Efficient management of cloud storage resources requires intelligent tier allocation strategies that balance cost optimization with performance requirements. While previous approaches have focused on binary classification schemes for storage tiering, real-world scenarios demand more granular solutions that can adapt to diverse user preferences and workload characteristics. This paper extends our previous work on access frequency prediction by proposing a comprehensive multiclass machine learning framework for intelligent cloud storage tiering. The proposed framework incorporates a novel three-tier classification system ( Cold / Warm / Hot ) and integrates user-centric preferences through a cost-weight parameter, enabling dynamic adaptation to varying preferences along the cost-latency spectrum. We demonstrate the framework’s effectiveness through extensive experiments on real-world access patterns, where we assess the performance of thirteen machine learning algorithms under various user preference profiles. The results show that our multiclass approach achieves cost reductions of up to 40% compared to a static tiering strategy, while providing Pareto-optimal solutions for different user profiles. Through comprehensive Pareto frontier analysis, we demonstrate the framework’s ability to provide transparent trade-off visualization, enabling informed decision-making for cloud storage administrators. Our main contributions are: a multiclass classification approach for storage tiering, the integration of user preferences via parameterized optimization, a comparative analysis of multiple algorithms across different preference configurations, and a practical validation of the framework’s applicability in production cloud storage environments.

Advances in microelectronics and the evolution of IoHT devices allow the collection and, consequently, generation of a greater volume of health data, intensifying the need for robust data privacy solutions. Traditionalk-anonymity-based anonymization techniques often suffer from high information loss, especially as the anonymity parameter k increases. To address these challenges, this article proposes Generalization Firstk-Member Clustering (GFKMC), a novelk-anonymity method that applies early generalization to quasi-identifiers, reducing computational overhead and minimizing information loss. Unlike traditional methods (e.g., Mondrian, Top-Down Greedy (TDG), and Clustering-Based (CB)), GFKMC maintains nearly constant information loss (≈ 25%) across varyingkvalues and better preserves machine learning model performance, especially in lowkscenarios. Empirical evaluations demonstrate that GFKMC outperforms baseline methods by significantly minimizing the trade-off between data utility and privacy. Moreover, GFKMC preserves the performance of machine learning models more effectively.

Coarse-Grained Reconfigurable Architectures (CGRAs) have been the subject of extensive research due to their balance between performance, energy efficiency, and flexibility. CGRAs must be capable of executing a dataflow graph (DFG), which depends on a compiler producing quality valid mappings with feasible running time performance and portable mapping DFGs on different CGRA architectures. Machine learning-based compilers have shown promising results by presenting high quality and performance but offer limited portability. Moreover, some approaches do not explore efficient placement methods or do not demonstrate whether scaling to more challenging, less connected architectures. This paper presents SmartMap, an architecture-agnostic framework that uses an actor-critic reinforcement learning method applied to a Monte-Carlo Tree Search (MCTS) to learn how to map a DFG onto a CGRA. This framework offers full portability using a state-action representation layer in the policy network instead of a probability distribution over actions. SmartMap uses a graph traversal placement method to provide scalability and improve the efficiency of MCTS by enabling more efficient exploration during the search. Our results show that SmartMap has 2.81x more mapping capacity, a 16.82x speed-up in compilation time, and consumes fewer resources compared to the state-of-the-art.

The increasing volume of sensitive data generated by various domains demands robust approaches to privacy protection. Anonymization based on k-anonymity stands out for mitigating the risks of re-identification of personal data. However, the impact on the performance of machine learning models is commonly neglected. This work proposes a novel comparative method to evaluate the effects of anonymization on the performance of machine learning models, considering privacy, information loss, and performance metrics. The empirical results show how generalization based on k-anonymity impacts federated learning solutions and provides insights for developing and improving methods that reconcile data privacy and efficiency.

The growing prevalence of interconnected devices in the Internet of Things (IoT) has intensified concerns about data security and user authentication. This study investigates a multimodal biometric authentication approach integrating Photoplethysmography (PPG) and Electroencephalography (EEG) signals to enhance accuracy and robustness. We adapted a previously validated PPG and Electrocardiogram (ECG) model, replacing the ECG component with an EEG-based method optimized through hyperparameter tuning. Our findings demonstrate that combining brain and heart signals improves authentication performance, with the multimodal approach surpassing unimodal methods. The optimized model achieved a precision of $97.05 \%$, a recall of $97.19 \%$, a F1-Score of $96.96 \%$, and an accuracy of $97.05 \%$, highlighting the potential of EEG-PPG fusion in secure biometric authentication.

