This study presents a comprehensive analysis of predictive modeling approaches for Brazilian Real Estate Investment Funds (REIFs), evaluating the comparative performance of machine learning techniques against traditional time-series methods. Our methodological framework examines three critical dimensions: (1) fund categorization (paper, brick, and hybrid assets), (2) modeling architectures (individual fund-specific models versus a unified ensemble approach), and (3) temporal forecasting horizons (1-month versus 6-month predictions). The research introduces significant innovation through the systematic integration of property-level attributes into the predictive models, marking the first such application in REIF forecasting literature. Empirical results demonstrate that machine learning models achieve superior predictive accuracy, particularly for medium-term forecasts, while the incorporation of physical property characteristics emerges as a statistically significant factor in enhancing model performance. These findings contribute both methodological and practical insights for investors and fund managers operating in Brazil’s real estate investment sector.

Customer Lifetime Value (CLV) estimation plays a pivotal role in enhancing customer management and long-term business planning. However, current methodologies often suffer from significant constraints, including strong dependence on context-specific variables, substantial data requirements, and potential threats to user privacy. This work introduces a versatile and privacy-conscious machine learning approach to CLV prediction that relies solely on variables extracted from transaction timestamps and monetary amounts. The proposed model was evaluated across five distinct datasets, each with varying structures and behavioral patterns, and benchmarked against well-known models from the literature. Results demonstrate that the proposed solution delivers robust predictive accuracy, achieving up to a 34% reduction in CLV error in some cases, while offering adaptability across a wide range of business scenarios.

Human mobility analysis is critical for applications ranging from urban planning and epidemic modeling to personalized recommendation systems. However, existing approaches often neglect the semantic dimension of mobility which can reveal latent behavioral insights. In this work, we introduce semantic motifs to capture lifestyle shifts at both individual and collective levels. Using the proposed framework, we analyze 95.5 million GPS records from 65,402 mobile users in Brazil, comparing movement behaviors between 2021, strict COVID-19 restrictions period, and 2022, after easing restrictions. Our distributed pipeline, implemented on Apache Spark and Apache Sedona, scales efficiently to extract and cluster these motifs, enabling large-scale spatiotemporal analysis. Key findings reveal a transition from teleworking to hybrid lifestyles with greater variability in displacements.

The Internet of Drones (IoD) is expected to revolutionize the utilization of drones in urban environments. Drone delivery applications are expected to become commonplace in people's everyday lives. Such applications will establish a network where drones will have organized airspace access, with variations in network node density based on location and time. Within this environment, the IoD network may encounter communication gaps among drones. In scenarios like drone delivery, altering a drone's route solely for message delivery within the network is impractical. We aim to deploy an auxiliary drone network to support IoD applications, similar to using auxiliary networks in vehicular networks for message delivery. This study introduces the AuxIoD method, which utilizes a genetic algorithm to position auxiliary drones strategically, bridging communication gaps within the IoD network. Compared to other strategies, our approach facilitated the delivery of approximately 30% more messages in the evaluated scenario.

Location-based services (LBSs) play a vital role in many Internet applications. Privacy is a mandatory aspect of these tasks, including protecting the user's sensitive information from malicious entities. Location privacy-preserving mechanisms (LPPMs) were designed to ensure privacy for LBS users, and several strategies have emerged, such as cloaking mechanisms. Likewise, several attacks appeared to threaten the user's privacy, being based on ground-related aspects. Therefore, we must investigate new strategies to enhance privacy protection mechanisms. Unmanned Aerial Vehicles (UAVs) can provide assisted coverage to ground users in different tasks, including the support of LPPMs. However, the existing strategies rely on some unfeasible premises, and this collaboration needs to be adequately explored. Therefore, in this study, we propose Sky Cloaking, which promotes the opportunistic connection between ground users and UAVs in such a way the UAVs manage the user's query, creating a cloaking region and hampering the success of an attacker. Through a comprehensive evaluation, we demonstrated that SkyCloaking can ensure high levels of location privacy to LBS users with a slight impact on the communication channel, overcoming existing strategies. In the best scenarios, SkyCloaking protected more than 80% of the user trajectory, mitigating the exploitation of users' sensitive information.

