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 i…

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.

Detecting faulty wireless sensor nodes through Stochastic classification

In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an ana…

A Network Protocol to Enhance Robustness in Tree-Based WSNs Using Data Aggregation

This paper proposes a data gathering strategy for wireless sensor networks and an implementation based on the IEEE 802.15.4 standard. The algorithm combines the benefits of single-path and multi-path routing strategies in a hybrid solution which makes use of alternative paths when necessary. We adopt a caching and retransmission technique, which exploits some peculiar features of data aggregation, with the use of implicit acknowledgments of reception. The paper also discusses simulation results that show how the mentioned techniques, combined with exploitation of the features of the IEEE 802.15.4 standard have been used to obtain an efficient protocol that takes energy consumption into acco…

Knowledge Extraction from Environmental Data Through a Cognitive Architecture

Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. T…

A Logical Framework for Augmented Simulations of Wireless Sensor Networks

This paper describes a framework for practical and efficient monitoring of a wireless sensor network. The architecture proposed exploits the dynamic reasoning capabilities of the situation calculus in order to assess the sensor network behavior before actually deploying all the nodes. Designing a wireless sensor network for a specific application typically involves a preliminary phase of simulations that rely on specialized software, whose behavior does not necessarily reproduce what will be experienced by an actual network. On the other hand, delaying the test phase until deployment may not be advisable due to unreasonable costs. This paper suggests the adoption of a hybrid approach that i…

A Network Tomography Approach for Traffic Monitoring in Smart Cities

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential presence of noisy measurements, and t…

An Ambient Intelligence System for Assisted Living

Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is ab…

Multi-robot Interacting Through Wireless Sensor Networks

This paper addresses the issue of coordinating the operations of multiple robots in an indoor environment. The framework presented here uses a composite networking architecture, in which a hybrid wireless network, composed by commonly available WiFi devices, and the more recently developed wireless sensor networks. Such architecture grants robots to enhance their perceptive capabilities and to exchange information so as to coordinate actions in order to achieve a global common goal. The proposed framework is described with reference to an experimental setup that extends a previously developed robotic tour guide application in the context of a multi-robot application.

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 impl…

SmartBuildings: An AmI system for energy efficiency

Nowadays, the increasing global awareness of the importance of energy saving in everyday life acts as a stimulus to provide innovative ICT solutions for sustainability. In this scenario, the growing interest in smart homes has been driven both by socioeconomic and technological expectations. One of the key aspects of being smart is the efficiency of the urban apparatus, which includes, among others, energy, transportation and buildings. The present work describes SmartBuildings, a novel Ambient Intelligence system, which aims at reducing the energy consumption of "legacy" buildings by means of artificial intelligence techniques applied on heterogeneous sensor networks. A prototype has been …

Gesture Recognition for Improved User Experience in a Smart Environment

Ambient Intelligence (AmI) is a new paradigm that specifically aims at exploiting sensory and context information in order to adapt the environment to the user's preferences; one of its key features is the attempt to consider common devices as an integral part of the system in order to support users in carrying out their everyday life activities without affecting their normal behavior. Our proposal consists in the definition of a gesture recognition module allowing users to interact as naturally as possible with the actuators available in a smart office, by controlling their operation mode and by querying them about their current state. To this end, readings obtained from a state-of-the-art…

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 o…

AN INNOVATIVE SYSTEM FOR VINEYARD MANAGEMENT IN SICILY

The aim of this study was to monitor the micro-climate of the grapevine in order to detect the adversities of the spring period (especially April and May), while reducing the operating costs of the vineyard, and increasing the overall quality of grapes. For this purpose a Wireless Sensor Network (WSN) was used. Furthermore, a comparison was performed between data measured by the wireless sensors and data provided by the fixed meteorological station of the Regione Siciliana (SIAS).