Cloud storage providers typically offer multiple tiers with differing performance and cost. Classifying data into correct tiers is challenging, given evolving access patterns. This paper presents a supervised learning framework to predict object access frequency, thus enabling cost-effective tier allocations. Using real-world Dropbox traces, our experiments show up to 37% cost savings compared to an online tiering baseline. We evaluate multiple machine learning methods and time-window strategies, demonstrating the trade-offs between cost optimization and recall (to avoid misclassifving frequently accessed objects).

One of the main events involving the world economy in 2022 was the beginning of the war between Russia and Ukraine. This event offers an opportunity to analyze how a large-magnitude world event can affect the use of cryptocurrencies. Ethereum is one of the most prominent and widely used cryptocurrency platforms and, as such, provides a valuable case study for this scenario. This work investigates the behavior of accounts and their transactions on the Ethereum network during this event. For this purpose, we collect all Ethereum transactions during two distinct periods: (i) during the month the conflict began, and (ii) during the previous year. We organized a dataset with the accounts involved in these transactions and the subset of these accounts that interacted with a service within Ethereum named Flashbots Auction. Flashbots Auction is crucial as it addresses issues regarding transaction ordering and miners exploiting that ordering to make profit. Then, we model temporal graphs in which each vertex represents an account, and each edge represents a transaction between two accounts. We analyzed the behavior of these accounts via graph metrics for both groups during each observed time window. The results show changes in account behavior and activity, as well as variations in daily transaction volume.

Technological advances enable the integration of Internet of Things (IoT) devices to perform continuous and proactive patient monitoring. These devices collect a large volume of sensitive data that requires privacy. Anonymization provides privacy by removing or modifying information that identifies an individual. However, traditional anonymization techniques, such as k-anonymity, depend on a fixed and pre-defined k value, susceptible to attribute disclosure attacks. This article presents Dynamic Anonymization by Separatrices (DAS), an approach for defining the ideal value k and for dynamic grouping of data to be anonymized using separatrices measurements. Results show that the proposed approach efficiently mitigates attribute disclosure attacks.

In an increasingly connected world, ensuring security in e-health is a challenge. Traditional security models based on perimeter trust are insufficient to guarantee the protection of these systems. Since these models work by directly assigning trust to the user, the entire network becomes vulnerable if the user’s credentials or device are compromised. Thus, this work proposes and evaluates a model based on Zero Trust to considerably increase security in e-health environments. The proposed model uses privilege reduction and user confidence analysis to perform access control. The evaluation follows simulation in different scenarios, assessing their assertiveness in delegating access. The results show the effective detection of anomalies in accesses by the model.

With the significant increase in mobile users connected to the wireless network, coupled with the escalating energy consumption and the risk of network saturation, the search for resource management has become paramount. Managing several access points throughout a whole region is hugely relevant in this context. Moreover, a wireless network must keep its Service Level Agreement, regardless of the number of connected users. With that in mind, in this work, we propose four prediction models that allow one to predict the number of connected users on a wireless network. Once the number of users has been predicted, the network resources can be properly allocated, minimizing the number of active access points. We investigate the use of Particle Swarm Optimization and Genetic Algorithms to hyper-parameterize a Multilayer Perceptron neural network and a Decision Tree. We evaluate our proposal using a campus-based wireless network dataset with more than 20,000 connected users. As a result, our model can considerably improve network performance by intelligently allocating the number of access points, thereby addressing concerns related to energy consumption and network saturation. The results have shown an average accuracy of 95.18%, managing to save network resources effectively.

Geopolitical conflicts significantly impact financial networks and systems, e.g., Russia and Ukraine. Cryptoeconomic blockchains such as Bitcoin and Ethereum were introduced as substitutes for traditional financial systems and might behave differently under significant stress. The Russia–Ukraine conflict allowed us to analyze the impact of such complex geopolitical conflicts on the user behaviors of cryptoeconomic blockchains. This article investigates the early stage of such geopolitical conflict using time-varying graphs. We collected and analyzed all the transactions for Bitcoin and Ethereum that took place 2 weeks before and after the conflict started, i.e., we focused on what can be defined as the acute impact of such an event. Our results suggest that the early stage of such geopolitical conflicts may significantly affect cryptoeconomic blockchains’ user behaviors. For instance, we detected that some users behaved more cautiously during the preconflict phase and resumed normalcy during the postconflict phase but exhibited a shift in their behavior. This article analyzes the relationship between the early stages of geopolitical conflicts and cryptoeconomic systems.