Unmanned aerial vehicles (UAVs)–also known as drones or Unmanned Aircraft–have found diverse applications in various fields owing to their significant advantages, including fast mobility and rapid deployment. UAVs are crucial in aerial networks, providing increased coverage and on-demand connectivity as mobile nodes. In recent years, UAVs have made room to leverage the Internet of Things (IoT) to the sky, enhancing air-to-ground communication and pointing toward the next generation of UAV networks. As this expansion is still in its early stages, there are several aerial network terminologies, each with similarities and differences, depending on the deployment domain and the services they offer. However, studies have yet to discuss these different terminologies consistently. Key aspects have yet to be thoroughly explored, such as the anticipated requirements for deploying these networks and how they relate to the various terminologies. This work systematically analyzes the existing terminologies of UAV networks, considering their requirements and applications, shedding light on their intersections and differences. Furthermore, we present the demands for the next generation of UAV networks and discuss how they impact the design of UAV-related applications, aiding in the design of new protocols, tools, and technologies for both industry and academia. Lastly, we highlight the emerging trends and challenges associated with deploying and integrating these networks.

Shortly, the airspace will have different types of aerial networks due to the continuous growth of demands for aerial applications. As these networks compete for airspace, it is necessary to manage them efficiently. Also, these networks are expected to communicate with each other and with terrestrial networks forming hybrid aerial-terrestrial networks (HATNs). Efficient protocols for data packet delivery in HATNs are crucial for these networks to work together. In addition, cooperation between networks can be essential for different emergencies. However, communication in HATNs is challenging because of the plurality of node types. This work presents an altitude-based routing protocol for Hybrid Aerial-terrestrial Networks (ARAT). ARAT is a geocast and store-carry-forward protocol with promising performance in sparse scenarios. Simulation results show that ARAT outperforms two protocols (a baseline protocol and the GeoUAV protocol) by 50% for the packet delivery ratio metric. Also, to the best of our knowledge, ARAT is the first protocol to consider a collaboration between Aerial Networks and Internet of Drones (IoD) with the traffic of drones constantly.

Location Privacy Protection Mechanisms (LPPMs) have been designed to enhance privacy in the Internet of Drones (IoD), however, they present suitable privacy levels only in specific network conditions. Also, they can lead to a lack of Quality of Service (QoS) if applied in unfavorable conditions. Thus, the dynamic assignment of the most suitable LPPM, given the IoD conditions, is a significant challenge. Reinforcement Learning (RL) represents a useful concept to handle this problem, given its exploratory characteristics and being able to enhance the knowledge about network dynamics. In this study, we propose IoDAPM, an RL-based approach for the Dynamic Assignment of Protection Mechanisms in IoD. Through simulations, we extensively trained the RL-based model, exploring the possible IoD network conditions. With this model, we carried out a comparative evaluation of existing LPPMs. The results highlighted that IoDAPM outperforms the compared mechanisms considering the QoS, providing enhanced performance regarding location privacy, energy efficiency, and flight delay.

In Unmanned Aerial Vehicles (UAVs), the real-time detection and identification of unauthorized UAVs is a significant challenge to be appropriately addressed. Currently, supervised-based learning models (e.g., Deep Neural Networks) can detect the presence of authorized UAVs with reasonable accuracy. Still, they can not handle properly the wide range of unknown signals in the airspace, mainly their categorization. Clustering techniques (e.g., DBSCAN) can be applied to identify and classify unfamiliar signals. However, the uncertainty regarding the nature of unknown sounds can lead to a large dimensional problem, hampering the performance of these techniques. Given these issues, we proposed DissIdent, a dissimilarity-based method for identifying unknown drones. Our approach takes advantage of the dissimilarity concept, in which a function of proximity maps extensive and multi-dimensional problems to a binary problem. DissIdent can identify patterns from different features through an intelligent workflow, mitigating the trade-off between the traceability and accuracy of massive multi-class problems. We carried out an extensive evaluation of DissIdent, comparing it with eight different approaches. The results pointed out DissIdent as a robust approach to detection and identification tasks, overcoming the compared methods. DissIdent addressed accuracy rates higher than 93% in all scenarios, presenting a concise detection and identification of unauthorized drones.