Improving user experience via motion sensors in an Ambient Intelligence scenario

Ambient Intelligence (AmI) is a new paradigm in Artificial Intelligence that aims at exploiting the information about the environment state in order to adapt it to the user preferences. AmI systems are usually based on several cheap and unobtrusive sensing devices that allow for continuous monitoring in different scenarios. In this work we present a gesture recognition module for the management of an office environment using a motion sensor device, namely Microsoft Kinect, as the primary interface between the user and the AmI system. The proposed gesture recognition method is based on both RGB and depth information for detecting the hand of the user and a fuzzy rule for determining the stat…

Gaining insight by structural knowledge extraction

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside the system itself; however, the designer is expected to intuitively grasp the most relevant features of the raw data as a. preliminary step. In this paper we propose a framework to get useful insight about a set of complex data, and we claim that a shift in perspective may be of help to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples. We will present a formulation of knowle…

Structural Knowledge Extraction from Mobility Data

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …

Monitoring wireless sensor networks through logical deductive processes

This paper proposes a distributed multi-agent architecture for wireless sensor networks management, which exploits the dynamic reasoning capabilities of the Situation Calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by tunable agents installed on the network nodes and is collected by a logical entity for network managing where it is merged with general domain knowledge, with the aim of identifying the root causes of faults, and deciding on reparative actions. The logical inference system has being devised to carry out automated isolation, diagnosis, and, whenever possible, repair of network anoma…

Cognitive meta-learning of syntactically inferred concepts

This paper outlines a proposal for a two-level cognitive architecture reproducing the process of abstract thinking in human beings. The key idea is the use of a level devoted to the extraction of compact representation for basic concepts, with additional syntactic inference carried on at a meta-level, in order to provide generalization. Higher-level concepts are inferred according to a principle of simplicity, consistent with Kolmogorov complexity, and merged back into the lower level in order to widen the underlying knowledge base.

User Activity Recognition for Energy Saving in Smart Homes

Abstract Energy demand in typical home environments accounts for a significant fraction of the overall consumption in industrialized countries. In such context, the heterogeneity of the involved devices, and the non negligible influence of the human factor make the optimization of energy use a challenging task; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. Our proposal consists in learning customized structural models for common user activities for predicting the trend of energy consumption; the approach aims to lower energy demand in the proximity of predicted peak loads so as to keep the overall cons…

Probabilistic Anomaly Detection for Wireless Sensor Networks

Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities.

An Integrated Architecture for Surveillance and Monitoring in an Archaeological Site

This paper describes an on-going work aimed at designing and deploying a system for the surveillance and monitoring of an archaeological site, namely the "Valley of the Temples" in Agrigento, Italy. Given the relevance of the site from an artistical and historical point of view, it is important to protect the monuments from malicious or simply incautious behavior; however, the vastity of the area to be monitored and the vague definition of its boundaries make it unpractical to provide extensive coverage through traditional sensors or similar devices. We describe the design of an architecture for the surveillance of the site and for the monitoring of the visitors' behavior consisting in an i…

Sensor9k : A testbed for designing and experimenting with WSN-based ambient intelligence applications

Ambient Intelligence systems are typically characterized by the use of pervasive equipment for monitoring and modifying the environment according to users' needs, and to globally defined constraints. Our work describes the implementation of a testbed providing the hardware and software tools for the development and management of AmI applications based on wireless sensor and actuator networks, whose main goal is energy saving for global sustainability. A sample application is presented that addresses temperature control in a work environment, through a multi-objective fuzzy controller taking into account users' preferences and energy consumption.

Human-ambient interaction through Wireless Sensor Networks

Recent developments in technology have permitted the creation of cheap, and unintrusive devices that may be effectively employed for instrumenting an intelligent environment. The present work describes a modular framework that makes use of a class of those devices, namely wireless sensors, in order to monitor relevant physical quantities and to collect users' requirements through implicit feedback. A central intelligent unit extracts higher-level concepts from raw sensory inputs, and carries on symbolic reasoning based on them. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users' desires, taking into account b…

Robust Data Gathering for Wireless Sensor Networks

2005 13th IEEE International Conference on Networks jointly held with the 2005 7th IEEE Malaysia International Conference on Communications, Proceedings Volume 1, 2005, Article number 1635527, Pages 469-474 2005 13th IEEE International Conference on Networks jointly held with the 2005 7th IEEE Malaysia International Conference on Communications; Kuala Lumpur; Malaysia; 16 November 2005 through 18 November 2005; Category number05EX1235; Code 69262 Robust data gathering for wireless sensor networks (Conference Paper) Ortolani, M. , Gatani, L. , Lo Re, G. Dipartimento di Ingegneria Informatica, Università degli Studi di Palermo, Viale delle Scienze Parco d'Orleans, 90128 Palermo, Italy View re…

