This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. Index Terms-Building energy management, case-based reasoning (CBR), energy efficiency, multi-agent systems (MAS).
The current situation with COVID-19 is changing our courses of action toward ensuring health security. This is particularly crucial in airports, which usually receive more than 300,000 travellers in one single day. In this work, we present an Internet of Things (IoT) network to monitor the status of toilets and improve their maintenance. The system is based on IoT networks with different sensors to control soap levels, room capacity, distances, temperature, and humidity. This information is processed by a multi-agent system that detects possible anomalies and makes decisions accordingly. A case study in a real environment is developed in order to demonstrate the usefulness of the system. The results show that the proposed method can be used to successfully manage and control airport toilets.
Smart environments are pervasive computing systems that provide higher comfort levels on daily routines throughout interactions among smart sensors and embedded computers. The lack of privacy within these interactions can lead to the exposure of sensitive data. We present PRIPRO (PRIvacy PROfiles), a management tool that includes an Android application that acts on the user’s smartphone by allowing or blocking resources according to the context, in order to address this issue. Back-end web server processes and imposes a protocol according to the conditions that the user selected beforehand. The experimental results show that the proposed solution successfully communicates with the Android Device Administration framework, and the device appropriately reacts to the expected set of permissions imposed according to the user’s profile with low response time and resource usage.
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