NDS Research Group - Networking and Distributed Systems

Ambient Intelligence

Ambient Intelligence is a new paradigm in Artificial Intelligence that introduces a shift in perspective as regards the role of the end user. The basic intrinsic requirement of any AmI system is the presence of pervasive and unobtrusive sensory devices, which is essential to ensure context-aware reasoning in order to act upon the environment, modify its state, and react to user-driven stimuli. In our approach a remote, distributed sensory device thus acts as the termination of a centralized sentient reasoner, where actual intelligent processing occurs; sensed data is processed in order to extract higher-level information, carrying on symbolic reasoning on the inferred concepts, and producing the necessary actions to adapt the environment to the users’ requirements. A set of actuators finally takes care of putting the planned modifications to the environment state into practice.

Related Publications

A Smart Assistant for Visual Recognition of Painted Scenes. F. Concone, R. Giaconia, G. Lo Re, M. Morana. In Joint Proceedings of the ACM IUI 2021 Workshops (ACMIUI-WS 2021), College Station, United States, April 13-17, 2021. CEUR-WS, Vol. 2903
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Smart Auctions for Autonomic Ambient Intelligence Systems. A. Bordonaro, A. De Paola, G. Lo Re, M. Morana. In Proceedings of the 2020 IEEE International Conference on Smart Computing (SMARTCOMP)
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The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular, the combination of smart contracts and blockchains enables AmI systems to autonomously communicate with untrusted entities and complete secure transactions without the brokering of a trusted third party. The proposed approach has been adopted to design a sample AmI application capable of managing requests from faulty devices in a Smart home.
SMCP: a Secure Mobile Crowdsensing Protocol for fog-based applications F. Concone, G. Lo Re, M. Morana. In Journal of Human-centric Computing and Information Sciences (HCIS 2020)
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The possibility of performing complex data analysis through sets of cooperating personal smart devices has recently encouraged the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis towards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. Unfortunately, because both of their distributed nature and high degree of modularity, edge-fog-cloud computing systems are particularly prone to cyber security attacks that can be performed against every element of the infrastructure. In order to address this issue, in this paper we present SMCP, a Secure Mobile Crowdsensing Protocol for fog-based applications that exploit lightweight encryption techniques that are particularly suited for low-power mobile edge devices. In order to assess the performance of the proposed security mechanisms, we consider as case study a distributed human activity recognition scenario in which machine learning algorithms are performed by users? personal smart devices at the edge and fog layers. The functionalities provided by SMCP have been directly compared with two state-of-the-art security protocols. Results show that our approach allows to achieve a higher degree of security while maintaining a low computational cost.
A fog-based hybrid intelligent system for energy saving in smart buildings. A. De Paola, P. Ferraro, G. Lo Re, M. Morana, M. Ortolani. In Journal of Ambient Intelligence and Humanized Computing (JAIHC)
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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, to constantly improve its performance by learning users? needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users? needs and preferences.
Smart Assistance for Students and People Living in a Campus. S. Gaglio, G. Lo Re, M. Morana, C. Ruocco. In Proceedings of the 2019 IEEE International Conference on Smart Computing (SMARTCOMP)
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A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices. F. Concone, G. Lo Re, M. Morana. In ACM Transactions on Internet Technology
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The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenario in which such devices, e.g., smartphones, tablet computers, or activity trackers can be exploited to infer relevant information is Human Activity Recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, i.e., when complex activities need to be analysed timely, users? smart devices can operate as part of a more complex architecture. In this paper we propose a multi-device HAR framework that exploits the fog computing paradigm to move heavy computation from the sensing layer to intermediate devices, and then to the cloud. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices, while also reducing the overall bandwidth consumption. Experimental analysis aims to evaluate the performance of entire platform in terms of accuracy of the recognition process, while also highlighting the benefits it might bring in smart environments.
Intelligent Systems for Smart Building Management. A. De Paola. In Proceedings of Intelligent Environments 2018
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Managing smart buildings is a challenging task, particularly in presence of contrasting goals, such as satisfying users? needs and reducing the energy consumption. Artificial Intelligence allows to design smart buildings really capable of proactively support the users to reach their goals. Intelligent systems should be capable of exploiting the information gathered by sensors pervading the building, understanding the context, selecting the best actions to perform, and actively modifying the environment. The design of such systems is a complex task, because of the possibly wide set of functional and non-functional requirements, and the dependences among intelligent functionalities and their embodiment in the building?s cyber physical space.
A Gesture Recognition Framework for Exploring Museum Exhibitions. V. Agate, S. Gaglio. In Proceedings of the International Conference on Advanced Visual Interfaces (AVI 2018)
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In this paper we present a gesture recognition framework for providing the visitors of a museum exhibition with a non intrusive interface for the multimedia enjoyment of digital contents. Early experiments were carried out at the Computer History Museum Exhibition of the University of Palermo.
