The increasing interest in searching for intelligent, proactive, and autonomous environments leads to the necessity of accessing contextual knowledge: knowledge about changes that occur in the environment and even about the capabilities of the devices that compose a specific deployment. This knowledge would allow carrying out commonsense reasoning (exhibit human-like comprehension) according to the current situation, enabling decision making and task planning. One of the most used forms of knowledge specification is ontologies, but ontological knowledge modeling is usually a manual process, making it a costly task. In addition, ontologies can be found that have been designed for specifying knowledge about IoT-related concepts, but many are overly complex or do not have an adequate orientation that allows modeling the system’s capabilities. There are many different types of reasoners, but Answer Set Solvers are of particular interest for commonsense reasoning. This paper proposes an ontology to represent contextual knowledge about IoT device capabilities, and an Answer Set Programming-based reasoner for the automatic generation of this knowledge. The main challenge is to demonstrate that contextual knowledge can be generated through commonsense reasoning processes implemented with Answer Set Programming, and that the resulting knowledge can be used for decision making (also through commonsense reasoning). This work shows how the generated knowledge is correct through different use cases, presents an application example that demonstrates the benefits of using the generated knowledge, and analyzes the reasoner performance to demonstrate that the execution time is adequate.

The SOSAc-Reasoner is a commonsense reasoning engine, implemented using Answer Set Programming. It is designed to automatically generate IoT context knowledge, representing the capabilities of system devices, from a simple smart scenario description. The inference engine is fed with knowledge about device types and generates knowledge according to two ontologies derived from the SOSA (Sensor, Observation, Sample, and Actuator) ontology. The SOSAc-Reasoner comprises two ASP rule modules: the basic and advanced inference modules, which perform reasoning with different objectives. Implemented with Potassco, the SOSAc-Reasoner effectively generates context knowledge within a reasonable timeframe. This significantly facilitates the task of modeling a highly valuable type of knowledge in intelligent environments, a task that traditionally involves manual efforts, is prone to errors, and consumes a significant amount of time.

Occupancy information in indoor spaces is playing an increasingly important role in the development of smart applications. The need for this type of information covers a multitude of domains in the Smart Buildings paradigm such as improving energy saving or occupant comfort. For this reason, we can find many works in the literature focused on occupancy tracking/monitoring using solutions based on RGB cameras and computer vision techniques, sensors and machine learning techniques, or air quality control, among others. But these solutions have limitations. Some of them do not support the tracking of people between spaces, the time to update information is too long, or the system used is too intrusive. This paper presents a solution to estimate the occupancy level in indoor spaces of different areas through depth cameras. This approach also proposes the integration of neural networks to deal with situations where the data collected from the environment is incomplete, filling the gaps caused by occlusion or performance problems. Finally, an occupancy service has been designed and deployed in order to provide occupancy information to other applications, such as evacuation services. The experiments carried out show how it is possible to obtain an accuracy of 90.20% through this approximation. In addition, we face some of the limitations mentioned above: the solution allows tracking movement and occupancy in large spaces without (1) lighting dependencies and (2) the requirement for users to wear devices. This, and the high accuracy obtained make the proposed work a great alternative for occupancy estimation in indoor spaces.

The "smart" behaviour that characterises commercial Smart Home systems is due to the relations that users manually establish between devices and actions through automation rules. Therefore, the intelligence of these systems is entirely (or mostly) predefined by humans. To address this problem, several Artificial Intelligence techniques have been applied in this field to try to improve the smartness of Smart Home systems by enhancing features such as self-adaptation, self-evolution and self-awareness. This work in progress article presents an approach to provide smart homes with these features through a reasoning system able to deduce, define, monitor and update the automation rules that drive their behaviour, in an autonomous manner and with a knowledge management at different levels of abstraction. That is to say, the rules normally defined by humans are now created and modified automatically. The proposal is based in a commonsense knowledge model that represents how the world works and enables to infer what resources can be used to accomplish a specific objective. Automations are generated according to general, commonsense and context (topology, users, sensors, etc.) knowledge, and they are more or less sophisticated depending on the available devices. The set of behavioural rules is updated when any of these resources change. Scone is the high-performance and open-source knowledge base system where the knowledge model has been implemented. Finally, to evaluate and demonstrate the potential of the model, a set of automation rules is built for a concrete use case. Results are shown in a virtual smart home on the Home Assistant platform.

