained hardware and operating system. Therefore, in this work, a software system for the graphical development of mash-up applications, dedicated to Wireless Sensor Networks, was designed and implemented. It is based on a Constrained Application Protocol application server, called Actinium, and on a graphical editor, called ClickScript. Especially the latter one has been substantially modified in order to communicate with Actinium and to interact with the constrained resources made available by the WSN. The effectiveness of the proposed solution has been tested through a real use case that has demonstrated the validity of the whole system.

Capturing behavioral changes of elderly people through unobtruisive sensing technologies

The behavioral analysis of individuals is an important science, especially if it is conducted on the elderly population, aiming to prevent Mild Cognitive Impairment (MCI) and frailty problems. A fundamental aspect in this context is to explore the use of innovative technologies enabling the Internet of Things (IoT), above all sensors, to unobtrusively capture personal data for automatically recognizing behavioral changes in elderly people. This is done with the aim to timely identify risks of MCI and frailty before they escalate into more serious conditions such as Alzheimer Disease. This paper aims to briefly describe the overall goal of the City4Age project, funded by the Horizon 2020 Programme of the European Commission, in particular focusing on the IoT-based personal data capturing system.

A Complex Event Processing based smart aid system for fire and danger management

When hazardous events occurs in buildings or in large environments with different access points and with a large number of users, rescue workers (firefighters, first aid workers, civil protection teams, etc.) need to intervene in a timely manner, where there is a certainty that there are users to help. Topically such events require avoiding waste of resources in environments where there are no people at the time of the disaster or where the damage is of low magnitude. To guide rescuers at the points of the building where there are users to help, we modeled and built an Internet of Things-based framework that monitors data and environmental parameters of interest and, if certain thresholds are exceeded, alerts the rescuers through a telephone call to emergency numbers. The hardware infrastructure is driven by a complex, flexible and adaptive software layer that behaves depending on a Complex Event Processing engine and a reflective middleware according to the rule based engine that manages data from the sensors and reasoning mechanisms of a knowledge base that models the given domain.

A novel rule-based semantic architecture for IoT building automation systems

The ever growing number of smart devices connected to the Internet of Things is giving users the chance to sense data from surrounding environment and act upon it. However, interpreting raw data coming from heterogeneous sensors and applying control algorithms to actuators is not a simple task for the common end-user who wants to create applications for smart environments. For these reasons, this work deals with the definition of a novel rule-based semantic architecture for the implementation of building automation applications in an IoT context. Sensor data are abstracted at a high semantic level related to the properties they are associated to and interactions with actuators are driven by high-level desired actions. Applications have the form of an Event-Condition-Action (ECA) rule and the layered architecture separates high-level semantic reasoning aspects from low-level execution details. The proposed architecture is also compared with main state-of-the-art solutions and some suitable technologies for its implementation are suggested.

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

The world population will be made up of a growing number of elderly people in the near future. Aged people are characterized by some physical and cognitive diseases, like mild cognitive impairment (MCI) and frailty, that, if not timely diagnosed, could turn into more severe diseases, like Alzheimer disease, thus implying high costs for treatments and cares. Information and Communication Technologies (ICTs) enabling the Internet of Things (IoT) can be adopted to create frameworks for monitoring elderly behavior which, alongside normal clinical procedures, can help geriatricians to early detect behavioral changes related to such pathologies and to provide customized interventions. As part of the City4Age project, this work describes a novel approach for collecting and managing data about elderly behavior during their normal activities. The data capturing layer is an unobtrusive and low-cost sensing infrastructure abstracting the heterogeneity of physical devices, while the data management layer easily manages the huge quantity of sensed data, giving them semantic meaning and fostering data shareability. This work provides a functional validation of the proposed architecture and introduces how the data it manages can be used by the whole City4Age platform to early identify risks related to MCI/frailty and promptly intervene.

An IoT-aware system for elderly monitoring

The aging population is a global phenomenon, characterized by many interesting challenges. In this context, the Internet of Things technologies could allow to analyze the elderlys behavioral in an unobtrusive way, thus helping to prevent Mild Cognitive Impairment and frailty problems. To this end, this work aims to define a reliable system for controlling the position and the body motility of the elderly in low-cost and low-power way. Movements and body motility are, indeed, good indicators of behavioral changes. The system represents the basis of a complete architecture for behavioral analysis and risk detection developed within the City4Age project, funded by the Horizon 2020 Programme of the European Commission.

An innovative approach for monitoring elderly behavior by detecting home appliance's usage

Detecting how elderly people interact with their surrounding environment, especially with domestic electrical appliances, is an important parameter to assess Mild Cognitive Impairments and frailty issues. This paper proposes an innovative approach for monitoring elderly behavior by detecting home appliances usage. It is based on an unobtrusive smart meter that periodically measures the global power consumption in the house, associated with some smart plugs for punctually monitoring specific electrical devices. This infrastructure has been implemented and validated within the Personal Data Capturing System of the City4Age Platform, where, joined with other provided monitoring systems, can feed risk detection algorithms with more accurate data. Summarizing, implemented system, although simple and at low cost, is able to combine data provided by designed power meter with those of smart meter plugs and, by means of implemented algorithms, to detect unusual elder behavior, moreover resulting reliable and accurate.

An innovative approach for elderly behavioral analysis by adopting enabling IoT technologies

As the average age of the citizens increases, cities must provide new services for the emerging problem. The City4Age project aims to provide meaningful interventions to address the problems related to Mild Cognitive Impairment and Frailty in elderly citizens. As part of the City4Age project we have developed a flexible and scalable data capturing and management infrastructure which combines both the Internet of Things and Linked Open Data paradigms. A proof-of-concept validation illustrates how data are collected, managed and computed by the proposed system to make them available for MCI and frailty risk detection algorithms and for third parties.