• Oraculum enables adaptive control for dynamic IoT environments. • It combines supervised, unsupervised, and reinforcement learning. • Predictive modeling supports proactive adaptation decisions. • Ontology-driven design improves metric organization and reasoning. • The model achieves 94% adaptation accuracy with 0.05 s mean adaptation time. Smart environments require adaptive resource management to handle dynamic workloads and system variability. Traditional solutions, which often rely on static configurations or heuristic adjustments, may not maintain performance as conditions change. This work presents Oraculum, an adaptive model that integrates real-time monitoring, predictive analytics, and automated decision-making. Unlike previous architectures that apply reactive or rule-based adaptations, Oraculum incorporates predictive reinforcement learning (TD3) to anticipate environmental changes and optimize reconfiguration decisions proactively. The model applies data-driven methods to adjust system configurations dynamically, improving both resource allocation and service quality. Experimental results demonstrate that Oraculum significantly reduces Mean Adaptation Time (MAT) compared to existing self-adaptive models while achieving an adaptation accuracy of 97%, overhead of 2%, and maintaining system stability at 98%. These findings highlight the advantages of predictive control in addressing the challenges of dynamic workloads and resource constraints in smart environments, offering a practical approach for maintaining consistent performance.

Chronic diseases account for 7 of the 10 leading causes of death worldwide, including heart disease, cancer, chronic respiratory diseases, and diabetes. This study presents B-Track, a computational model designed to support non-communicable disease (NCD) care through behavior analysis. Using machine learning and data from wearable devices, B-Track creates user behavior profiles and provides personalized recommendations to promote healthier lifestyles. The model was evaluated through a prototype tested with 10 patients undergoing treatment, many of whom had existing risk factors or diagnoses such as heart disease, hypertension, or diabetes. Five patients demonstrated sustained behavioral improvements, including increased consumption of healthy foods and higher physical activity levels. Three others showed short-term improvements, and one patient showed no significant change. Evaluation using the Technology Acceptance Model indicated that 83% of users found B-Track useful, and 80% found it easy to use. These results suggest that B-Track may support long-term behavior change in managing chronic disease risk factors.

Smart environments frequently experience fluctuations in demand, infrastructure usage, and service-level expectations, requiring adaptive systems capable of dynamic selfoptimization. This work proposes a self-adaptive framework that integrates real-time monitoring, anomaly prediction, and reinforcement learning (RL) to proactively reconfigure systems before degradation occurs. Unlike previous approaches limited to specific layers or metrics, the proposed solution supports diverse performance indicators-ranging from hardware and software to network and SLA levels-and offers an extended repertoire of adaptation actions, such as resource scaling, node restarts, and heuristic tuning. Its design leverages predictive alerts to reduce reaction delays and improve adaptation timing. Experimental validation demonstrated a mean adaptation time of 0.05 seconds, approximately $94 \%$ adaptation accuracy, $4 \%$ overhead, and $98 \%$ operational stability, confirming its effectiveness for real-time optimization in heterogeneous and dynamic environments.

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational settings has revolutionized the traditional teaching-learning environment, giving rise to the concept of smart classrooms. This transformation not only enhances the monitoring of people and processes but also impacts the dynamics of student behaviors, which are influenced by various factors such as the teaching environment and teacher-student interactions. Recognizing and accurately identifying these behaviors is crucial for educators to implement effective interventions and improve learning outcomes. This study conducts a systematic literature mapping to examine the contemporary landscape of student behavior identification in smart classrooms. The analysis reveals a rich diversity of methodologies and approaches used in this area. Key contributions of this research include the development of taxonomies for 37 technologies deployed, 25 challenges faced, 58 student behaviors observed, as well as the identification of nine subjects benefiting from this data and 10 methodologies for processing behavioral information. By offering an overview of the technological and methodological underpinnings of behavior identification in smart classrooms, this study significantly propels forward the domain of smart classroom research, equipping educators and technologists with a holistic understanding of how to navigate and leverage the complexities associated with monitoring student behavior.