The Internet of Drones (IoD) is a network paradigm that allows drones to perform several services, gathering and sharing location-based information, representing a piece of the next generation of the Internet of Things (IoT). Location privacy is a paramount requirement in IoD. A few Location Privacy Protection Mechanisms (LPPMs) are designed for IoD. Unfortunately, they are not energy-aware approaches, essential for IoD protocols since drones have power limitations. We present the MixRide, an energy-aware LPPM for IoD to overcome these issues. It provides location privacy through the aerial-grounded vehicle collaboration, where the drones take a ride with grounded vehicles, changing their pseudonyms while saving energy. A comparative experimental evaluation pointed out that MixRide can provide location privacy to the IoD at the same level as the state-of-the-art LPPM while improving the drone's power consumption. Our results also provide new insights on the trade-off between the delay caused by the ride and the drone's energy power consumption.

Recently applications that use a single drone are evolved into an Internet of drones (IoD) ecosystem. The IoD has a complex control as it needs to coordinate the drones in the airspace, which involves mobility and communication between them. It facilitates the IoD control if drones frequently report their positions to the control base, use well-defined airways to fly, and are managed through Software-Defined Internet of Drones (SDIoD). In the shared airspace, drones are expected to exchange messages with each other. In this work, we propose PSDIoD, a Position-based routing protocol for SDIoD, which has two versions. The first, called PSDIoD-SP (Shortest Path), considers the shortest path in the airways to select the routes. The second, called PSDIoD-EA (Energy-Aware), examines the radio power consumption estimated to choose the route. Due to nodes having a limited battery, power is a crucial factor in IoD. Both of which consider the knowledge of the position of drones to route packets. This work advances the state of the art by proposing efficient routing solutions that were developed in a suitable way for the IoD environment. The results show that PSDIoD versions are better in power consumption, packet delivery ratio, delay, and overhead when compared to a baseline protocol. Furthermore, PSDIoD-EA is approximately 9% more economical in terms of power consumption when compared to PSDIoD-SP.

The recent advancement of drone technologies and communication protocols allows us to envision a robust and dynamic mobile vehicular network paradigm called the Internet of Drones (IoD). In this environment, drones will perform several location-based services (LBSs) for users, awakening the interest of malicious entities whose intention is to hamper the service. Hence, drones need high protection regarding their localization in LBSs. However, there is a lack of Location Privacy Protection Mechanisms (LPPMs) for an IoD scenario. Dummy-based LPPMs provide proper location privacy in traditional mobile networks, mainly for sparse configurations. The design of this mechanism for IoD can overcome this deficiency. This study proposes a novel dummy-based LPPM for the IoD, called TDG, that focuses on the IoD topology characteristics regardless of near drones being the first approach presented in this context. Through extensive experiments, we show that TDG can provide proper location privacy for drones in sparse configurations, reducing the use of the wireless communication channel. TDG can protect the real drone’s trajectory up to more than 90% of the time, leaking less than 25% of the drone’s real coordinates.

Internet of Drones (IoD) is an architecture that aims to enable different drones to share the same airspace. This architecture can help coordinate drone access to airspace in urban environments. Considering that the IoD is a dynamic network, it is possible to have scenarios in which drone traffic is sparse when, for instance, the network has isolated drones. In this case, the drones’ communication range does not reach any other drone. Thus, store-carry-forward protocols may be suitable for maintaining network communication. Moreover, different networks can collaborate to fill these communication gaps. In this study, we explore the collaboration between IoD and Bus Networks. Our analysis shows that maintaining a hybrid communication between drones and buses can fill the gaps in the communication between drones. The main goal of this work is to present the IoDSCF – a store-carry-forward routing protocol for joint Bus Networks and the Internet of Drones (IoD). IoDSCF takes advantage of both networks to extend the communication reachability. Our results reveal that IoDSCF presents better results in the number of delivered packets and end-to-end delay than a solution based only on communication between drones. This is a promising strategy for data communication, mainly in smart cities.