A Cognitive Architecture for Ambient Intelligence Systems. V. Agate, P. Ferraro, S. Gaglio. In Proceedings of the 6th International Workshop on Artificial Intelligence and Cognition (AIC 2018)
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Nowadays, the use of intelligent systems in homes and work- places is a well-established reality. Research efforts are moving towards increasingly complex Ambient Intelligence (AmI) systems that exploit a wide variety of sensors, software modules and stand-alone systems. Un- fortunately, using more data often comes at a cost, both in energy and computational terms. Finding the right trade-off between energy savings, information costs and accuracy of results is a major challenge, especially when trying to integrate many heterogeneous modules. Our approach fits into this scenario by proposing an ontology-based AmI system with a cognitive architecture, able to perceive the state of the surrounding environment, to reason on the current situation and act accordingly to modify the state of the environment based on the user?s preferences.
Towards a Smart Campus through Participatory Sensing. F. Concone, P. Ferraro, G. Lo Re. In Proceedings of the 4th IEEE International Conference on Smart Computing (SMARTCOMP 2018)
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In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smart- phones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus ?smart?. In particular, our system infers the activities performed by users (e.g., students) in a campus in order to identify trends and behavioral patterns. This information allows the system to decide in real-time which actions are needed to provide the best possible services to users, according to their needs and preferences. Experimental results underline the benefits that the system might bring in a smart campus.
Smart Services for an Augmented Campus. V. Agate, F. Concone, P. Ferraro. In Proceedings of the 4th IEEE International Conference on Smart Computing (SMARTCOMP 2018)
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Technological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users? lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users? smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones.
A Context-aware System for Ambient Assisted Living. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re, M. Morana, M. Ortolani, D. Peri. In Proceedings of the 11th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2017)
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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 anomalies in user?s behavior or health condition, in order to send appropriate alerts to family members and caregivers. Experimental results performed on a simulated smart home prove the effectiveness of the proposed system.
An Ambient Intelligence System for Assisted Living. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re, M. Morana, M. Ortolani, D. Peri. In Proceedings of the International Annual Conference of AEIT (2017)
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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 able to detect and predict anomalies in such data, sending appropriate alerts to caregivers and family members. The effectiveness of the proposed AAL system is demonstrated by extensive experimental results carried out in a simulated smart home.
Smartphone Data Analysis for Human Activity Recognition. F. Concone, S. Gaglio, G. Lo Re, M. Morana. In Proceedings of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017)
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In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the user?s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a participatory sensing paradigm where user?s feedbacks on recog- nised activities are exploited to update the inner models of the system. Experimental results show the effectiveness of our solution as compared to other state-of-the-art techniques.
Context-Awareness for Multi-Sensor Data Fusion in Smart Environments. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re. In Proceedings of the 15th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2016)
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Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic inference systems by including context information, and proves the suitability of such an approach in the application scenario of user activity recognition in a smart home environment. However, the selection of the most convenient set of context information to be considered is not a trivial task. To this end, we carried out an extensive experimental evaluation which shows that choosing the right combination of context information is fundamental to maximize the inference accuracy.
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re, S. Das. In IEEE Transactions on Mobile Computing
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The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the inference process. At the highest tier, a self-optimization process dynamically reconfigures the sensory infrastructure, by sampling a subset of sensors in order to minimize energy consumption and maximize inference accuracy. A Bayesian approach allows to deal with the imprecision of sensory measurements, due to environmental noise and possible hardware malfunctions. The effectiveness of our approach is demonstrated with the application scenario of the user activity recognition in an Ambient Intelligence system managing a smart home environment. Experimental results show that the proposed solution outperforms static approaches for context-aware multi-sensor fusion, achieving substantial energy savings whilst maintaining a high degree of inference accuracy.
A Machine Learning Approach for User Localization Exploiting Connectivity Data. P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani. In Journal of Engineering Applications of Artificial Intelligence (Elsevier)
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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 connectivity information. Performance of our approach has been thoroughly assessed based on data collected in simulation as well as in actual deployment.
SmartBuildings: an AmI System for Energy Efficiency. A. De Paola, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 4th International Conference on Sustainable Internet and ICT for Sustainability, 2015
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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 realized addressing two different scenarios, i.e. the management of a campus and of a manufacturing facility. A complete description of the elements included in the case study is presented.
Human Activity Recognition Process Using 3-D Posture Data. S. Gaglio, G. Lo Re, M. Morana. In Human-Machine Systems, IEEE Transactions on, vol.45, no.5, pp.586-597
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In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields, and Eigenjoints, respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real time show promise for applied use.
