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The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments enables several important applications, including the continuous monitoring of fragile subjects in their homes for healthcare systems. The majority of the approaches in the literature assume that only one resident is living in the home. Multi-inhabitant ADLs recognition is significantly more challenging, and only a limited effort has been devoted to address this setting by the research community. One of the major open problems is called data association, which is correctly associating each environmental sensor event (e.g., the opening of a fridge door) with the inhabitant that actually triggered it. Moreover, existing multi-inhabitant approaches rely on supervised learning, assuming a high availability of labeled data. However, collecting a comprehensive training set of ADLs (especially in multiple-residents settings) is prohibitive. In this work, we propose MICAR: a novel multi-inhabitant ADLs recognition approach that combines semi-supervised learning and knowledge-based reasoning. Data association is performed by semantic reasoning, combining high-level context information (e.g., residents' postures and semantic locations) with triggered sensor events. The personalized stream of sensor events is processed by an incremental classifier, that is initialized with a limited amount of labeled ADLs. A novel cache-based active learning strategy is adopted to continuously improve the classifier. Our results on a dataset where up to 4 subjects perform ADLs at the same time show that MICAR reliably recognizes individual and joint activities while triggering a significantly low number of active learning queries. [ABSTRACT FROM AUTHOR]

One of the major open problems in sensor-based Human Activity Recognition (HAR) is the scarcity of labeled data. Among the many solutions to address this challenge, semi-supervised learning approaches represent a promising direction. However, their centralized architecture incurs in the scalability and privacy problems that arise when the process involves a large number of users. Federated learning (FL) is a promising paradigm to address these problems. However, the FL methods that have been proposed for HAR assume that the participating users can always obtain labels to train their local models (i.e., they assume a fully supervised setting). In this work, we propose FedAR: a novel hybrid method for HAR that combines semi-supervised and federated learning to take advantage of the strengths of both approaches. FedAR combines active learning and label propagation to semi-automatically annotate the local streams of unlabeled sensor data, and it relies on FL to build a global activity model in a scalable and privacy-aware fashion. FedAR also includes a transfer learning strategy to fine-tune the global model on each user. We evaluated our method on two public datasets, showing that FedAR reaches recognition rates and personalization capabilities similar to state-of-the-art FL supervised approaches. As a major advantage, FedAR only requires a very limited number of annotated data to populate a pre-trained model and a small number of active learning questions that quickly decrease while using the system, leading to an effective and scalable solution for the data scarcity problem of HAR. [ABSTRACT FROM AUTHOR]

Ambient intelligence systems would benefit from the possibility of assessing quality and reliability of context information based on its derivation history, named provenance. While various provenance frameworks have been proposed in data management, context data have some peculiar features that claim for a specific support. However, no provenance model specifically targeted to context data has been proposed till the time of writing. In this paper, we report an initial investigation of this challenging research issue by proposing a provenance model for data acquired and processed in ambient intelligence systems. Our model supports representation of complex derivation processes, integrity verification, and a shared ontology to facilitate interoperability. The model also deals with uncertainty and takes into account temporal aspects related to the quality of data. We experimentally show the impact of the provenance model in terms of increased dependability of a sensor-based smart-home infrastructure. We also conducted experiments to evaluate the communication and computational overhead introduced to support our provenance model, using sensors and mobile devices currently available on the market. [ABSTRACT FROM AUTHOR]

major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service. [ABSTRACT FROM AUTHOR]

Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module. [ABSTRACT FROM AUTHOR]