In the last few years, new scenarios have emerged for drone applications including those of risk scenarios. In this case, it is essential to have communication among drones whenever they perform tasks that need some sort of coordination, management or synchronization, for instance. In this work, we propose IoDAGR – Internet of Drones Airway-based Geocast Routing, a geocast protocol to transmit messages, such as alerts, to a specific geographical region in a drone network. IoDAGR advances the state of the art of geocast protocols for drones by proposing an efficient solution to this scenario. Simulation results revealed that IoDAGR presents promising results when compared to a baseline protocol.

The Internet of Drones (IoD) refers to a robust mobile network with well-defined airways where drones perform interoperable services, enhancing the deployment of Smart Cities. Automatic Drone Identification (ADI) is a protection mechanism to detect and avoid malicious drones, where different techniques have been used, such as acoustic signals. In this field, the sound generated by propellers and motors has particular characteristics, being a potential aspect to explore; however, this investigation is still missing. This study examines the use of rhythm-based descriptors as input features to ADI, based on the hypothesis that the acoustic signal generated by different drones has different rhythmic properties. Aiming to explore and validate our approach, we formulate an ADI methodology using rhythm-based features. We use a freely available drone audio dataset, comparing our results with a baseline study. As a result, our classification model improves 3.47% the baseline binary classification and 2.97% the multiclass classification, reaching accuracy rates of 0.9985 and 0.9591, respectively. Although the improvements are narrow, they point out that acoustic features have a great potential to enhance ADI mainly in dense drone-based environments, where drone identification is an essential task.

The Internet of Drones (IoD) is a novel network paradigm that presents a unique mobile network scenario with particular characteristics. Hence, privacy is a mandatory aspect to be assured, but there is a lack of studies regarding location privacy protection in IoD. Mix Zones is a location privacy protection mechanism well explored in terrestrial mobile networks, however, its investigation in IoD is still missing. In this work, we propose a novel Mix Zones-based approach, called MixDrones, that changes the airway of a drone besides its pseudonym. Hence, we advance the state of the art of location privacy protection mechanisms for IoD, in which MixDrones is the first approach proposed in this context. We carried out experiments through simulations comparing our approach with the traditional mechanism regarding anonymization coverage and resilience. We also evaluate the possibility of drone collision occurrence. The results pointed out that MixDrones provided a better location privacy protection than the traditional mechanism, anonymizing a large number of drones and being resilient through a trajectory-based de-anonymization attack, with less than 25% of trajectories being de-anonymized in all scenarios. Moreover, MixDrones mitigated the side effects of airway change, presenting low rates of airways change competition situations.

Brain-Computer Interfaces (BCI) based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm can be used to differentiate targets with different frequencies of scintillation through visual stimuli. This work aimed to evaluate the SSVEP stimuli of high and low frequencies for the development of a SSVEP-BCI system to assist in decision-making. Our main objective in this paper is to evaluate a high-frequency stimuli SSVEP system, which are not optimal for evoking the target signals. This experimental scenario is closer to real situations, a step towards BCI-based aid for decision-making in unconstrained situations. To that end, we performed experiments with four subjects to evaluate SSVEP stimuli at low frequencies, traditionally used in the paradigm, and high frequencies configured in a threshold not visible to the human eye, allowing the prototype to behave more closely to real-world situations while avoiding visual fatigue. The average accuracy among the four subjects was 89.37% ± 8.26% for low-frequencies and 80.62% ± 7.18% for high-frequencies. This indicates that it is possible to achieve acceptable results with high-frequency stimuli for decision-making.

This paper proposes the MannAccess, a novel low cost assistive educational tool of digital image for Visually Impaired (VI), aiding the teaching-learning process of visual content. It consists of an assistive environment composed of interactive software and a refreshable pin display with a novel 3-axis pin activation mechanism, decreasing its development cost substantially. The MannAccess allows the integration with different image recognition methods using a proposed image intermediary representation. In order to accomplish evaluations, we developed a prototype of MannAccess and integrated it with MannAR, an automata image recognition method. We carried out experiments with VI students which pointed out that our tool provided proper accessibility, usability, and user experience. In addition, we accomplished a monetary cost evaluation, indicating that MannAccess had the most accessible monetary cost compared to related devices. In a nutshell, MannAccess showed that it is possible to develop a low-cost assistive technology to aid the VI visual content education, integrating different image recognition methods in a single assistive tool.