The fields of robotics and game consoles offer an interesting and broad range of lab platforms with appropriate characteristics for teaching Computer Architecture concepts. This work analyzes the impact of one approach based on game consoles and another one based on robotics from a triple dimension: student motivation, acquired knowledge, and perception of the employed platform. The study has been carried out on a sample of 96 students using the Arduino-based robot and 75 students using the Nintendo-DS console. A mixed methodology is employed encompassing quantitative and qualitative approaches. Five instruments are used to measure the three aforementioned dimensions. Results show that despite both platforms performing similarly in the three considered dimensions (student motivation, acquired knowledge, and perception of the employed platform), the robotics platform does it slightly better than game console, based on the obtained average scores for the considered instruments. Despite this outperforming, motivation and perception decrease for the students using the robotics platform as result of some identified constraint. This suggests that changes are required in the organization of the lab sessions to promote teamwork skills and to overcome the lack of simulators to remove the obstacles hinting motivation and performance. However, a clear correlation between motivation and perception and acquired knowledge has not been identified on computer architecture. Implications of affordances and constraints of both platforms, types of activities, and their impact on results have been discussed.

Traditionally, the standards of spatial modeling are oriented to represent the quantitative information of space. However, in recent years an increasingly common challenge is appearing: flexibly and appropriately integrating quantitative information that goes beyond the purely geometric. This problem has been aggravated due to the success of new paradigms such as the Internet of Things. This adds an additional challenge to the representation of this information due to the need to represent characteristic information of the space from different points of view in a model, such as WiFi coverage, dangerous surroundings, etc. While this problem has already been addressed in indoor spaces with the IndoorGML standard, it remains to be solved in outdoor and indoor–outdoor spaces. We propose to take the advantages proposed in IndoorGML, such as cellular space or multi-layered space model representation, to outdoor spaces in order to create indoor–outdoor models that enable the integration of heterogeneous information that represents different aspects of space. We also propose an approach that gives more flexibility in spatial representation through the integration of standards such as OpenLocationCode for the division of space. Further, we suggest a procedure to enrich the resulting model through the information available in OpenStreetMap.

Framed within the PLATINO research project we have prototyped an energy-harvesting device specifically designed for supporting a set of smart farming applications. To this purpose, our prototype is equipped, among other components, with several sensors for environmental and energy conditions monitoring and a LoRa communication module to enable a Low-Power Wide Area Network. The physical network will be composed of dozens of PLATINO devices acting as end-nodes and of a drone with limited time of flight acting as a mobile gateway, which will receive the data temporally stored on the end-devices. This paper analyzes the set of constraints imposed by the European LoRa regulations and by the drone itself to design an efficient communication protocol between the drone and the end-devices.

Rankings are a valuable element for city-comparison purposes since results withdrawn from these comparisons can, eventually, support the evaluation of strategic decisions taken by cities. Smart city rankings are not an exception and, as they draw more attention, the number of them exponentially increases. This paper evaluates the appropriateness of existing smart city rankings for quantifying the materialization degree of the smart city concept. The analysis reveals that current rankings generally overlook indicators of the Information and Communication Technologies dimension. To bridge this gap, this work proposes a methodology based on Multiple-Attribute Decision Making that uses technological criteria for designing smart city rankings. The proposed methodology is evaluated against the cities of New York, Seoul, and Santander. Imbalances between results provided by the studied rankings and our evaluation are detected, which suggests the need for a new insight into more suitable and precise evaluation of the smartness degree of cities.

This research is intended to evaluate the suitability of a common-sense-based approach for providing causal explanations to power quality disturbances and, more specifically, to voltage-sage events, with the aim of improving the security and reliability of the electrical grid. Since voltage sags are one of the most common power-quality disturbance and may have a severe impact on sensitive loads and users, the case study presented is dedicated to find their causes as a prerequisite to prevent them. The main contribution presented in this work is in the knowledge management domain. However, the proposed architecture adopts a multi-layer approach that comprehensively faces all the necessary stages, comprising: measurement and data collection, filtering, distribution, homogenization, and integration up to the process of inference of the possible cause of the event. The architecture of the proposed system comprises three layers, namely: the information gathering, the information modeling and the information understanding layers. The proposed system has been evaluated using a synthetic dataset of simulated voltages sags. Information about weather conditions and different location features and environmental circumstances is aggregated to the electrical features of the voltage sag, in order to improve the inference of the external cause of the event. Even though a real-world dataset would be required to fully validate the proposed system and assess the benefits in a real scenario, the results obtained with the synthetic dataset are satisfactory and an overall accuracy above 90% of the cause identification is achieved.

This paper presents an architecture for smart buildings based on common-sense reasoning using the IndoorGML standard. The main objective is the construction of a knowledge model that allows inferring information that is not explicitly defined in that model. The main challenges addressed are: 1) the modeling of the concepts presented by the standard in the knowledge base used, known as Scone; 2) the automation of the introduction of knowledge in Scone with the proposed model; and 3) the implementation of a service-oriented architecture that makes possible to introduce knowledge in a transparent way. The semantic knowledge we present here, which shows more advanced and flexible capabilities, will make it possible in the future to carry out reasonings that not only depend on the structure of a building, but on many other aspects of the IoT such as sensors or actuators that are difficult to contemplate if a common sense approach is not followed.