Learning evolves through human interactions and adapting to technological advancements. The fourth industrial revolution elevated the importance of machines in improving competitiveness, efficiency, and product quality. However, this emphasis on technology overshadowed the human-centric perspective. In contrast, Industry 5 aims to foster collaboration between humans, robots, and digital systems. Our study utilized systematic mapping to explore collaboration in I5. Specifically, we investigated how collaborative learning occurs in industrial workplaces. The main objective was to identify prevalent collaborative learning techniques, technologies, challenges, and data types in I5 environments. The results reveal various collaborative learning approaches, highlighting their potential to improve productivity, innovation, and worker participation in I5. The study uncovers significant trends, such as the increasing reliance on digital platforms and Artificial Intelligence (AI)-driven tools, which facilitate collaborative learning while posing unique challenges. Our taxonomy provides a structured framework for understanding these dynamics, serving as a valuable guide for practitioners and researchers. The findings underscore the necessity for adaptive learning strategies and the integration of advanced technologies to promote effective collaboration in industrial settings. This research contributes to the theoretical understanding of collaborative learning in I5 and offers practical insights for its implementation, thereby supporting the evolution of industry practices in this new technological era.

The use of game design elements in non-game contexts, defined as gamification, is being used to increase user engagement in non-game environments, such as workplaces, schools, or software applications. However, the challenge developers face when implementing gamification is identifying which game elements will engage users. Besides, the proposals often tend to support only the most common user types and engagement factors. In response to this challenge, this study proposes a generic gamification model, GamiProM. GamiProM helps to design gamified solutions by using an ontology that encompasses knowledge about gamification elements and mechanics, user types, and types of motivations. Using profile management, GamiProM aims to provide knowledge representation and add semantic value to user characteristics and the information generated by gamification. A case study allowed the evaluation of the model through the gamification of an existing application using GamiProM. The results indicate that GamiProM can identify user profiles that support personalization through a combination of software, rules, and ontologies.

Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical and psychological illnesses. The advancement of sensing technologies, such as Internet of Things, has contributed to the understanding and assisting events related to anxiety and stress. However, the main challenge is knowing which approaches can be used to better monitor these emotions and assist people. Based on a systematic literature review, this work analyzed studies both to determine how data is collected, and to monitor anxiety and stress levels. Two taxonomies synthesize the techniques mapped. The results indicated more emphasis on studying stress than anxiety and more focus on detecting anxiety and stress levels than on assisting the user. Among the main techniques to collect data, 62.5% of the studies used physiological data like heart data, and for data analysis techniques, 48% of the studies used Decision Trees.

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness.

Noncommunicable diseases (NCDs) or chronic diseases are responsible for 41 million deaths each year, equivalent to 71% of all worldwide deaths. Many technologies are used to aid the treatment of NCDs, and data analysis has been used as an approach to improve the understanding of human behaviour related to risk factors. This study aims to distinguish how human behaviour data analysis has been applied to support the treatment and prevention of NCDs, what technologies are currently used, and what gaps are still left unexplored. We conducted a systematic mapping study to analyse academic articles published from 2010 to September 2021. A filtering process mitigated article bias by reviewing, analysing, and classifying 41 works from 12,395 collected. The main results obtained presented that 43% applied data analysis in depression, 17% applied for general NCDs, and 12% for diabetes. Whereas, machine learning represents 60% of technologies found in the articles, mobile devices 58%, and wearables 29%. This study proposes two taxonomies obtained from the analysis of the selected articles that allow systematised guides to access the knowledge produced in the study. In addition, the taxonomies link technologies used to identify human behaviour with associated NCDs.