This research-to-practice full paper deals with the difficulty that it is possible to teach computational thinking through the teaching of algorithms and programming to a person with ADD. We propose approaches to teach algorithms, knowing that it is a difficult task for many teachers. Likewise, inspiring the student's interest also has its difficulties. The Brazilian Computer Society (SBC) understands that it is fundamental and strategic for Brazil that computer content is taught in basic education. Considering the 4.0 education of a connected generation, the thinking of computer education is relevant to the construction of youth and adult education. When we include people with ADD, this challenge is not trivial. This work presents a case study carried out with a high school student who has ADD and the techniques that were used to teach him to program to maintain his interest in the area. The method used was divided into three stages: Computer Discovery (1); Introduction to computational thinking through programming (2); Learn by Doing, in line with real-world problem solving using pair programming technique and challenge-based learning strategy (3). In order to investigate the effectiveness of the developed strategies, an experimental case study was carried out with a student who has ADD. The student was introduced to the content and approaches for 34 weeks, totaling 204 hours. The student presented a strong indication of learning and will forever have the ability of computational thinking to program and develop their applications.

This work introduces a Brazilian Speech Database (BrSD), a novel dataset freely available created to support the development of speech-based recognition tasks. As far as we know, this is the first Portuguese language based database with these characteristics created and made available to the research community. We also describe experiments accomplished on BrSD exploring its different possibilities of classification tasks, i.e., age group and gender classification. We use four well-known acoustic features extracted directly from the audio signal and one texture-based feature extracted from a visual representation of the audio signal, the spectrogram. We considered three different classification scenarios: each feature individually, early fusion of the features, and late fusion of the features. Experiments were conducted using Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers. The obtained results showed that SVM classifier achieved the best recognition rates both in early and late fusion scenarios. The best recognition rates achieved were 91.25%, 88.75%, and 80.25% for gender, age group, and age-gender classification tasks, respectively.

Vehicular network applications are emerging to bring many benefits to the users during their journey. However, the design and deployment of such applications require studies that provide insightful information about the network formed by vehicles. To this end, in the last years researchers have been characterizing real mobility traces collected from taxis equipped with GPS devices. However, these traces present temporal and spatial gaps, as it was demonstrated in other studies. In this work, we compare the real original traces with their calibrated version (i.e., with no gaps) to show that the existing gaps expressively affect key metrics such as contact duration, inter-contact time, and network capacity. As result, we contribute to the research community by presenting important analysis of real vehicular network topology and by showing the importance of calibrating the traces before evaluating vehicular network applications.

MannaSim is an extension to the consecrated network simulation tool NS-2, to simulate Wireless Sensor Networks (WSN). However, MannaSim's power consumption module can be improved to include some influential factors in a WSN. The objective of this work is to present PowerMannaSim. This extension provides two important features for the simulation of energy consumption in a WSN. The first one is an estimating model for the number of clock cycles spent in a code that runs on the sensor node. The second is a realistic energy decay model of the sensor node's batteries, which is based on the discharge rate of the battery and considers factors such as the environment's temperature in which the sensor node is.

Most Vehicular Ad-hoc Network (VANET) applications require the delivery of a variety of content to vehicles. Furthermore, content in such applications is usually geo-localized (i.e., designated to a specific region of interest). To help with the delivery of geo-localized content in VANET applications, content replication strategies can be exploited to keep content available where the interested vehicles are expected to be. To this end, we propose a Geo-Localized Origin-Destination-based Content Replication (GO-DCR) solution that relies on the vehicles' origin and destination points to select the vehicles most likely appropriate to replicate content. We compare GO-DCR to an existing solution through simulation. The results reveal that GO-DCR increases the content availability while reducing delivery cost.