This article presents an Internet of Things architecture for Smart Homes that specifically targets service composition and reconfiguration as enablers for the actuation and smart behavior capabilities. To this end, the main challenge that has to be addressed is the support to a seamless integration, composition, and reconfiguration of Internet of Things objects. Two enabling technologies are proposed here: a planning strategy based on a common-sense reasoning approach for service composition and a virtual-network protocol for Inter-Domain Messaging. The planner will identify the services that, properly connected, will cater for arisen, and therefore, unexpected needs. The virtual-network protocol will provide the support for this interconnection to take place in a transparent and orthogonal manner. This is particularly important to enable autonomous systems to instantiate composite services. To demonstrate the capabilities of the resulting framework, two use cases are presented, which under real circumstances demonstrate the potential of the proposed approach.

The Internet of Things (IoT) paradigm envisions a world of interconnected objects in which every object or thing can be univocally addressed and accessed, independently of its inherent technology and location. To the date, most of the state-of-the-art solutions proposed to realize the IoT vision employ a cloud-based approach. This means that, independently on where objects are physically located, their interconnection relies on a cloud-based solution. In a daily life situation it is like requesting a call-center operator to get us in contact to someone that is nearby us. Common sense tells us that it is not only simpler, but more efficient, to directly talk to him/her minimizing delays, failing points, and unnecessary intermediaries. This paper presents a protocol for IoT, named IDM (Inter-Domain Messaging), intended to overcome the limitations of cloud-based solutions for IoT. The IDM approach consists in offering an abstraction layer upon which different technologies can interact in a seamless and efficient manner. This paper also provides an experimental validation consisting in the implementation of a prototype interconnecting five different domains, with technologies like ZigBee, Bluetooth, RS485 or WiFi.

This paper proposes and analyzes the use of the Arduino Zero board as the lab platform for the Computer Structure course that constitutes an essential part of Computer Science studies. The understanding of the main functional blocks of a computer, addressing the main concepts included in the course syllabus, is reinforced by mean of the hands-on experience acquired in the lab sessions and the completion of a project based on a mobile robot. Special care has been devoted to link the theoretical concepts with their practical application. The inclusion of a debugging chip (EDBG) in the Arduino Zero board is one of the main assets to enable exploring the architecture and analyze the execution of programs down to the assembler instructions level.

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A commonsense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from"passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone. (C) 2016 Elsevier Ltd. All rights reserved.

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

In this paper a Commercial-off-the-Self (COTS) platform for Unmanned Aerial Vehicles (UAV) is presented. The goal of this work is to provide the industry with a flexible and efficient solution to smooth the integration and heterogeneous development challenges in this scenario. On one hand, the proposed platform enables transparent and efficient interaction with the control station implemented in ZeroC Ice. On the other hand, the proposed approach abstracts both embedded and desktop software developers from the platform details. A customized hardware-software layer assures a high-level, efficient reliable communication while a complete tool-chain automatizes the generation of application specific code, reducing the development time.

Smart environments, enabled by the Internet of Thing (IoT) paradigm, advocate for more intelligent and interconnected systems, electronic devices, tools, and appliances. While most efforts are nowadays addressed to provide connectivity or smartness to IoT devices, unfortunately, few have realised the importance of supporting automatic service composition and service reconfiguration capabilities at middleware level. Due to the openness that characterise such environments, the range of possible interactions and available services and devices cannot be totally defined nor prescribed in advanced. This uncertainty demands mechanisms to dynamically adapt existing systems, and their functionality, to address unforeseen needs. In order to do so, a general understanding of contexts, services, and device capabilities is needed. From that understanding, new responses can be automatically devised on run-time. This paper presents a semantic middleware specifically devised to support automatic and autonomous service reconfiguration and composition. The novelty consists in moving the semantics, traditionally held at the programming-interface level, to a common-sense knowledge-base system, with higher expressive power and reasoning capabilities.

The occurrence of emergency situations in high-rise buildings, daily hosting hundreds of people, may force the massive evacuation of their occupants with the ultimate goal of preventing the loss of lives. In this paper, we propose an adaptive algorithm for dynamically computing safe evacuation routes, while the load of people is balanced between the accesses of each floor of the building, thus avoiding the accumulation of people in hotspots. The combination of this algorithm with sensors to detect the events of interest and a navigation system to guide the evacuees turns this solution into a specially suited approach for evacuation in high-rise buildings. Simulation results demonstrate that the yield evacuation routes drive all people outdoor with a similar average path length for the different risk scenarios addressed.