Automated Machine Learn (AutoML) process is target of large studies, both from academia and industry. AutoML reduces the demand for data scientists and makes specialists in specific fields able to use Machine Learn (ML) in their domains. An application of ML algorithms is over time-series forecasting, and about these, few works involve the application of AutoML. In this work, an AutoML approach that aggregates time-series forecasting models is proposed. Furthermore, a special focus is given to the optimization stage, which uses genetic algorithm to boost searching for hyper-parameters. In the end, results are compared with a recent time-series forecasting benchmark and we verify that the AutoML model proposed in this work surpasses the benchmark.

Brain-computer interfaces (BCI) are systems that use brain signals to communicate with and control devices, with applications ranging over multiple domains. In healthcare, one of the major applications of BCIs is neurorehabilitation. For example, BCIs help stroke patients recover motor abilities by providing sensory feedback based on imagined movement. Convolutional neural networks (CNN) can be used to classify such motor imagery electroencephalogram (EEG) signals and provide this kind of feedback. However, since these signals are usually noisy and can differ significantly over time and among people, it is frequently necessary to collect a large amount of data to train these models. This process can be time-consuming and fatiguing for the user, impairing the quality of neurorehabilitation treatments and other applications. This paper investigates how data augmentation can mitigate this problem by reducing the need for data and increasing feedback accuracy. We analyze five data augmentation methods from the literature on two motor imagery datasets. We apply data augmentation to a few-parameter CNN in varying settings of EEG electrodes, motor imagery tasks, and number of training samples. Our results show that data augmentation can reduce the amount of original data needed, leading to superior accuracy with 33.33 % fewer training samples in some instances. They also show that combining different data augmentation methods can further improve accuracy.

The network function virtualization paradigm replaces dedicated hardware appliances with software running on virtual machines in commodity servers. However, to overcome performance and scalability issues on virtual network functions (VNFs) demanding intensive packet processing, we propose implementing these VNFs as programmable rules distributed among software-defined networking (SDN) switches. Our approach allows infrastructure providers to combine hardware-based and software-based SDN switches, exploring the tradeoff between the packet processing speed and instantiation flexibility. Our proposal includes a scalable mechanism, which decides the type and number of switches to meet network demand, along with a load balancing mechanism to distribute flows among switches, considering their singularities. We first evaluate our proposal in a small testbed with real equipment, confirming its effectiveness. Then, we evaluate our proposal with simulations to further investigate its scalability. Our results show that, even under high traffic loads, the joint operation of both mechanisms improves VNF capacity and saves virtualization resources.

The performance of Attribute-Based Access Control is negatively affected by communications over the network between the policy decision point and policy information point for each attribute request. Attribute caching is a standard solution to mitigate this problem. However, due to the dynamic nature of attributes, the cost to keep them refreshed increases for each new attribute replica. This paper presents a method that predicts each request and anticipate the positioning of the attributes closer to the requester exploring the tradeoff between the cost of creating a new replica versus updating a replica. The proposed method follows a two-way approach, where it deals with the current attribute requests and their estimates based on user mobility and the best positions for the attributes. Through trace-driven simulations, considering traces from a large university campus, the method shows a reduction of up to 80% in the number of hops to get the needed attributes at negligible refreshment cost.

Over the years, wireless networks have been suffering a significant increase in the number of connected users, and dealing with this increase is extremely important in terms of economy and quality of service. In this work, a prediction model is proposed to improve this relationship, focusing on predicting the number of connected users to the wireless network. Our model consists of a particle swarm optimizer applied to the parameters of the Multilayer Perceptron neural network. The model was evaluated with real mobility data obtained from wireless networks with a total of more than twenty thousand users. The predictions made by the model allow allocating the network bandwidth efficiently, generating savings in the available resources. In fact, the simulated results indicate an average coefficient of determination of 94.08% and average savings of 67.31 % of the total available bandwidth.

The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarized, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data.