Although many benefits emerge from the growing capabilities of smartphones, there are also concerns related to the long-term hyper-connected experience. Based on a systematic mapping method, this study investigates the primary factors of problematic smartphone use (PSU). Initially, this mapping considered ten academic databases, which allowed the analysis of 436 studies, and the creation of a taxonomy that categorises technology addiction topics such as the Internet, Smartphones, Video games, and Electronic devices. After the initial search and filtering, the study selected and deeply analysed 115 articles concerning the PSU influences on mental health, proposing a taxonomy to classify mental disorders and common symptoms related to PSU. The outcomes suggest that those who fear missing out on important events, females, depressed, anxious, and bored people are prone to PSU, reinforcing the importance of understanding the factors that lead a person to use smartphones in a problematic way and alternatives to help people cope with PSU. Scales such as the Smartphone Addiction Scale-Short Version (SAS-SV) and strategies such as cognitive-behavioural therapy (CBT) and limiting smartphone access are being used to handle PSU. Finally, this study presents implications and recommendations for future research in this area.

Studies indicate the growth and relevance of systems that can recognize affective states in the most different areas. A tendency to more natural environments could be noticed that considering physical or physiological signs and the individual’s relationship with the environment, other people, and daily activities. Therefore, what is the importance of combining ubiquitous computing, affective computing, and the contributions of context-aware information to provide more accurate and intelligent affective systems? This study presents a systematic literature mapping about the use of contextual information to identify affective states, which covered articles published between 2010 and October 2021, resulting in 1.638 studies. After applying filters, which we explain further in this article, we selected 49 works to answer a set of research questions. The results indicate that physiological data was the main parameter for recognizing affective signs (62.3%, 33/53), followed by visual data (32.1%, 17/53). The links between context and affective signs presented a more significant occurrence in the combination of contexts related to activities and physiological data (34%, 18/53).

Nowadays, one of the most relevant challenges of a data center is to keep its information secure. To avoid data leaks and other security problems, data centers have to manage vulnerabilities, including determining the higher-risk vulnerabilities to prioritize. However, the current literature is scarce in the proposal of intelligent methods for the complex problem of vulnerabilities prioritization. Depending on the adopted metrics, the priority could shift, compromising simple sorting-based approaches and impairing the utilization of conflicting risk assessment metrics. Unlike the related work, this study proposes a multi-objective method that uses user-chosen vulnerabilities assessment metrics to output a complete list of these vulnerabilities ranked by their risk and overall impact in the context of an organization. The method includes a multi-objective large-scale optimization problem representation, a novel population initialization scheme, an expressive fitness function, a post-optimization process, and a custom way to select the best solution among the non-dominated ones. The dataset used in the experiments contains anonymized real-world information about database vulnerabilities obtained from a private organization. The experiments' results indicated that the proposed method can reduce the number of vulnerabilities needed to reach an organization's predefined security targets compared to the baselines simulating a security team's analysis. Multi-objective optimization achieved on average a 48,17% reduction in the vulnerabilities needed to reach the organization's target values compared to the baselines.

Multi-objective decision-making and dynamic short-est paths are two areas of research widely studied and of great importance for computer science, engineering, and economics. Both areas have seen a remarkable evolution in their algorithms in the last decades and have contributed to several applications in real life. The applications range from cost reduction in the expansion of telecommunications and electrical networks to the accessibility of people and autonomous vehicles. However, despite their importance, the investigation of methods at the intersection of these two areas has not been explored in the literature. Problems at this intersection are characterized by graphs whose topology can change over time (i.e., edges can be inserted or deleted online) and whose edges' costs are defined by more than a single criterion or objective. In this paper, we introduce the multi- objective dynamic shortest path problem (MODSP) and present the first algorithm to solve it. In particular, we formally define the MODSP problem and explain its relation to multi-objective decision-making and dynamic shortest paths. Concerning the algorithm, we present the first single-source MODSP (SMDSP) approach as a first effort towards solving MODSP problems while avoiding the recomputation of the paths from scratch when the graph is updated. Finally, we perform an experimental evaluation of the SMDSP and a comparison with the state-of-art algorithm for multi-objective shortest path problems. The results of our experimental evaluation prove that in an environment subjected to constant updates, the SMDSP algorithm is more efficient.