Autonomic Behaviors in an Ambient Intelligence System. A. De Paola, P. Ferraro, S. Gaglio, G. Lo Re. In Proceedings of the 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (IEEE SSCI 2014)
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Ambient Intelligence (AmI) systems are constantly evolving and becoming ever more complex, so it is increasingly difficult to design and develop them successfully. Moreover,because of the complexity of an AmI system as a whole, it is not always easy for developers to predict its behavior in the event of unforeseen circumstances. A possible solution to this problem might lie in delegating certain decisions to the machines themselves, making them more autonomous and able to self-configure and self-manage, in line with the paradigm of Autonomic Computing. In this regard, many researchers have emphasized the importance of adaptability in building agents that are suitable to operate in real-world environments, which are characterized by a high degree of uncertainty. In the light of these considerations, we propose a multi-tier architecture for an autonomic AmI system capable of analyzing itself and its monitoring processes, and consequently of managing and reconfiguring its own sub-modules to better satisfy users? needs. To achieve such a degree of autonomy and self-awareness, our AmI system exploits the knowledge contained in an ontology that formally describes the environment it operates in, as well as the structure of the system itself.
User activity recognition for energy saving in smart homes. P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani. In Journal of Pervasive and Mobile Computing
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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 consumption within a predefined range, thus minimizing the impact on the end users. In order to build the models, the inherent re- cursive structure of user activities is abstracted from raw sensor readings, via an approach based on information theory. Experimental assessment based on publicly available datasets and synthesized consumption models is provided to show the effectiveness of our proposal.
Sensor Networks for Energy Sustainability in Buildings. A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das. In Sensor Networks for Sustainable Development, 2014, pp. 107-122, ISBN: 978-1-4665-8206-4, DOI: 10.1201/b17124-10
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The topic of energy saving in buildings is increasingly raising the interest of researchers for its practical outcomes in terms of economic advantages, and long-term environmental sustainability. Many sensory devices are currently available that allow precise monitoring of every physical quantity; in particular it is possible to obtain estimates of energy consumption which can be used to enact proper energy saving strategies. Such devices may be considered part of a complex sensor infrastructure permeating the whole site of interest, which may be characterized by the adopted protocols and architectural models.This work provides a comprehensive review of the current literature about sensory devices for energy consumption measurement, and global architectures for implementing energy saving in buildings.
Intelligent Management Systems for Energy Efficiency in Buildings: A Survey. A. De Paola, M. Ortolani, G. Lo Re, G. Anastasi, S.K. Das. ACM Computing Surveys
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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.
User Activity Recognition via Kinect in an Ambient Intelligence Scenario. P. Cottone, G. Maida, M. Morana. In Proceedings of 2013 International Conference on Applied Computing, Computer Science, and Computer Engineering
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The availability of an ever-increasing kind of cheap, unobtrusive, sensing devices has stressed the need for new approaches to merge raw measurements in order to realize what is happening in the monitored environment. Ambient Intelligence (AmI) techniques exploit information about the environment state to adapt the environment itself to the users' preferences. Even if traditional sensors allow a rough understanding of the users' preferences, ad-hoc sensors are required to obtain a deeper comprehension of users' habits and activities. In this paper we propose a framework to recognize users' activities via a depth and RGB camera device, namely the Microsoft Kinect. The proposed approach takes advantage of the position of relevant human body joints estimated by using Kinect depth information. In our system, significant configurations of joints positions (i.e., postures) are discovered by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, each activity is modeled by Hidden Markov Models (HMMs) as a sequence of known postures. In order to maintain a high level of pervasiveness, a real prototype has been implemented by connecting the Kinect sensor to a miniature computer with limited computational resources. Experimental tests have been performed on a dataset we collected at our laboratory and results look very promising.
User Activity Recognition for Energy Saving in Smart Homes. P. Cottone, S. Gaglio, G. Lo Re, M. Ortolani. In Proceedings of the 3rd International Conference on Sustainable Internet and ICT for Sustainability, 2013, pp. 1-9
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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.
Gesture Recognition for Improved User Experience in a Smart Environment. S. Gaglio, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the thirteenth International Conference on Advances in Artificial Intelligence
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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 motion sensor device are classified according to a supervised approach based on a probabilistic support vector machine, and fed into a stochastic syntactic classifier which will interpret them as the basic symbols of a probabilistic gesture language. We will show how this approach is suitable to cope with the intrinsic imprecision in source data, while still providing sufficient expressivity and ease of use.
Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario. G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 3rd International Conference on Pervasive and Embedded Computing and Communication Systems, 2013
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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 state of the detected hand. The shape of the hand is interpreted as one of the basic symbols of a grammar expressing a set of commands for the actuators of the AmI system. In order to maintain a high level of pervasiveness, the Kinect sensor is connected to a miniature computer capable of real-time processing.