Brain-Computer Interface (BCI) is an emergent technology with a wide range of applications. For instance, it can be used for post-stroke motor rehabilitation in order to restore part of the motor control of someone injured in an accident. The BCI process involves the signal acquisition and preprocessing of data, extraction and selection of features, and classification. Thus, in order to have a correct classification of the movements, several filters are commonly used to handle the signal data. Here we propose the use of Differential Evolution (DE) with cross-entropy as the objective function to find an appropriate filter. Computational experiments are performed using 2 datasets from BCI competitions for motor imagery with signals of Bipolar and Monopolar Electroencephalography. Also, these problems involve two-class and multiclass classifications. The results show that the proposed DE obtained mean results 9.85% better than the well-known approach Filter Bank Common Spatial Pattern for BCIs with 2 classes for bipolar signals, and it allows for the reduction of the number of electrodes for BCIs with 4 classes.

Today, video streaming is a well-established application on the internet. Providers are able of transmitting video globally, in large-scale, although there are still some challenges, such as offering high video quality for all clients, particularly in live transmission, due to limited butter size and intense client arrival. Low quality result in low engagement (i.e. client permanence). To increase engagement, the provider can adopt strategies to anticipate (i.e. predict) quality losses. To make this possible, the provider needs to learn what session characteristics lead to an early session abandonment. If a new session presents such characteristics, the provider can presume that it will terminate prematurely. To implement an accurate prediction scheme we must choose a feature set able to explain the user permanence (i.e. engagement). Traditional prediction approaches use user-centered quality metrics such stall rate and average bitrate. In our data-set, these metrics give a prediction accuracy of less than 70%. To improve this, we propose a new set of metrics that explores the client adaptation flow. We model it through Markov chains and show that this increases the prediction accuracy up to 82%. Besides this, grouping similar sessions and train a predictor for each group can increase the accuracy. Finally, we present a use case for the predictor in order to investigate its feasibility in real systems.

Monitoring underwater environments is still a hard and costly task. Indeed, electromagnetic and optical waves suffer high attenuation, being absorbed in a few meters and even acoustic communication presents low throughput and high bit error rate. Most of the existing approaches to enhance underwater communication performance relies on developing acoustic modems, multiple access to the communication channel and, data routing. In this paper, we present COPPER: a COoperative Protocol for PERvasive Underwater Acoustic Networks. COPPER synchronously/asynchronously works on top of TDMA method combined with an ARQ scheme based on selective repeat technique. It uses idle sensor nodes as relay nodes, enhancing communication space diversity. Our simulations show that, when compared to a non-cooperative protocol, COPPER enhances overall network performance metrics. For instance, it reduces packet error rate by 28.32% and increases goodput by 16.87% while spending less than 1% more energy.

The specification of quality of service (QoS) requirements in traditional networks is limited by the high administrative cost of these environments. Nevertheless, newer network paradigms, as software-defined networks (SDNs), simplify and relaxes the management of networks. In this sense, SDN can provide a simple/effective way to develop QoS provisioning. In this paper, we propose a QoS provision architecture exploiting the capabilities of SDN. Our approach allows the specification of classes of service and also negotiates the QoS requirements between applications and the SDN network controller. The SDN controller, in turn, monitors the network and adjusts its performance through resource reservation and traffic prioritization. We developed a proof-of-concept of our proposal and, our experimental results show that the additional routines present low overhead, whereas -for a given test application- we observe a reduction of up to 47% in transfer times.

Software Defined Networks (SDNs) offer flexibility to current networks, allowing operators to manage network elements using software on an external server. SDNs are founded on a key feature: the separation of the control plane from the data plane. OpenFlow is the most popular SDN southbound interface today. However, does OpenFlow really decouple the data plane from the control plane? This is the leading question in this work. The literature has sought to quantity the impact of OpenFlow commands from control plane on data plane performance. Particularly, we argue that it is possible to damage the date plane by too many flow updates. Attackers, for instance, can use this effect in a cloud environment to reduce the performance of a collocated virtual network. However, it is not clear what is the exact impact of this coupling on production hardware and software switches. We investigate this through experiments, under representative scenarios, and propose a threshold mechanism to mitigate the effect of malicious administrators. We have observed that both hardware and software switches suffer from this limitation, presenting an average RTT degradation of up to 12.35% in the hardware switch, and 25.9% on the software switch. Finally, the proposed mechanism mitigates the lack of decoupling and malicious behavior.

