Search results for "Smart"

Enabling Technologies on Hybrid Camera Networks for Behavioral Analysis of Unattended Indoor Environments and Their Surroundings

2008

This paper presents a layered network architecture and the enabling technologies for accomplishing vision-based behavioral analysis of unattended environments. Specifically the vision network covers both the attended environment and its surroundings by means of multi-modal cameras. The layer overlooking at the surroundings is laid outdoor and tracks people, monitoring entrance/exit points. It recovers the geometry of the site under surveillance and communicates people positions to a higher level layer. The layer monitoring the unattended environment undertakes similar goals, with the addition of maintaining a global mosaic of the observed scene for further understanding. Moreover, it merges …

Plantxel: Towards a plant-based controllable display

2018

The use of plants as a mean for both visualization and interaction has been already explored in smart environments. In this work, we explore the possibility of constructing a controllable dynamic plant-based display using thigmonastic plants, i.e. plants that change the shape and position of their leaves as a response to external stimuli. As an initial step towards this vision, we first introduce our approach of building a plant-based pixel (plant-pixel, or plantxel), and the principles of composing a plantxel-based public display. We then present the results of a feasibility study conducted with Mimosa spegazzinii plants, showing that our approach can achieve an acceptable contrast ratio, …

Detection of Points of Interest in a Smart Campus

2019

Understanding users' habits is a critical task in order to develop advanced services, such as personalized recommendation and virtual assistance. In this work, we propose a novel approach to detect Points of Interest visited by users of a campus, by using mobility traces collected through users' smartphones. Our method takes advantage of the intentional and recurrent nature of human movements to build up mobility profiles, and combines different machine learning methods to merge sensory information with the past users' behavior. The proposed approach has been validated on a synthetic dataset and the experimental results show its effectiveness.

A fog-based hybrid intelligent system for energy saving in smart buildings

2019

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, …

REPUTATION MANAGEMENT ALGORITHMS IN DISTRIBUTED APPLICATIONS

2020

Nowadays, several distributed systems and applications rely on interactions between unknown agents that cooperate in order to exchange resources and services. The distributed nature of these systems, and the consequent lack of a single centralized point of control, let agents to adopt selfish and malicious behaviors in order to maximize their own utility. To address such issue, many applications rely on Reputation Management Systems (RMSs) to estimate the future behavior of unknown agents before establishing actual interactions. The relevance of these systems is even greater if the malicious or selfish behavior exhibited by a few agents may reduce the utility perceived by cooperative agents…

Ambient Intelligence for Energy Efficiency in a Complex of Buildings

2013

Evaluating Correlations in IoT Sensors for Smart Buildings

2021

International audience; In this paper we introduce a dataset of environmental information obtained via indoor and outdoor sensors deployed in the SMART Infrastructure Facility of the University of Wollongong (Australia). The acquired dataset is also made open-sourced along with this paper. We also propose a novel approach based on an evolutionary algorithm to determine pairs of correlated sensors. We compare our approach with three other standard techniques on the same dataset: on average, the accuracy of the evolutionary method is about 62,92%. We also evaluate the computational time, assessing the suitability of the proposed pipeline for real-time applications.

A mobile application for assessment of air pollution exposure

2013

In this paper the architecture of a mobile air quality monitoring system is introduced. A mobile application will act as a personal assistant, monitoring and giving advices about gas pollutants daily exposure. Currently in development stage as part of a larger air quality monitoring system project, the application will enable users to monitor their daily exposure to gas pollutants by combining user location data and urban air quality information provided by the network of fixed monitoring stations of the city of Palermo.

An Agent-based Service Network for Personal Mobile Devices

2006

We propose the Agent Network for Bluetooth Devices, a system that uses personal mobile devices as adaptive human-environment interfaces to supply people with ad hoc information and high-level services. The ANBD system operates with a hierarchical framework of service-providing nodes, dynamically composed and managed by mobile agents.

Anomaly Detection for Reoccurring Concept Drift in Smart Environments

2022

Many crowdsensing applications today rely on learning algorithms applied to data streams to accurately classify information and events of interest in smart environments. Unfor-tunately, the statistical properties of the input data may change in unexpected ways. As a result, the definition of anomalous and normal data can vary over time and machine learning models may need to be re-trained incrementally. This problem is known as concept drift, and it has often been ignored by anomaly detection systems, resulting in significant performance degradation. In addition, the statistical distribution of past data often tends to repeat itself, and thus old learning models could be reused, avoiding co…