Reactive maintenance (RM) is a core service of the operation and maintenance (O&M) phase, the most prolonged and costly within the building lifecycle. RM is characterised by inefficient asset information and communication management, impacting critical FM problems and users’ experience. Building information modelling (BIM) and Internet of things (IoT) has enabled the development of digital twins, moving facilities management (FM) from a reactive approach towards a predictive one. Although previous studies have investigated the application of such technologies to FM, there is a lack of understanding on procedural issues related to its implementation in FM and RM. This research aimed to characterise strategies and decisions involved in prototyping a BIM and IoT-based smart-lighting maintenance system and identify its potential impacts on universities’ maintenance processes. The adopted research strategy and data collection methods involved prototyping, questionnaires, and interviews. The results show a high level of complexity in converging maintenance needs and technological abilities for FM and the importance of procedures and standards at organisational and industry levels. Moreover, it evidenced that the automation of functions and the centralisation of information enabled by BIM and IoT can optimise service provision, generate environmental and efficiency gains, and improve users’ safety and satisfaction.

The spatial analysis of social media data has recently emerged as a significant source of knowledge for urban studies. Most of these analyses are based on an areal unit that is chosen without the support of clear criteria to ensure representativeness with regard to an observed phenomenon. Nonetheless, the results and conclusions that can be drawn from a social media analysis to a great extent depend on the areal unit chosen, since they are faced with the well-known Modifiable Areal Unit Problem. To address this problem, this article adopts a data-driven approach to determine the most suitable areal unit for the analysis of social media data. Our multicriteria optimization framework relies on the Pareto optimality to assess candidate areal units based on a set of user-defined criteria. We examine a case study that is used to investigate rainfall-related tweets and to determine the areal units that optimize spatial autocorrelation patterns through the combined use of indicators of global spatial autocorrelation and the variance of local spatial autocorrelation. The results show that the optimal areal units (30 km² and 50 km²) provide more consistent spatial patterns than the other areal units and are thus likely to produce more reliable analytical results.

Several market sectors are attracted by the potential of unmanned aerial vehicles (UAVs), such as delivery, agriculture, and cinema, among others. UAVs are becoming part of Internet of Things (IoT) networks in the development of autonomous and scalable solutions. However, these vehicles are gradually becoming attractive targets for cyberattacks. This study proposes the development of an efficient platform based on the Message Queuing Telemetry Transport (MQTT) protocol for UAV control and Denial-of-Service (DoS) detection embedded in the UAV system. For the efficiency test, latency, network and memory consumption on the platform were measured, in addition to the correlation between payload and delay time. The results of efficiency tests were collected for the three levels of quality of service (QoS). A strong correlation greater than 90% was found between delay and data size for all QoS levels, showing almost a linear proportion. In DoS detection, the best results were a true positive rate (TPR) of 0.97 with 16 features from the AWID2 dataset using LightGBM with Bayesian optimization and data balancing. Unlike other studies, the built platform shows efficiency for UAV control and guarantees security in the communication with the broker and in the Wi-Fi UAV network.

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients’ needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.