Understanding the impact of performance degradation on users' QoE during live Internet streaming is key to maximize the audience and increase content providers' revenues. It is known that some problems have a strong correlation with low QoE--e.g., users experiencing video stalls tend to leave video sessions earlier. It is, however, mostly unknown whether such observations hold for live streaming of large-scale events (e.g., the FIFA World Cup). Such events are particular due to the widespread interest in the streamed content, reaching an impressively high audience worldwide. We study whether and to what extent performance degradation during live streaming of large-scale events affects users' QoE. We leverage a unique dataset collected from a major content provider in South America during the 2014 FIFA Soccer World Cup. We first extract performance metrics from the logs: stream bitrate, bitrate switches, playback stalls, and playback startup latency. We then correlate these performance metrics with session duration, which we use as a QoE indicator. We confirm the strong correlations between the metrics and QoE indicators; in particular, frequent stalls are often accompanied by higher probability of early session termination. Moreover, we quantify how such correlations vary according to broadcast matches and client terminals. Some of our findings challenge intuition--e.g., we find that PC users seem more tolerant to problems than users on mobile terminals. Our results provide better understanding of user QoE and are an important step towards user QoE models in large-scale events.

The decoupling of data and control planes of network switches is the main characteristic of Software Defined Networks. The OpenFlow (OF) protocol implements this concept and it is found today in various off-the-shelf equipment. Despite being widely employed in industry and research there is no systematic evaluation of OF data plane performance in the literature. In this paper we evaluate the performance and maturity of the main features of OF 1.0 on nine hardware and software switches. Results show that the performance varies significantly among implementations. For instance, packet delays vary by one order of magnitude among the evaluated switches, while the packet size does not impact the performance of OF switches.

Personal Cloud Storage (PCS) is a very popular Internet service. It allows users to backup data to the cloud as well as to perform collaborative work while sharing content. Notably, content sharing is a key feature for PCS users. It however comes with extra costs for service providers, as shared files must be synchronized to multiple user devices, generating more downloads from cloud servers. Despite the increasing interest in this type of service, a thorough investigation on the costs and benefits of PCS for service providers and end users has not been conducted yet. To that end, we propose a model to analyze cost-benefit tradeoffs for both parties. We develop utility functions that capture, in an abstract level, the satisfaction of the service provider and users in various scenarios. Then, we apply our model to evaluate alternative policies for content sharing in PCS. We consider two alternative policies for the current PCS sharing architecture, which count on user collaboration to reduce providers' costs. Our results show that such policies are advantageous for providers and users, leading to 39% utility improvements for both parties, while requiring low commitment of resources from participating users.

Health monitoring over wireless networks is at the same time increasingly popular and a challenging task. In fact, in a medical environment, the high density of wireless devices leads to an extensive amount of co-channel interferences, imposing risk to patients' life due to poor network performance (e.g. high latency and packet loss rate). In this work, we present a DynamiC distributed Channel Allocation (DCCA) protocol. DCCA takes into account the existence of co-located wireless body area networks (WBANs) and multiple base stations in a single medical environment. In other words, DCCA avoids co-channel interference and offers workload balancing among base stations by dynamically allocating channels based on a greedy solution to the graph-coloring problem. Simulation results from medical environment scenarios show that DCCA improves the quality of communication when compared with other representative frequency- allocation protocol. On average, DCCA increases 30% the network goodput and reduces 40% network latency.

Content Centric Networking (CCN) represents an important change in the current operation of the Internet, prioritizing content over the communication between end nodes. Routers play an essential role in CCN, since they receive the requests for a given content and provide content caching for the most popular ones. They have their own forwarding strategies and caching policies for the most popular contents. Despite the number of works on this field, experimental evaluation of different forwarding algorithms and caching policies yet demands a huge effort in routers programming. In this paper we propose SDCCN, a SDN approach to CCN that provides programmable forwarding strategy and caching policies. SDCCN allows fast prototyping and experimentation in CCN. Proofs of concept were performed to demonstrate the programmability of the cache replacement algorithms and the Strategy Layer. Experimental results, obtained through implementation in the Mininet environment, are presented and evaluated.