Adaptive Collision Avoidance through Implicit Acknowledgments in WSNs

The large number of nodes, typical of many sensor network deployments, and the well-known hidden terminal problem make collision avoidance an essential goal for the actual employment of WSN technology. Collision avoidance is traditionally dealt with at the MAC Layer and plenty of different solutions have been proposed, which however have encountered limited diffusion because of their incompatibility with commonly available devices.In this paper we propose an approach to collision avoidance which is designed to work over a standard MAC Layer, namely the IEEE 802.15.4 MAC, and is based on application-controlled delays of packet transmission times. The proposed scheme is simple, decentralized …

Adaptive Collision Avoidance through Implicit Acknowledgments in Wireless Sensor Networks

An Efficient Retransmission Strategy for Data Gathering in Wireless Sensor Networks

This paper introduces a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving 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 use 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.

Sensor Mining for User Behavior Profiling in Intelligent Environments

The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users’ habit profiles are implicitly described by sensory data, which explicitly show the consequences of users’ actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University.

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 com…

1<sup>st</sup> International Workshop on Sustainable Internet and Internet for Sustainability (SustaInet 2011

We are pleased to present the proceedings of the First International Workshop on Sustainable Internet and Internet for Sustainability (SustaInet 2011), held in conjunction with WoWMoM 2011.

Secure random number generation in wireless sensor networks

The increasing adoption of wireless sensor networks as a flexible and inexpensive tool for the most diverse applications, ranging from environmental monitoring to home automation, has raised more and more attention to the issues related to the design of specifically customized security mechanisms. The scarcity of computational, storage, and bandwidth resources cannot definitely be disregarded in such context, and this makes the implementation of security algorithms particularly challenging. This paper proposes a security framework for the generation of true random numbers, which are paramount as the core building block for many security algorithms; the intrinsic nature of wireless sensor no…

Your friends mention It. What about visiting it? A mobile social-based sightseeing application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces

AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of su…

Rule based reasoning for network management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity whe…

A P2P Architecture for Multimedia Content Retrieval

The retrieval facilities of most Peer-to-Peer (P2P) systems are limited to queries based on unique identifiers or small sets of keywords. This approach can be highly labor-intensive and inconsistent. In this paper we investigate a scenario where a huge amount of multimedia resources are shared in a P2P network, by means of efficient content-based image and video retrieval functionalities. The challenge in such systems is to limit the number of sent messages, maximizing the usefulness of each peer contacted in the query process. We achieve this goal by the adoption of a novel algorithm for routing user queries. The proposed approach exploits compact representations of multimedia resources sh…

A Monitoring Framework Exploiting the Synergy between Actual and Virtual Wireless Sensors

This paper describes a framework that allows realistic monitoring of a wireless sensor network in order to assess its behavior before actually deploying all the nodes. Designing a wireless sensor network for a specific application typically involves a preliminary phase of simulations that rely on specialized software, whose behavior does not necessarily reproduce what will be experienced by an actual network. On the other hand, delaying the test phase until deployment may not be advisable due to unreasonable costs. This paper suggests the adoption of a hybrid approach that involves coupling an actual wireless sensor network composed of a minimal set of actual nodes with a simulated one; we …

FDAE: A f̲ailure d̲etector for a̲synchronous e̲vents

An Ambient Intelligence Architecture for Extracting Knowledge from Distributed Sensors

Precisely monitoring the environmental conditions is an essential requirement for AmI projects, but the wealth of data generated by the sensing equipment may easily overwhelm the modules devoted to higher-level reasoning, clogging them with irrelevant details. The present work proposes a new approach to knowledge extraction from raw data that addresses this issue at different levels of abstraction. Wireless sensor networks are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts represented in a geometrical space and carries on symbolic re…

A Hybrid Framework for Soft Real-Time WSN Simulation

The design of a wireless sensor network is a chal- lenging 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 seaml…

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

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, …

Understanding the Environment through Wireless Sensor Networks

This paper presents a new cognitive architecture for extracting meaningful, high-level information from the environment, starting from the raw data collected by a Wireless Sensor Network. The proposed framework is capable of building rich internal representation of the sensed environment by means of intelligent data processing and correlation. Furthermore, our approach aims at integrating the connectionist, data-driven model with the symbolic one, that uses a high-level knowledge about the domain to drive the environment interpretation. To this aim, the framework exploits the notion of conceptual spaces, adopting a conceptual layer between the subsymbolic one, that processes sensory data, a…

