Given population growth and its increasing concentration in urban areas, traffic congestion increasingly affects various aspects of daily life. From long commutes to stressed workers, traffic congestion reduces mobility, limits accessibility, and negatively impacts the environment and urban quality of life. Intersections play a critical role in managing traffic flow, and many approaches leverage technology to optimize Traffic Signal Control (TSC). However, most adaptive models in the literature focus only on pedestrian and vehicle demand, overlooking broader contextual factors. In this paper, we propose SIAGeTrans, an adaptive system based on IoT and Game Theory that aims to consider inputs beyond demand fluctuations, including factors like weather, flooding, and emergency vehicles. As a result, it reduces overall waiting time by up to one-third and significantly lowers fuel consumption and emissions compared to the fixed-time approach.

Emerging IoT applications with stringent requirements on latency and data processing have posed many challenges to cloud-centric platforms for Smart Cities. Recently, Fog Computing has been advocated as a promising approach to support such new applications and handle the increasing volume of IoT data and devices. The Fog Computing paradigm is characterized by a horizontal system-level architecture where devices close to end-users and IoT devices are used for processing, storage, and networking functions. Fog Computing platforms aim to facilitate the development of applications and systems for Smart Cities by providing services and abstractions designed to integrate data from IoT devices and various information systems deployed in the city. Despite the potential of the Fog Computing paradigm, the literature still lacks a broad, comprehensive overview of what has been investigated on the use of such paradigm in platforms for Smart Cities and open issues to be addressed in future research and development. In this paper, a systematic mapping study was performed and we present a comprehensive understanding of the use of the Fog Computing paradigm in Smart Cities platforms, providing an overview of the current state of research on this topic, and identifying important gaps in the existing approaches and promising research directions.

Tourism and Travel sector are improving services through the use of a large amount of data gathered from different sources. The ease access to reviews, rating and experiences from different tourists made the touristic planning rich and complex. Hence, a big challenge faced by tourism sector is to use the gathered data for detecting tourist preferences. In this work, we proposed a solution able to detecting tourist implicit preferences based on photos. We used emerging techniques such as Convolutional Neural Network and fuzzy logic to classify tourists in five categories. Finally, a set of photos were analyzed and the results reached over 82% on accuracy metric for all classifiers.

The cloud lock-in problem is commonly addressed by three strategies: (i) use of intermediate layer between cloud services consumers and providers, (ii) use of standardized interfaces to access cloud services, or (iii) use of models with open specifications. This paper evaluated these strategies and concluded that despite the advances they introduced, none of them actually solves the cloud lock-in problem. To address this issue, this paper proposes the use of Semantic Web concepts to avoid cloud lock-in. The strategy consists of using RDF models to specify the features of a cloud service, which are managed by SPARQL queries. The contribution of this paper is fourfold: (i) to evaluate three multi-cloud solutions for the cloud lock-in problem in the context of three cloud platforms, (ii) to propose a strategy using RDF and SPARQL for managing cloud resources, (iii) to present the Cloud Query Manager (CQM), an SPARQL server that implements the proposal, and (vi) to compare three multi-cloud solutions with CQM in terms of response time and the effectiveness in the resolution of the cloud lock-in problem.

With the advance of the Cloud Computing paradigm, new challenges in terms of models, tools, and techniques to support developers to design, build and deploy complex software systems that make full use of the cloud technology arise. In the heterogeneous scenario of this new paradigm, the development of applications using cloud services becomes hard, and the software product lines (SPL) approach is potentially promising for this context since specificities of the cloud platforms, such as services heterogeneity, pricing model, and other aspects can be catered as variabilities to core features. In this perspective, this paper (i) proposes a seamless adaptation of the SPL-based development to include important features of cloud-based applications, and (ii) reports the experience of developing HW-CSPL, a SPL for the Health Watcher (HW) System, which allows citizens to register complaints and consult information regarding the public health system of a city. Several functionalities of this system were implemented using different Cloud Computing platforms, and run time specificities of this application deployed on the cloud were analyzed, as well as other information such as change impact and pricing.

Ubiquitous environments still suffer from low availability given that any device may fail and it is hard to replace a failed element. In this paper we present AdaptUbiFlow (Adaptive Ubiquitous Workflow), an OpenCOPI´s element that aims to increase the availability of an ubiquitous system. When a device fails, AdaptUbiFlow supports the automatic reconfiguration of the system replacing the failed device (or service) by an equivalent one, this makes the system fault-tolerant without the need of any manual intervention. The replacing of a device/service is chosen taking into account not only the QoS and QoC (Quality of Context) provided but also the application's execution flow to ensure that the best adaptation option will be chosen. AdaptUbiFlow evaluation showed encouraging results.

This paper presents the results of an exploratory study on the fault-proneness of aspect-oriented programs. We analysed the faults collected from three evolving aspect-oriented systems, all from different application domains. The analysis develops from two different angles. Firstly, we measured the impact of the obliviousness property on the fault-proneness of the evaluated systems. The results show that 40% of reported faults were due to the lack of awareness among base code and aspects. The second analysis regarded the fault-proneness of the main aspect-oriented programming (AOP) mechanisms, namely pointcuts, advices and intertype declarations. The results indicate that these mechanisms present similar fault-proneness when we consider both the overall system and concern-specific implementations. Our findings are reinforced by means of statistical tests. In general, this result contradicts the common intuition stating that the use of pointcut languages is the main source of faults in AOP.