Measurement and tracking have crucial roles in Software-Defined Networks (SDNs). Unfortunately, most of procedures and techniques to perform measurements and monitoring tasks are implemented in software at network end-hosts. Despite the large use, a software based approach generates imprecision, high costs, and makes monitoring more difficult. In this paper, we extend OpenFlow switch to implement a measurement architecture for SDN. Our system performs measurements in a simple and scalable way without depending on end-hosts. It allows monitoring the performance at the granularity of flows. Moreover, our system also enables software-defined measurements can collect flow's statistics on the fly. We have prototyped our architecture on the NetFPGA platform and, as an initial case study, we have implemented a module to measure packet interarrival time. This module has been validated in a realistic testbed. Our results demonstrate that the proposed architecture presents a negligible difference when compared to measurements performed by software at end-hosts.

Power line communication systems face several challenges which degrade data communication quality. To overcome such issues, we propose CodePLC, a network coding Power Line Communication MAC protocol. We use a single relay node to intermediate communication, storing, and forwarding linear combinations of data packets. We evaluate CodePLC performance through simulations of a common topology for a PLC system under a wide range of scenarios. In sum, our results show that in a broadcast like transmission, the use of network coding enhances overall system performance. When compared to a traditional PLC system, we have observed an average of 115% goodput increase. Moreover, our protocol reduces in 112% the average of network occupancy buffers. Finally, CodePLC reduces mean latency by four times.

In this paper we characterize the impact of failures in Brazil's national research network (RNP) on traffic at a large client university. We analyze reachability disruptions, caused by failures of RNP's interdomain links, that block all international traffic. We also analyze performance disruptions, caused by simultaneous failure of multiple RNP intradomain links, which result in congestion and performance degradation. We study the impact of disruptions on traffic, application mix, and user behavior. Our results show that users adapt their behavior when some applications become unavailable and when network performance degrades. For example, users tend to migrate to Youtube when Facebook becomes unavailable during reachability disruptions; similarly, users migrate to Facebook when congestion during performance disruptions severely degrade Youtube experience. We also correlate the impact of disruptions to network topology and show that performance during a performance disruption depends on the location and importance of failed links.

Cloud storage systems are currently very popular, generating a large amount of traffic. Indeed, many companies offer this kind of service, including worldwide providers such as Dropbox, Microsoft and Google. These companies, as well as new providers entering the market, could greatly benefit from knowing typical workload patterns that their services have to face in order to develop more cost-effective solutions. However, despite recent analyses of typical usage patterns and possible performance bottlenecks, no previous work investigated the underlying client processes that generate workload to the system. In this context, this paper proposes a hierarchical two-layer model for representing the Dropbox client behavior. We characterize the statistical parameters of the model using passive measurements gathered in 3 different network vantage points. Our contributions can be applied to support the design of realistic synthetic workloads, thus helping in the development and evaluation of new, well-performing personal cloud storage services.

P2P live streaming are increasingly popular nowadays. Due to their popularity, these systems may be a target of attacks and opportunistic user behavior. In this paper, we address the pollution attacks in such systems. We present a pollution damage model and also analyze a reputation system as a tool to fight attacks in P2P live streaming systems. The model we propose evidences that attacks are harmful even in a system with a small number of polluters. In this case, peers must have more than 3 times network bandwidth than they should have in a system without polluters. Our experimental results on PlanetLab show that just check data integrity is not an effective protection. In this case, we observe a very high data loss rate. Finally, the reputation system is effective against pollution attack. When peers do not whitewash their identities, the reputation system quickly identifies polluters. In this case, the overhead and loss rate can be negligible. During a whitewashing, the new approach presents less than 20% overhead and 3% of loss.

P2P-TV applications have attracted a lot of attention from the research community in the last years. Such systems generate a large amount of data which impacts the network performance. As a natural consequence, characterizing these systems has become a very important task to develop better multimedia systems. However, crawling data from P2P live streaming systems is particularly challenging by the fact that most of these applications have private protocols. In this work, we present a set of logs from a very popular P2P live streaming application, the SopCast. We describe our crawling methodology, and present a brief SopCast characterization. We believe that our logs and the characterization can be used as a starting point to the development of new live streaming systems.

Peer-to-Peer live streaming systems help content providers and distributors drastically reduce bandwidth costs by sharing costs among peers. Researchers have dedicated significant effort developing techniques to discourage or exclude uncooperative peers from peer-to-peer systems. However, users are often unable to cooperate, e.g., users using a mobile device with limited, costly bandwidth. We study the impact of uncooperative peers on video discontinuity and latency using PlanetLab. We find that simple mechanisms, like forwarding video data requests to cooperative peers instead of wasting effort sending requests to uncooperative peers, allows peer-to-peer live streaming to serve 50% of uncooperative peers without performance degradation. We argue that denying service to uncooperative peers may not be the best long-term approach; our findings suggest that peer-to-peer live streaming can support uncooperative peers.