Abstract The usage of recommenders systems is already widespread. Every day people are exposed to different item offerings based on the prediction of their interests and decisions. Context information, such as location, goals, and close entities, plays a key role in the recommendations' accuracy. The use of context histories allows one to identify similar context histories and predict contexts. This article proposes Vulcont, a recommender system based on a context histories' ontology. Vulcont merges the benefits of ontology reasoning with context histories to measure the context history similarity, based on the semantic and ontology properties provided by the context’s domain. Vulcont considers synonymous and classes' relations to measure similarity. After that, a collaborative filtering approach identifies sequences' frequency to identify potential items for recommendation. The proposed recommendation is evaluated and discussed in four scenarios in an offline experiment, which explores the semantic value of context histories. The main contribution of Vulcont is the use of semantic relations and the properties of ontology in a similarity measurement of context histories, which is a data structure more complete than that of single contexts.

This study proposes SW‐Context, a model focused on defining context information for software, and storing this information in contextual history. The proposed model provides qualitative information concerning source code and design issues to support the daily activities of software developers, thereby improving their situation awareness. SW‐Context was evaluated through a case study in industry, in which 12 developers used it to support maintenance activities throughout one month. After this period, all developers filled out a qualitative questionnaire, which was formulated based on the well‐established technology acceptance model. The obtained results suggest that 67% of developers completely agree with the usefulness and ease of use of the SW‐Context. These results are encouraging and show the potential for implementing SW‐Context in broader software maintenance scenarios.

With the growing availability of devices capable of capturing information about their surroundings and the expansion of mobile connectivity, Internet of Things (IoT) solutions are increasingly been integrated into society. For IoT solutions to emerge successfully on the market, they will employ more than traditional mobile computing, but they will also require the use of everyday objects in an interconnected way. This interconnected world will support the intelligence in environments. In order to provide this infrastructure for environments, it will be necessary to propose platforms for software development based on context awareness and context processing. In this sense, this article proposes CMFrame, a framework for managing contextual information captured from physical environments using hierarchical and dynamic entities. CMFrame allows that entities to modify their hierarchical organization to manage environments and their related contexts. Contexts linked to each entity are also dynamic and can store different amounts of values at any time. The article presents the proposed framework and its evaluation through two applications focused on intelligent environments. The first is dedicated to monitor the movements of entities in an environment, and the second addresses energy monitoring. The scientific contribution of CMFrame is the proposal to abstract the management of dynamic and hierarchical context histories through a framework.

Context: Gamification focuses on the improvement of users' engagement when performing tasks by making use of game mechanics and elements in order to increase their motivations. Many researches have developed gamification models supporting a variety of motivational characteristics to provide engagement solutions for different areas. Objective: This paper carries out a systematic mapping in the field of gamification, looking for models with motivational characteristics in an attempt to characterise the state of the art of this field, identifying gaps and tendencies for further research. Method: We carried out a systematic mapping aiming at finding the primary studies in the existing literature, which were later classified and analysed according to twelve criteria. Results: We analysed 70 papers that resulted in 17 primary studies, published until September 2016. Most of them focus on Education, making use of Gamification to increase the motivation of a learning process. The gamification mechanics and elements most used were Badges/Achievements and Points/ExperiencePoints(XP), and most of the studies were not validated, thus not providing empirical evidence of the impact of gamification. Conclusions: Existing research in the field is somehow preliminary, and more research effort to analyse the applicability of the models and their respective evaluations would be needed.

