Marco Ortolani

Monitoring High-Quality Wine Production using Wireless Sensor Networks

This work reports the experience on the design and deployment of a WSN-based system for monitoring the productive cycle of high-quality wine in a Sicilian winery. Besides providing the means for pervasive monitoring of the cultivated area, the project described here is aimed to support the producer in ensuring the overall quality of their production, in terms of accurate planning of interventions in the field, and preservation of the stored product. Wireless Sensor Networks are employed as the sensing infrastructure of a distributed system for the control of a prototypal productive chain; nodes have been deployed both in the field and in the cellar, where wine aging is performed, and data is collected at a central unit in order to perform inferences that suggest timely interventions that preserve the grapes’ quality.

Intelligent Management Systems for Energy Efficiency in Buildings: A Survey

In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.

WSNs for structural health monitoring of historical buildings

Monitoring structural health of historical heritage buildings may be a daunting task for civil engineers due to the lack of a pre-existing model for the building stability, and to the presence of strict constraints on monitoring device deployment. This paper reports on the experience maturated during a project regarding the design and implementation of an innovative technological framework for monitoring critical structures in Sicily, Italy. The usage of Wireless Sensor Networks allows for a pervasive observation over the sites of interest in order to minimize the potential damages that natural phenomena may cause to architectural or engineering works. Moreover, the system provides real-time feedback to the civil engineer that may promptly steer the functioning of the monitoring network, also remotely accessing sensed data via web interfaces.

A Hybrid Framework for Soft Real-Time WSN Simulation

The design of a wireless sensor network is a challenging task due to its intrinsically application-specific nature.Although a typical choice for testing such kind of networks requires devising ad-hoc testbeds, this is often impractical as it depends on expensive, and hard to maintain deployment of nodes. On the other hand, simulation is a valuable option, as long as the actual functioning conditions are reliably modeled, and carefully replicated.The present work describes a framework for supporting the user in early design and testing of a wireless sensor network with an augmented version of TOSSIM, the de-facto standard for simulators, that allows merging actual and virtual nodes seamlessly interacting with each other; the proposed tool does not require any special modification to the original simulation code, but it allows contemporary execution of code in actual, and virtual nodes, as well as simulation of nodes executing different application logics. The reported experimental results will also show how soft-real time constraints are guaranteed for the augmented simulation.

User activity recognition for energy saving in smart homes

Current energy demand for appliances in smart homes is nowadays becoming a severe challenge, due to economic and environmental reasons; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. The present proposal consists in recognizing user daily life activities by simply relying on the analysis of environmental sensory data in order to minimize energy consumption by guaranteeing that peak demands do not exceed a given threshold. Our approach is based on information theory in order to convert raw data into high-level events, used to represent recursively structured activities. Experiments based on publicly available datasets and consumption models are provided to show the effectiveness of our proposal.

Adaptive Distributed Outlier Detection for WSNs

The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.

Multi-sensor Fusion through Adaptive Bayesian Networks

Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.

Exploiting the Human Factor in a WSN-Based System for Ambient Intelligence

Practical applications of Ambient Intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by Wireless Sensor Networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system.By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part.WSNs act as a transparent interface that allows the system to understand human requirements through implicit feedback, and consequently adapt its behavior.A central unit will carry on symbolic reasoning based on the concepts extracted from sensory inputs collected and pre-processed by pervasively deployed WSNs.

Robust and Efficient Data Gathering for Wireless Sensor Networks

This paper describes a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving, typical of such networks, and for a reasonable tradeoff between robustness and efficiency. The proposed algorithm implements an efficient strategy for retransmission of lost packets by discovering alternative routes and making clever use of multiple paths when necessary; in order to do that we build upon the general framework presented in recent works, that provided a formulation of duplicate and order insensitive aggregation functions, and by taking advantage of some intrinsic characteristics of the wireless sensor networks, we exploit implicit acknowledgment of reception and smart caching of the data. Assuming that, unlike in an ideal scenario, data originates from only a subset of all sensors, our approach provides a better usage of the resources and a minimization of the traffic in the network, and, as a consequence, of the overall consumed energy.

Secure random number generation in wireless sensor networks

Reliable random number generation is crucial for many available security algorithms, and some of the methods presented in literature proposed to generate them based on measurements collected from the physical environment, in order to ensure true randomness. However the effectiveness of such methods can be compromised if an attacker is able to gain access to the measurements thus inferring the generated random number. In our paper, we present an algorithm that guarantees security for the generation process, in a real world scenario using wireless sensor nodes as the sources of the physical measurements. The proposed method uses distributed leader election for selecting a random source of data. We prove the robustness of the algorithm by discussing common security attacks, and we present theoretical and experimental evaluation regarding its complexity in terms of time and exchanged messages.