The research of underwater sensor networks (UWSNs) is gaining attention due to its possible applications in many scenarios, such as ecosystem preservation, disaster prevention, oil and gas exploration and freshwater reservoirs management. The main elements of a UWSN are underwater sensor nodes (UWNs). In this paper we present HydroNode: a low cost, energy efficient, multipurpose underwater sensor node (UWN). Nowadays, to the best of our knowledge, there is no UWNs that is simultaneously low cost, low power, able to couple diverse types of sensors and educationally available. Thus, the objective of this paper is to fill this gap by describing the design of HydroNode, an underwater sensor node that fulfill all these requirements and can be used in various UWSNs applications. We used only commercial off-the-shelf components to build our underwater sensor node. Due to its multipurpose design, HydroNode can be used in different UWSNs, therefore aiding the research of UWSN system protocols, configurations and applications.

Peer-to-Peer live video streaming systems are becoming increasingly popular. Nevertheless, in spite of various studies of client behavior aspects and system optimizations, the current knowledge about the dynamic properties of the system, particularly how the P2P overlay network changes over time during a live transmission, is still superficial. In this paper, we provide a characterization of the dynamic properties of a popular P2P live streaming media application, namely Sop Cast. We use complex network metrics to analyze how the structure of the network evolves over time from the perspective of individual nodes (local view) and of the whole network (global view). We find that Sop Cast peers may be clustered into three profiles based on their centrality properties in the network. Moreover, in spite of peers changing their partners over time, they tend to remain with the same centrality profile. Also, the global network structure tends to remain roughly stable over time, except for a decaying clustering coefficient. Our findings can be used to generate more realistic synthetic P2P workloads and to drive future system designs and simulations.

Peer-to-peer live streaming media systems are becoming more popular each day. As in file sharing P2P system, they are susceptible to content pollution attack. In this kind of attack, a peer alters the media content decreasing the perceived quality of the streaming. In this paper we evaluate the impact of pollution attack in P2P live streaming and we present two reputation system to avoid content polluted dissemination and isolate malicious peers. Our results show that a few number of polluters is capable to compromise all the application and the 2 proposed reputation systems can quickly identify and isolate polluters and also be resistant to peers collusion.

This paper provides an extensive analysis of pre-stored streaming media workloads, focusing on the client interactive behavior. We analyze four workloads that fall into three different domains, namely, education, entertainment video and entertainment audio. Our main goals are: (a) to identify qualitative similarities and differences in the typical client behavior for the three workload classes and (b) to provide data for generating realistic synthetic workloads.

Power consumption became a crucial problem in the development of mobile devices, especially those that are communication intensive. In these devices, it is imperative to reduce the power consumption devoted to maintaining a communication link during data transmission/reception. The application of dynamic power management methodologies has contributed to the reduction of power consumption in general purpose computer systems. However, to further reduce power consumption in communication intensive real-time embedded devices, we have to consider the state of the computation and external events in addition to power management policies. In this paper we propose a model of an Extended Power State Machine (EPSM), where we adapt a Power State Machine to include the state of an embedded program in the power state machine formulation. This EPSM model is used to adapt the Quality of Service (QoS) in communication intensive devices to ensure low power consumption. In such development, a middleware layer fits in the system's architecture, being responsible for intercepting the data communication and implementing the EPSM. Also, a software tool was developed, allowing the Middleware Code to be generated based on the State Machine. A case study demonstrates the application of the proposed model to a real situation.

Video on Demand (VoD) services are very appealing these days. In this work, we discuss four distinct alternatives for the architecture of a VoD server and compare their performances under different conditions. We later used the best configuration found in our analysis to evaluate, using simulations, the performance of a distributed VoD service in an ATM network covering an area the size of a large city neighborhood. We also introduced optimizations to the system, such as anticipated delivery and retrieval of video blocks from neighbor clients. Our results indicate that a server-driven operation mode (i.e., based on cycles) is not the most appropriate choice for a variety of workloads, even when the layout is striped (a result that challenges the conventional wisdom in the field). Also, our optimization strategies increased significantly the number of clients served in the system, which in our study case represented a savings of approximately 33% in the hardware required for the deployment of the service.