Abstract The space inside a vehicle, which people can enjoy while travelling, is becoming more intelligent due to the technological developments within the automotive industry. This has influenced the increase in new services that are intended to fulfil the needs of vehicle users. This article presents a survey on the provisioning of ubiquitous intelligent services for vehicular users. It also identifies clusters of research interest on this subject. To this end, an evaluation of results from six scientific research databases supports these goals. This process initially identified 37 328 publications; after filtering and clustering, the scope of analysis became 39 articles. The main contributions points to (i) the existence of five active research clusters, as a result of the inclusion of a visual clustering step in the systematic mapping protocol; (ii) an indication of a string of studies starting in the late ’90s from traffic issues, intelligent transport systems and profiling, to smart cities, the Internet of Things, big data, fog computing and internet of vehicles in recent years; and (iii) that the main challenges associated with the implementation of ubiquitous intelligent services for vehicular users are related to data security, infrastructure, connectivity and high mobility.

As a result of rural and suburban migration to the cities, urban life has become a significant challenge for citizens and, particularly, for city administrators who must manage the sustainable use of resources such as energy, water, and transportation. Smart cities are the biggest vision to efficiently address these challenges through a real-time monitoring, providing an intelligent planning and a sustainable urban development. However, to accomplish them we need a tightly integration among citizens, city devices, city administrators, and the data center platform where all data is stored, combined, and processed. In this context, we propose ElCity, a model that combines citizens and city devices data to enable an elastic multilevel management of energy consumption for a particular city. As design decision, this management must occur automatically without affecting the quality of already offered services. The main contribution of ElCity model concerns the exploration of the cloud elasticity concept in multiple target levels (smartphones from citizens, city devices involved in the public lightning, and data center nodes), turning on or off the resources on each level in accordance with their demands. In this way, this article presents the ElCity architecture, detailing its modules distributed along the three data sources, in addition to an experiment that uses city devices and citizens data from Rome to explore energy saving. The results are promising, with an Energy Monitor module that allows the estimation of the energy consumption of elastic applications based on CPU and memory traces with an average and median precision of 97.15 and 97.72 percent. Moreover, we proposed a reduction of more than 90 percent in the energy spent in public lightning in the city of Rome which was obtained thanks to an analysis of geolocation data from their citizens.

In the Project Management context, the right allocation of the individuals about their competences and availability for tasks execution reduces the risk as the planning deviations from the time, cost and quality of the project. To achieve these benefits, it is relevant to identify people with specific skills and knowledge required for the development of each project activity. In this sense, this article proposes a model to support the activity management and allocation of individuals in projects through an ontology based on the concepts of the Theory of Activity, seeking to help through consultations the allocating of appropriate resource. Through the scenarios presented for evaluation was demonstrated the suitability to use the model for assistance of project managers.

According to World Health Organization, non-communicable diseases were responsible for 68% of global deaths in 2012. The care of this type of disease goes beyond the patient's engagement since their relatives, friends and acquaintances, also influence in their treatment. Ontologies can map the relationships between patients and their environment, thus representing a formal description from concepts of a domain, and then answering questions formulated for this domain. This paper presents an ontology for detect the spreading of happiness, obesity and smoking on social networks. This ontology was built and validated following the GrUniger and Fox's Methodology, and proved feasible for use in applications that need to evaluate the influence between nodes of a social network and to recommend new beneficial contacts to their users.

Ubiquitous computing aims to make tasks that depend on computing transparent to the users, providing resources and services anytime and anywhere. Currently, libraries have sought to position themselves closer to their users, by adopting ubiquitous technologies. This paper presents an intelligent model to support ubiquitous libraries called U-Library. U-Library specification focuses on relevant aspects to build intelligent systems, such as the use of trails, dynamic profiles, recommendation strategies, ontologies and multi-agent systems (MAS). The model provides appropriate information and tools for librarians to maintain resources and services in a library, as well as tools that enable them to offer better services. We have developed a prototype that was evaluated by volunteers using data from a real library. More than 200 thousand records formed the resources database. The trails database consisted of $\\sim $ 7 million records, covering the period from 31 January 2000 to 1 July 2013, and associated with 26 476 users. The group of volunteers that participated of the experiment consisted of 20 clients and 25 librarians. The evaluation sought to measure ease of use and perceived usefulness of U-Library, as well as the quality of the recommendations delivered. The results showed a good acceptance of the model.