Motion Sensors for Activity Recognition in an Ambient-Intelligence Scenario. P. Cottone, G. Lo Re, G. Maida, M. Morana. In Proocedings of the 5th International Workshop on Smart Environments and Ambient Intelligence, 2013, pp. 646-651
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In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is based on the estimation of some relevant joints of the human body by using Kinect depth information. The most significative configurations of joints positions are combined by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, Hidden Markov Models (HMMs) are applied to model each activity as a sequence of known postures. The proposed solution has been tested on a public dataset while considering four different configurations corresponding to some state-of-the-art approaches and results are very promising. Moreover, in order to maintain a high level of pervasiveness, we implemented a real prototype by connecting Kinect sensor to a miniature computer capable of real-time processing.
Ambient Intelligence for Energy Efficiency in a Building Complex. G. Lo Re, M. Ortolani, G. Anastasi. In ERCIM NEWS, 90, pp, 36-37
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An intelligent system for energy efficiency in a complex of buildings. A. De Paola, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 2nd International Conference on Sustainable Internet and ICT for Sustainability, 2012, pp. 1-5
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Energy efficiency has nowadays become one of the most challenging task for both academic and commercial organizations, and this has boosted research on novel fields, such as Ambient Intelligence. In this paper we address the issue of timely and ubiquitous monitoring of building complexes in order to optimize their energy consumption, and present an intelligent system addressed to the typical end user, i.e. the administrator, or responsible operator, of the complex. A three-level architecture has been designed for detecting the presence of the building inhabitants user and understanding their preferences with respect to the environmental conditions in order to optimize the overall energy efficiency of the buildings. Wireless Sensor and Actuator Networks (WSAN) are used to remotely monitor and control the environment according to the decisions made by a centralized reasoner. A case study derived from an actual implementation of the system regarding the management of a university building is also described.
Sensor 9k: A testbed for designing and experimenting with WSN-based ambient intelligence applications. A. De Paola, S. Gaglio, G. Lo Re, M. Ortolani. In Pervasive and Mobile Computing, vol. 8, issue 3, 2012, pp. 448-466
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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.
User detection through multi-sensor fusion in an AmI scenario. A. De Paola, M. La Cascia, G. Lo Re, M. Morana, M. Ortolani. In Proceedings of the 15th International Conference on Information Fusion, 2012, pp. 2502-2509
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Recent advances in technology, with regard to sensing and transmission devices, have made it possible to obtain continuous and precise monitoring of a wide range of qualitatively diverse environments. This has boosted the research on the novel field of Ambient Intelligence, which aims at exploiting the information about the environment state in order to adapt it to the user's preference. In this paper, we analyze the issue of detecting the user's presence in a given region of the monitored area, which is crucial in order to trigger subsequent actions. In particular, we present a comprehensive framework that turns data perceived by sensors of different nature, and with possible imprecision, into higher-level information; a case study derived from an actual implementation of the system regarding the management of an office environment is also described, and experimental results are presented.
Cognitive meta-learning of syntactically inferred concepts. S. Gaglio, G. Lo Re, M. Ortolani. In Frontiers in Artificial Intelligence and Applications, 2011, pp. 118-123
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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.
Adaptable data models for scalable ambient intelligence scenarios. A. De Paola, G. Lo Re, F. Milazzo, M. Ortolani. In Proceedings of the International Conference on Information Networking (ICOIN), 2011, pp. 80-85
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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.
Sensor mining for user behavior profiling in intelligent environments. A. Augello, M. Ortolani, G. Lo Re, S. Gaglio. In Advances in Distributed Agent-Based Retrieval Tools, 2011, pp. 143-158
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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.
Exploiting the human factor in a WSN-based system for ambient intelligence. A. De Paola, A. Farruggia, S. Gaglio, G. Lo Re, M. Ortolani. In Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, 2009. CISIS '09, pp. 748-753
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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.
Human-ambient interaction through wireless sensor networks. A. De Paola, S. Gaglio, G. Lo Re, M. Ortolani. In Proceedings of the 2nd Conference on Human System Interactions, 2009. HSI '09, pp. 64-67
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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 both implicit and explicit feedback from the users.
An ambient intelligence architecture for extracting knowledge from distributed sensors. A. De Paola, G. Lo Re, S. Gaglio, M. Ortolani. In Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, 2009, pp. 104-109
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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 reasoning based on them. The same tiered architecture is replicated in order to provide further levels of abstraction.
WSNs for structural health monitoring of historical buildings. G. Anastasi, G. Lo Re, M. Ortolani. In Proceedings of the 2nd Conference on Human System Interactions, 2009. HSI '09, pp. 574-579
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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.
Knowledge extraction from environmental data through a cognitive architecture S. Gaglio, L. Gatani, G. Lo Re, M. Ortolani. In Innovations in Hybrid Intelligent Systems, 2007, pp. 329-336
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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. The features of the proposed approach are illustrated through the description of a sample application for wildfire detection.