Abstract
Commercial off-the-shelf devices and applications are expected to be pivotal in the coming massive deployment of pervasive computing technology in home settings. The integration of these devices and applications in the same household may result in unplanned interactions involving users and entertainment, communication, and health-related devices and applications. These unplanned interactions are a serious concern when, for example, communication or entertainment applications interfere with the behavior of health-related devices. This paper presents a novel graph-based approach for representing the expected behavior of commercial off-the-shelf devices and applications, their interactions, and for detecting interference in pervasive computing systems. A set of home care scenarios is used to assess the applicability of this approach. We then provide two setups where this approach can be applied: (i) in a pre-deployment setup, where simulation is used to detect possible instances of interference, and (ii) at run-time, collecting observations from devices and applications and detecting interference as it occurs. For pre-deployment and simulation we use Opensim to recreate a home household. For run-time, we use Simple Network Management Protocol for systems state introspection and a sliding window mechanism to process the collected data-stream. Crown Copyright

Abstract
Context-aware ubiquitous computing systems should be able to introspect the surrounding environment and adapt their behavior according to other existing systems and context changes. Although numerous ubiquitous computing systems have been developed that are aware of different types of context such as location, social situation, and available computational resources, few are aware of their computational behavior. Computational behavior introspection is common in reflective systems and can be used to improve the awareness and autonomy of ubicomp systems. In this paper, we propose a decentralized approach based on Simple Network Management Protocol (SNMP) and Universal Plug and Play (UPnP), and on state transition models to model and expose computational behavior. Typically, SNMP and UPnP are targeted to retrieve raw operational variables from managed network devices and consumer electronic devices, e.g., checking network interface bandwidth and automating device discovery and plug and play operations. We extend the use of these protocols by exposing the state of different ubicomp systems and associated state transitions statistics. This computational behavior may be collected locally or remotely from ubicomp systems that share a physical environment, and sent to a coordinator node or simply shared among ubicomp systems. We describe the implementation of this behavior awareness approach in a home health-care environment equipped with a VoIP Phone and a drug dispenser. We provide the means for exposing and using the behavior context in managing a simple home health-care setting. Our approach relies on a system state specification being provided by manufacturers. In the case where the specification is not provided, we show how it can be automatically discovered. We propose two machine learning approaches for automatic behavior discovery and evaluate them by determining the expected state graphs of our two systems (a VoIP Phone and a drug dispenser). These two approaches are also evaluated regarding the effectiveness of generated behavior graphs.

Abstract

Abstract

Abstract
Panoramic or aerial images can be acquired with some easiness and cover vast tracts of territory to be used in fire detection. The analysis of these images, in particular based on color and threshold indices, can be very interesting computationally when applied in real time systems and collected, for example, through drones or watchtowers. This paper presents a solution designated Color Algorithm for Flame Exposure (CAFE), which significantly improves an existing method (cf. Forest Fire Detection Index - FFDI) in flame detection, based on daylight images, in mixed Mediterranean landscape, containing vegetation, buildings, burning areas, land, etc. The CAFE approach, presented, adds a parameterizable transformation of the image into the Lab color space. This approach was tested in four distinct scenarios, significantly reducing false positives and maintaining an equivalent level of false negatives when compared to the FFDI approach.

Abstract
Location data plays an important role in several applications embedded in our digital living. These applications, usually, take advantage of Global Positioning System (GPS). However, GPS is not targeted for indoor location, therefore this paper presents an alternative system, based on Bluetooth Low Energy (BLE) beacons that together with bluetooth-enabled Smartphones, allows the development of low cost and accurate location-aware applications for indoor scenarios. The paper describes the challenges associated with the system deployment and presents algorithms to improve the distance estimation process as the user moves around the smart space. The evaluation performed shows that this approach has good results on noise reduction and movement adaptation allowing a close tracking of the indoor user position. © Springer International Publishing AG, part of Springer Nature 2018.