Research regarding stroke indicates that short elapsed time between accident and treatment can be fundamental to allow saving patient's life and avoid future sequels. This paper describes a model for monitoring and rescue victims in situations of possible stroke occurrence. It uses stroke symptoms that can be monitored by mobile equipment, ambient intelligence and artificial neural networks. The model is independent from human operation and applications or third parties devices, therefore adding facilities to increase the quality of life for people with stroke sequel, due to constant monitoring and follow-up provided, allowing the stroke patient to consider a recovery period with greater autonomy. A prototype based on free software platforms was developed, in order to assess the accuracy and the time elapsed between the prototype to detect and to send an alert. The results indicate a positive outlook for the work continuity.

The use of characteristics of smart objects that have interactions features with humans, gave rise to the Internet of Things (IoT). Numerous derivations from this concept have been proposed. In this article, we focus on one of those called Social Internet of Things (SIoT). SIoT prioritizes the relationship between smart objects, where the objects can establish a connection among themselves without the interference of their owners. The purpose of this article is to explore the concept of SIoT in a University Campus, offering direct communication between intelligent devices. These devices share information based on an academic criteria and preferences informed by their owners. To evaluate the proposal, we developed a case study. The preliminary results show the viability of the proposal.

The use of recommender systems is widespread in several everyday systems. People are now exposed to different offerings, based on their interests, in order to anticipate decisions. However, in a different way recommender systems for accessibility domain require further studies and researches. In order to provide resources for people with special needs, we developed the Vulcanus, a recommender system designed for people with disabilities, offering access to resources according user's needs. The recommender system approaches concepts from ubiquitous computing, such as user profiles, context awareness, trails management, and similarity analysis. Vulcanus uses two different approaches: resources patterns and categories patterns; where different systems can use it, for example as a Web Service. Vulcanus was evaluated in different scenarios. These scenarios used more than 800 thousands of trails that simulate wheelchair users using resources along six months. The obtained results show that both approaches are able to provide relevant recommendations, providing resources that in fact can support users' needs.

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed using thresholds. In this way, limits are used to drive resource mangement actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application's load pattern in the elasticity? This article answers these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the upper threshold influences the application time more than the lower one.

The ubiquitous computing, or ubicomp, is a promising technology to help chronic diseases patients managing activities, offering support to them anytime, anywhere. Hence, ubicomp can aid community and health organizations to continuously communicate with patients and to offer useful resources for their self-management activities. Communication is prioritized in works of ubiquitous health for noncommunicable diseases care, but the management of resources is not commonly employed. We propose the UDuctor, a model for ubiquitous care of noncommunicable diseases. UDuctor focuses the resources offering, without losing self-management and communication supports. We implemented a system and applied it in two practical experiments. First, ten chronic patients tried the system and filled out a questionnaire based on the technology acceptance model. After this initial evaluation, an alpha test was done. The system was used daily for one month and a half by a chronic patient. The results were encouraging and show potential for implementing UDuctor in real-life situations.

Currently, we can observe the growing of the use of mobile devices as a media for both Internet access and carrying out purchase and sale of products and services. Commonly, E- commerce platforms are enabled by either proprietary systems or by buying ads on specialized websites. This scenario involves the adoption of strategies based on e-commerce servers, which represent a non-scalable strategy. The situation is even more critical considering the current scenario, where mobile devices has been involved in trading, the so-called m-commerce. In this context, this paper proposes the use of the P2P technology as the network substrate for supporting mobile commerce. For that, we are using the notion of ultrapeers that act as managers that receive, process and pass on requests for trade. Suppliers and consumers act as end points and access ultrapeers of their geographic region through a standardized interface. To implement the distributed scenario, changes are made over the DNS servers so that mobile devices can locate ultrapeers based on their geolocation data. Thus, suppliers who need to travel can advertise their products in the regions which they pass, boosting their business through this opportunistic approach. Finally, experimental evaluation showed that the architecture is feasible for mobile commerce environments composed by both stationary and mobile devices.