The ubiquitous computing scenario is characterized by heterogeneity of devices used to access services, and by frequent changes in the user’s context. Hence, adaptation according to the user’s context and the used devices is necessary to allow mobile users to efficiently exploit Internet-based services. In this paper, we present a distributed framework, named MIMOSA, that couples a middleware for context-awareness with an intermediary-based architecture for content adaptation. MIMOSA provides an effective and efficient solution for the adaptation of Internet services on the basis of a comprehensive notion of context, by means of techniques for aggregating context data from distributed sources, deriving complex contextual situations from raw sensor data, evaluating adaptation policies, and solving possible conflicts. The middleware allows programmers to modularly build complex adaptive services starting from simple ones, and includes tools for assisting the user in declaring her preferences, as well as mechanisms for detecting incorrect system behaviors due to a wrong choice of adaptation policies. The effectiveness and efficiency of MIMOSA are shown through the development of a prototype adaptive service, and by extensive experimental evaluations. [ABSTRACT FROM AUTHOR]

This paper presents a formal framework to represent and manage advanced policy rules, which incorporate the notions ofprovisionandobligation. Provisions are those conditions that need to be satisfied or actions that must be performed by a user or an agent before a decision is rendered, while obligations are those conditions or actions that must be fulfilled by either the user or agent or by the system itself within a certain period of time after the decision. This paper proposes a specific formalism to express provisions and obligations within a policy and investigates a reasoning mechanism within this framework. A policy decision may be supported by more than one rule-based derivation, each associated with a potentially different set of provisions and obligations (called a global PO set). The reasoning mechanism can derive all the global PO sets for each specific policy decision and facilitates the selection of the best one based on numerical weights assigned to provisions and obligations as well as on semantic relationships among them. The formal results presented in the paper hold for many applications requiring the specification of policies, but this paper illustrates the use of the proposed policy framework in the security domain only. [ABSTRACT FROM AUTHOR]

In a workflow system, autonomous agents perform various activities cooperatively to complete a common task. Successful completion of the task often depends on correct synchronization and scheduling of agents' activities. It would greatly enhance the capabilities of current workflow systems if quantitative temporal constraints on the duration of activities and their synchronization requirements can be specified and reasoned about. This paper investigates such requirements and related reasoning algorithms. In particular, the paper studies the consistency, prediction and enactment services in a workflow system, and provides corresponding algorithms. The consistency service is to ensure that the specification of the temporal constraints is possible to satisfy; the prediction service is to foretell the time frame for the involved activities; and the enactment service is to schedule the activities so that, as long as each agent starts and finishes its task within the specified time period, the overall constraints will always be satisfied. For the enactment service, the paper identifies two practically interesting families of enactment schedules for autonomous agents, namely “free schedules” and “restricted due-time schedules”. In a free schedule, an agent may use any amount of time to finish the task as long as it is between the minimum and maximum time declared by the agent when the workflow is designed. A restricted due-time schedule is a more restrictive one in which the maximum amount of time that an agent may use is limited to a smaller number than the declared maximum. The paper presents efficient algorithms to find free and restricted due-time schedules. The paper also provides algorithms for the above services when multiple time granularities are involved in the temporal constraint specification. [ABSTRACT FROM AUTHOR]

In the recent literature on time representation, an effort has been made to characterize the notion of time granularity and the relationships between granularities. The main goals are having a common framework for their specification, and allowing the interoperability of systems adopting different time granularities. This paper considers the mathematical characterization of finite and periodic time granularities, and investigates the requirements for a user-friendly symbolic formalism that could be used for their specification. Instead of proposing yet another formalism, the paper analyzes the expressiveness of known symbolic formalisms for the representation of granularities, using the mathematical characterization as a reference model. Based on this analysis, a significant extension to the collection formalism defined in [15] is proposed, in order to capture a practically interesting class of periodic granularities. [ABSTRACT FROM AUTHOR]

This paper presents a general framework to define time granularity systems. We identify the main dimensions along which different systems can be characterized, and investigate the formal relationships among granularities in these systems. The paper also introduces the notion of a network of temporal constraints with (multiple) granularities emphasizing the semantic and computational differences from constraint networks with a single granularity. Consistency of networks with multiple granularities is shown to beNP hard in general and approximate solutions for this problem and for the minimal network problem are proposed. [ABSTRACT FROM AUTHOR]