Simulation is the approach most adopted to evaluate Vehicular Ad hoc Network (VANET) and Delay-Tolerant Network (DTN) solutions. Furthermore, the results' reliability depends fundamentally on mobility models used to represent the real network topology with high fidelity. Usually, simulation tools use mobility traces to build the corresponding network topology based on existing contacts established between mobile nodes. However, the traces' quality, in terms of spatial and temporal granularity, is a key factor that affects directly the network topology and, consequently, the evaluation results. In this work, we show that highly adopted existing real vehicular mobility traces present gaps, and propose a solution to fill those gaps, leading to more fine-grained traces. We propose and evaluate a cluster-based solution using clustering algorithms to fill the gaps. We apply our solution to calibrate three existing, widely adopted taxi traces. The results reveal that indeed the gaps lead to network topologies that differ from reality, affecting directly the performance of the evaluation results. To contribute to the research community, the calibrated traces are publicly available to other researchers that can adopt them to improve their evaluation results.

Information dissemination applications (e.g. weather, news, advertisements, traffic, and so on) are among the most promising ones for vehicular networks. However, disseminating information to all vehicles in vehicular networks is not a trivial task because of their specific characteristics like high dynamic topology and different density values along time and space. The proposals found in literature focused on improved flooding schemes that, in realistic scenarios, may cause unnecessary overhead and redundant data. This work addresses the dissemination to all vehicles problem by assuming that some vehicles are more appropriate to be good disseminators than others. This hypothesis was validated through a large-scale urban mobility scenario and two heuristics were proposed to select the appropriate disseminators. Results showed that the proposed heuristics were able to achieve high coverage (more than 97% of vehicles) with low redundant data.

Vehicular Ad-Hoc Network (VANET) applications are context-aware since they need environment and local contexts to operate properly and help their users on many aspects, like indicating the best route to take or warning about accidents and traffic congestion situations. These applications need a context provider system responsible for collecting, analyzing, reasoning and making contexts available to them. In this work we propose a smart context provisioning middleware, called ConProVA, that provides context information to applications, is able to infer logical context and deals with conflicts of interest. We evaluate through simulation an instance of ConProVA considering an accident detection and avoidance application. The results show that it is possible to improve the users' satisfaction by reducing total travel time and carbon dioxide emission when accidents occur.

Evaluating large scale vehicular networks solutions is a great challenge once real experiments are not feasible yet and there are no large scale testbeds available. Thus, the most used evaluation technique is simulation, which requires a realistic mobility model to provide accurate results. However, building large scale realistic vehicular mobility models is a hard task and many important characteristics have been neglected, like the origin-destination (O-D) macroscopic one. In this work it is proposed a trace-based O-D macroscopic model inferred by the adoption of statistical techniques in order to characterize a validated traffic model. This work contributes to the research community by providing a more realistic O-D macroscopic model, which can be considered a complement for many traffic generators available in literature.

Ubiquitous systems are generally embedded into the environment and provide their services all the time and everywhere. In order to increase transparency and personalization, ubiquitous applications are normally context-aware, i.e., they use information about entities of interest to adapt their services. Since they are connected to everyday elements, such systems are frequently shared by two or more users, who may provide conflicting contextual data. Therefore, these systems can reach an inconsistent state, in which they are unable to decide how to perform their intended adaptations. This work proposes a novel methodology that detects and solves conflicts of interest for ubiquitous context-aware applications with different characteristics. Besides, the developed approach considers the trade-off between users' satisfaction and resources consumption in order to select and apply a conflict resolution algorithm. Results obtained through simulations showed that the proposed solution is flexible, dynamic, and able to provide users' satisfaction as well as to save system resources.

Context-awareness allows ubiquitous systems to adapt their services according to the current users' characteristics and the surrounding environment. Most of the context-aware applications found in literature only consider external contexts like location and temperature. However, internal contexts, like feelings and physiological needs, could also be relevant to adaptation decisions. Nevertheless, these contexts are hard to be obtained because, usually, they can not be gathered by physical sensors. In this work it is proposed a system that infers feelings and physiological needs based on the semantic processing of sentences posted in Twitter by the users of ubiquitous context-aware applications. The preliminary qualitative results show that the inference system is accurate when the sentences are well-formed and do not present typos.