In this paper we present a view of EXEHDA middleware and a novel service created for dynamic adaptation. EXEHDA is service-oriented, adaptive and was conceived to support the execution of ubiquitous applications. The main concept in the proposed design for the middleware and for the application is context awareness expressed in an adaptive behavior. The middleware manages and implements the follow-me semantics for ubiquitous applications. This is also a key to provide functionality adapted to the constraints and unpredictability of the large-scale environment. EXEHDA provides services for distributed adaptive execution, context recognition, ubiquitous storage and access, and anonymous and asynchronous communications. To evaluate the proposed service we developed a case study, implementing an application in medical area. Analyzing the results we can see that the users found the application easy to use and usefulness for health workers at a hospital.

Ubiquitous computing (Ubicomp) environments are characterized by high distribution, heterogeneity and dynamism. In these environments, applications must be aware of their contexts and adapt to changes in them. A major research challenge in the area of Ubicomp is related to context awareness. Considering the characteristics of ubiquitous environments, this paper presents an architecture for context awareness, called DynamiCC (Dynamic Context Composition), that includes elements to support: context modeling, contextual data collection, actuation on the environment, and composition and interpretation of contextual information. We consider that the main contributions of this work are the employ of a hybrid approach for context modeling, and the proposal of an architecture that supports the interpretation and the composition of dynamic contexts, which enables the construction of complex contexts, in runtime of applications. To assess the functionality of the DynamiCC, we did a discussion of usage scenarios, highlighting the prototypes and tests performed.

GlobalEdu is a generic model created to support learning in ubiquitous computing environments. This model has the necessary support to implement learning-related functionalities in ubiquitous environments. The basic ubiquitous computing support must be supplied by a middleware where GlobalEdu lays atop. This paper proposes the integration of GlobalEdu with two middlewares: ISAM and LOCAL. ISAM supports the creation of large-scale ubiquitous systems. As such, its integration with GlobalEdu results in large-scale ubiquitous learning environments. On the other hand, LOCAL is dedicated to create small-scale, location and context-aware ubiquitous learning environments. The integration between GlobalEdu and LOCAL results in a local ubiquitous learning system. We created a practical scenario, where our proposal was evaluated.

The increasing use of mobile devices and the dissemination of wireless networks have stimulated mobile and ubiquitous computing research. In this context, education is being considered one of the main application areas. New pedagogical opportunities are created through the use of location systems to track learners, and through context awareness support. This paper proposes a model to explore these opportunities using location information and context management as learning support tools. This model, called LOCAL, was conceived for small scale learning environments, but can be applied in large-scale as well. The model was implemented and the initial results show its utility to assist the teaching and learning processes.

The ubiquitous computing scenario brings many new problems such as coping with the limited processing power of mobile devices, frequent disconnections, the migration of code and tasks between heterogeneous devices, and others. Current practical approaches to the ubiquitous computing problem usually rely upon traditional computing paradigms conceived back when distributed applications where not a concern. In this paper we propose UbiHolo, a new end-to-end model oriented to development and execution of applications over large- scale ubiquitous computing systems. UbiHolo is based on the Holoparadigm (in short, Holo), a new software paradigm oriented to the development of distributed computer systems. After that we show that UbiHolo is scalable, and that it is possible to create and manage execution of applications over large-scale ubiquitous environments without a great impact on application 's user response tunes. Our results are obtained through a simulation of a Holo system with up to 950 nodes, which was implemented over the p2psim simulator.