Notice of Violation of IEEE Publication Principles: Enhanced P2P Services Providing Multimedia Content

[This paper has been withdrawn by the publisher]Traditional peer-to-peer (P2P) services provide only basic searching facilities, based on unique identifiers or small sets of keywords. Unfortunately, this approach is very inadequate and inefficient when a huge amount of multimedia resources is shared. In this paper, we present an original image and video sharing system, in which a user is able to interactively search interesting resources by means of content-based image and video retrieval techniques. In order to limit the network traffic cost, maximizing the usefulness of each peer contacted in the query process, we also propose the adoption of an adaptive overlay routing algorithm, exploit…

A distributed Bayesian approach to fault detection in sensor networks

Sensor networks are widely used in industrial and academic applications as the pervasive sensing module of an intelligent system. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, dust on the sensor, manumissions and other causes. The aim of this paper is to develop a distributed Bayesian fault detection algorithm that classifies measurements coming from the network as corrupted or not. The computational complexity is polynomial so the algorithm scales well with the size of the network. We tested the approach on a synthetic dataset and obtained significant results in terms of correctly labeled measurements.

Adaptable data models for scalable Ambient Intelligence scenarios

In most real-life scenarios for Ambient Intelligence, the need arises for scalable simulations that provide reliable sensory data to be used in the preliminary design and test phases. This works present an approach to modeling data generated by a hybrid simulator for wireless sensor networks, where virtual nodes coexist with real ones. We apply our method to real data available from a public repository and show that we can compute reliable models for the quantities measured at a given reference site, and that such models are portable to different environments, so as to obtain a complete, scalable and reliable testing environment.

Ambient Intelligence for Energy Efficiency in a Complex of Buildings

A Context-Aware System for Ambient Assisted Living

In the near future, the world's population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user's behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict…

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 acknowl…

Secure random number generation in wireless sensor networks

The increasing adoption of wireless sensor networks as a flexible and inexpensive tool for the most diverseapplications, ranging from environmental monitoring to home automation, has raised more and more atten-tion to the issues related to the design of specifically customized security mechanisms. The scarcity ofcomputational, storage, and bandwidth resources cannot definitely be disregarded in such context, and thismakes the implementation of security algorithms particularly challenging. This paper proposes a securityframework for the generation of true random numbers, which are paramount as the core building blockfor many security algorithms; the intrinsic nature of wireless sensor nodes …

A TRNG Exploiting Multi-Source Physical Data

In recent years, the considerable progress of miniaturization and the consequent increase of the efficiency of digital circuits has allowed a great diffusion of the wireless sensor network technology. This has led to the growth of applications and protocols for applying these networks to several scenarios, such as the military one, where it is essential to deploy security protocols in order to prevent opponents from accessing the information exchanged among sensor nodes. This paper analyzes security issues of data processed by the WSN and describes a system able to generate sequences of random numbers, which can be used by security algorithms and protocols. The proposed True Random Number G…

Wireless Sensor Networks for Marine Environment Monitoring

Extracting Structured Knowledge From Sensor Data for Hybrid Simulation

Obtaining continuous and detailed monitoring of indoor environments has today become viable, also thanks to the widespread availability of effective and flexible sensing technology; this paves the way for the design of practical Ambient Intelligence systems, and for their actual deployment in real-life contexts, which require advanced functionalities, such as for instance the automatic discovery of the activities carried on by users. Novel issues arise in this context; on one hand, it is important to reliably model the phenomena under observation even though, to this end, it is often necessary to craft a carefully designed prototype in order to test and fine-tune the theoretical models.

Sensor Networks for Energy Sustainability in Buildings

Predictive models for energy saving in Wireless Sensor Networks

ICT devices nowadays cannot disregard optimizations toward energy sustainability. Wireless Sensor Networks, in particular, are a representative class of a technology where special care must be given to energy saving, due to the typical scarcity and non-renewability of their energy sources, in order to enhance network lifetime. In our work we propose a novel approach that aims to adaptively control the sampling rate of wireless sensor nodes using prediction models, so that environmental phenomena can be consistently modeled while reducing the required amount of transmissions; the approach is tested on data available from a public dataset.