Ubiquitous systems are generally embedded into the environment and provide their services all the time and everywhere. In order to increase transparency and personalization, ubiquitous applications are normally context-aware, i.e., they use information about entities of interest to adapt their services. Since they are connected to everyday elements, such systems are frequently shared by two or more users, who may provide conflicting contextual data. Therefore, these systems can reach an inconsistent state, in which they are unable to decide how to perform their intended adaptations. This work proposes a novel methodology that can detect and solve conflicts of interest for ubiquitous context-aware applications with different characteristics. Besides, the developed approach considers the trade-off between quality of services and resources consumption in order to select and apply a conflict resolution algorithm. Results obtained through simulations showed that the proposed solution can provide reasonable users' satisfaction levels, as well as to save system resources.

The context-aware computing is a research field that defines systems capable of adapting their behavior according to any relevant information about entities (e.g.,people, places and objects) of interest. The ubiquitous computing is closely related to the use of contexts, since it aims to provide personalized, transparent and on-demand services. Ubiquitous systems are frequently shared among multiple users, once they are designed to be embedded into everyday objects and environments such as houses, cars and offices. In scenarios where more than one user shares the same ubiquitous context-aware application, conflicts may occur during adaptation actions due to individual profiles divergences and/or environment resources incompatibility. In such situations it is interesting to use computer supported collaborative work techniques in order to detect and solve those conflicts, considering what is better for the group but also being fair enough with each individual demand, whenever possible. This work presents the important concepts on the collective ubiquitous context-aware applications field. Furthermore, it proposes a new methodology for conflicts detection and resolution that considers the trade-off between quality of services and resources consumption.

The context-aware computing is a research field that defines systems capable of adapting their behavior according to any relevant information about entities (e.g., people, places and objects) of interest. Ubiquitous systems are closely related to the use of contexts and are frequently shared among multiple users, once they are designed to be embedded into everyday environments such as houses, cars, and offices. In scenarios where more than one user shares the same ubiquitous context-aware application, conflicts may occur during adaptation actions due to individual profile divergences and/or environment resource incompatibility. In such situations it is interesting to use some mechanism to detect and solve those conflicts, considering what is better for the group but also being fair enough with each individual demand, whenever possible. This work presents the important concepts in the collective ubiquitous context-aware applications field through a disaster emergency attendance application. Furthermore, the proposal of a methodology for conflict detection and resolution is described. The obtained simulation results showed how this methodology can maintain the application working properly while solving the collective conflicts, managing resource consumption and providing an interesting user satisfaction average.

A fundamental goal of a wireless sensor network (WSN) is to collect and deliver data to external applications. Due to the strong constraints of these networks, routing algorithms usually consider application-specific characteristics and, consequently, there is no self-contained algorithm appropriate for every case. In particular, many WSN applications are event- driven. In such scenarios, the behavior of the network may vary a lot, which favors different algorithms at different instants. In these cases, hybrid adaptive solutions are more suitable by allowing the adoption of a better routing strategy in response to the variation of the network conditions. In this work, we evolve this approach by considering the temporal characteristic of event occurrence and detection through an estimation model. The proposed solution, called multi, predicts the need of routing infrastructure creation and maintenance for the adaptation between a reactive and a proactive strategy. To show the advantages of the proposed approach, an instance of multi is evaluated through simulations. Comparisons with its independent reactive and proactive components and other proposed solutions show improvements on energy consumption.

There are many approaches that can be applied to the management of wireless sensor networks (WSN) and nowadays the research in this field is only beginning. A management approach determines how the management monitor and control functions are performed by the network and network elements. Since a wireless sensor network presents severe resource constraints, a certain management approach must be chosen properly in order to optimize resource usage and at the same time satisfy the application requirements. In this paper we try to answer the following question: what is the tradeoff between to migrate management services or to adopt traditional approaches for WSN management? We have concluded from simulated and experimental results that the choice of the best approach to be adopted must consider the size of the network, the configuration of the nodes, the application goals and the management complexity, in terms of managed objects. We have also noticed that, despite the service migration approach being theoretically interesting for WSN management, for the sensor networks and sensor nodes hardware current technological situation, the approach would be applied with some negative impact on the performance of the network.