The increasing use of mobile devices and the dissemination of wireless networks have stimulated mobile and ubiquitous computing research. In this context, education is being considered one of the main application areas. This paper proposes the use of mobile and ubiquitous computing to support and improve learning in a new kind of academic structure called Undergraduate Course of Reference (nicknamed GRefe). The GRefe was proposed in Unisinos, a university located in south of Brazil. Currently, there are four GRefes. These courses are organized in Learning Programs and Learning Projects. They use a practical and multidisciplinary approach to stimulate the learning. We proposed the use of mobile and ubiquitous computing technology to articulate and improve the academic activities of a specific GRefe called Computer Engineering. We believe that GRefe organization simplified and stimulated the use of these technologies in a learning environment.

We propose a peer-to-peer event ordering and simulation technique aimed at networked real-time action games. Partially based on replicated simulators, its goal is to support decentralized playout in small-scale game sessions on instanced action spaces while being resistant to collusion cheating. The action spaces are linked to persistent-state social spaces of larger scale which are supported by centralized simulation. Together, these two kinds of spaces offer support for massively multiplayer on-line games (MMOGs) that offer a mix of socialization on large-scale persistent environments and fast interaction on small-scale temporary ones. Although player nodes on action spaces are required to run a conservative and an optimistic simulator simultaneously, we show that 2.2 simultaneous simulation steps are executed on average and that 11.95 simultaneous steps occur as the average peak situation for 20-player sessions with 150ms to 300ms network delays between nodes, 5% probability of any late events introducing errors, and rollback and re-execution operations having their execution spreaded through 100ms of real time or longer.

State-of-the-art Massively Multiplayer Games such as EverQuest and Ultima Online are currently implemented as client-server systems. Although this approach allows the development of commercially viable MMG services, the costs associated with running a scalable client-server MMG service are often too high for small companies or research projects. This paper proposes FreeMMG, a mixed peer-to-peer and client-server approach to the distribution aspect ofMMGs. It is argued that the FreeMMG model supports scalable, cheat-resistant, massively multiplayer realtime strategy (RTS) games using a lightweight server that delegates the bulk of the game simulation to the clients. A working prototype game called FreeMMG Wizards is presented, together with some preliminary scalability test results featuring up to 300 simulated game clients connected to a FreeMMG server. The results show that the measured server traffic can be considered very low if compared with more centralized alternatives.

The multiparadigm approach integrates programming language paradigms. We have proposed the Holoparadigm (Holo) as a multiparadigm model oriented to the development of distributed systems. Holo uses a logic blackboard (called history) to implement a coordination mechanism. The programs are organized in levels using abstract entities called beings. First, we describe the principal concepts of the Holoparadigm. After, we propose the Distributed Holo (DHolo), a model to support the distributed execution of programs developed in Holo. DHolo is based on object mobility and blackboards. This distributed model can be fully implemented on the Java platform. Experiments were done using Voyager and Horb to implement mobility. Blackboards were implemented using Java and JavaSpaces.

Mobile computing has emerged as a new field, distinct from conventional distributed computing by its focus on mobility and its consequence. The physical and logical mobility needs of applications with new requirements: built-in mobility, adaptability and flexibility. Providing a system architecture that simplifies the task of implementing the mobile applications with adaptive behavior is the objective of the ongoing ISAM project. In order to achieve its goal, ISAM uses, as strategies, an integrated environment that: (a) provides a programming paradigm and its execution environment; (b) treats of adaptation process through multilevel collaborative model, in which both the system and the application contribute for that. We present the ISAM architecture and introduce the relative issues to the treatment of adaptive mobile applications.

ISAM is a proposal directed to resource management in heterogeneous networks, supporting physical and logical mobility, dynamic adaptation and the execution of distributed applications based on components. In order to achieve its goals, ISAM uses, as strategy, an integrated environment that: (a) provides a programming paradigm and its execution environment; (b) handles the adaptation process through a multilevel collaborative model, in which both the system and the application contribute. In this paper we discuss the main mechanisms used to implement the ISAM features, and we also present a parallel application that explores some of this features.