A machine learning approach for user localization exploiting connectivity data

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training. This work addresses the localization problem, exploiting knowledge acquired in sample environments, and extensible to areas not considered in advance. Localization is turned into a learning problem, solved by a statistical algorithm. Additionally, parameter tuning is fully automated thanks to its formulation as an optimization problem based only on co…

Fuzzy subgroup mining for gene associations

When studying the therapeutic efficacy of potential new drugs, it would be much more efficient to use predictors in order to assess their toxicity before going into clinical trials. One promising line of research has focused on the discovery of sets of candidate gene profiles to be used as toxicity indicators in future drug development. In particular genomic microarrays may be used to analyze the causality relationship between the administration of the drugs and the so-called gene expression, a parameter typically used by biologists to measure its influence at gene level. This kind of experiments involves a high throughput analysis of noisy and particularly unreliable data, which makes the …

An Efficient Retransmission Strategy for Data Gathering in Wireless Sensor Networks Authors

This paper introduces a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving 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 use 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.

Gl-learning

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application s…

Mimicking biological mechanisms for sensory information fusion

Current Artificial Intelligence systems are bound to become increasingly interconnected to their surrounding environment in the view of the newly rising Ambient Intelligence (AmI) perspective. In this paper, we present a comprehensive AmI framework for performing fusion of raw data, perceived by sensors of different nature, in order to extract higher-level information according to a model structured so as to resemble the perceptual signal processing occurring in the human nervous system. Following the guidelines of the greater BICA challenge, we selected the specific task of user presence detection in a locality of the system as a representative application clarifying the potentialities of …

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-tim…

Achieving Robustness through Caching and Retransmissions in IEEE 802.15.4-based WSNs

This paper proposes a network-layer protocol for wireless sensor networks based on the IEEE 802.15.4 standard. Our protocol is devised to provide reliable data gathering in latency-constrained applications, and exploits both the flexibility of the IEEE 802.15.4 MAC layer and features of data aggregation techniques, such as implicit acknowledgment of reception. The proposed protocol acts as a routing module and a control entity for the MAC layer and provides reliable communication, while managing power saving and synchronizertion among nodes. Without relying on MAC-layer acknowledgments, the protocol implements caching and network-layer retransmissions, triggered upon detection of a link fai…

QoS-Aware Fault Detection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS re…

Reliable Data Gathering in Tree-Based IEEE 802.15.4 Wireless Sensor Networks

This paper describes a routing protocol for enhanced robustness in IEEE 802.15.4-based sensor networks, which also addresses typical MAC layer issues, including power management, synchronization and link reliability. The algorithm uses a single-path strategy in error-free scenarios and resorts to using alternative paths when communication errors are detected. Our proposal exploits implicit acknowledgement of reception, a feature which may be provided by data aggregation when a broadcast medium such as the wireless channel is used. Therefore MAC layer acknowledgements are not used and errors recovery relies on a caching and retransmission strategy. The protocol requires synchronization among…

Notice of Violation of IEEE Publication Principles: Distributed Multimedia Digital Libraries on Peer-to-Peer Networks

This paper presents an original approach to image sharing in large, distributed digital libraries, in which a user is able to interactively search interesting resources by means of content-based image retrieval techniques. The approach described here addresses the issues arising when the content is managed through a peer-to-peer architecture. In this case, the retrieval facilities are likely to be limited to queries based on unique identifiers or small sets of keywords, which may be quite inadequate, so we propose a novel algorithm for routing user queries that exploits compact representations of multimedia resources shared by each peer in order to dynamically adapt the network topology to …

A Structural Approach to Infer Recurrent Relations in Data

Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word "knowledge" refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge emb…

A Networking Framework for Multi-Robot Coordination

Autonomous robots operating in real environments need to be able to interact with a dynamic world populated with objects, people, and, in general, other agents. The current generation of autonomous robots, such as the ASIMO robot by Honda or the QRIO by Sony, has showed impressive performances in mechanics and control of movements; moreover, recent literature reports encouraging results about the capability of such robots of representing themselves with respect to a dynamic external world, of planning future actions and of evaluating resulting situations in order to make new plans. However, when multiple robots are supposed to operate together, coordination and communication issues arise; w…