Wireless sensor networks (WSNs) are characterized by having scarce resources. The usual way of designing network functions is to consider them isolatedly, a strategy which may not guarantee the correct and efficient operation of WSNs. For this reason, in this paper we propose an integrated design of network functions. We take two important WSN functions - density control and routing - as an example and present two approaches to integrate them. In particular, we present two solutions, named RDC-sync and RDC-integrated, which integrate a geographical density control algorithm with tree routing. The simulations experiments performed prove that the integrated design improves the network performance, especially when density control and routing are fully integrated

The goal of a wireless sensor network (WSN) management solution is to promote network resources productivity and quality of services. This paper presents a comparative study of client/server (CS) and mobile agent (MA) approaches used in distributed self-management solutions for a hierarchical heterogeneous WSN, in which the managers are embedded into the cluster head nodes. These approaches were simulated and evaluated according to energy consumption. Considering energy consumption with transmission, the results reveal that the MA approach is more scalable than the CS one, tending to be more useful when the number of network elements and managed objects increases. On the other hand, the CS approach presents more interesting results regarding energy consumption with processing. Besides, the simulations performed also show that the MA size is an important parameter that impacts directly on the results

Wireless Sensor Networks (WSNs) have emerged as a new monitoring and control solution for a variety of applications. Although the behavior of a WSN is characterized by the type of its application, a common element exist: faults are a normal fact, not isolated events as in traditional networks. Thus, in order to guarantee the network quality of service it is essential for the WSN to be able to detect failures and perform something akin to healing, recovering from events that might cause some of its parts to malfunction. In this work we propose and evaluate a failure detection scheme using a management architecture for WSNs, called MANNA. We take a deep look at its fault management capabilities supposing the existence of an event-driven WSN. This is a challenging and attractive kind of WSN and we show how the use of automatic management services defined by MANNA can provide self-configuration, self-diagnostic, and self-healing (some of the self-managing capabilities). We also show that the management solution promote the resources productivity without incurring a high cost to the network.

A wireless sensor network aims to collect data and, sometimes, control an environment. This kind of network is composed of hundreds to thousands of devices that have the capability of sensing, processing and wireless communicating, called sensor nodes. The sensor nodes are projected with small dimensions (cm/sup 3/ or mm/sup 3/) and this size limitation ends up restraining the node resources, like energy, processor and transceiver capacity. The task of building and deploying management systems in environments where there will be tens of thousand of network elements with particular features and organization is very complex. This task becomes worse due to the physical restrictions of these unattended sensor nodes. In this paper we have implemented and evaluated some automatic services of configuration and performance management, proposed by a WSN management architecture called MANNA. This architecture is based on the paradigm of self-management, which contains the automatic functions and services of management using a minimum of human interference. This work aims to evaluate different WSN configurations considering an application of continuous data sensing and dissemination, and the effects of the management solution proposed for this network. The built application does temperature and carbon monoxide concentration level monitoring, in an urban area. The results show the cost-benefit relations of the different organizations and demonstrates that management can promote the productivity of the resources and control the quality of the provided services.

A wireless sensor network is a special kind of ad-hoc network with distributed sensing and processing capability that can be used in a wide range of applications, such as environmental monitoring, industrial applications and precision agriculture. Despite their potential applications, such networks have particular features imposed by resource restrictions, such as low computational power, reduced bandwidth and specially limited power source. In case of a network with a high density of sensor nodes, some problems may arise such as the intersection of sensing area, redundant data, communication interference, and energy waste. A management application is necessary to make the most of network resources. On the other hand, a high-density network can introduce a fault-tolerant mechanism, increase precision, and provide multi-resolution data. The network density control depends on the application. In this paper, we propose a method to set up which nodes should be turned off or on. The management may take the sensor node out of service temporally. Our design uses a Voronoi diagram, which decomposes the space into regions around each node. That schema could be used in management architecture for a wireless sensor network.