This article presents a system that harnesses dormant resources from Personal Computers (PCs) at the edge, with the aim of executing cloud native microservices. The system encourages PC owners to willingly share resources to profit through a resource pricing mechanism that takes into account electricity costs and PC performance. Concurrently, the system enables cloud native microservices developers to set their maximum valuation for PC resources, intending to execute such services at the edge. The system identifies real-time idle resources and strategically allocates them across multiple geographical regions, enabling microservices deployment closer to end-users, thus diminishing communication latency. Utilizing double auctions, the system efficiently matches PCs to microservices in their respective regions. Detailed measurements in comprehensive simulations substantiate the efficacy of the system, ensuring benefits for PC owners and, concurrently, a decrease in operational costs for microservices developers.

Aligning corporate support functions, such as Information Technology (IT), with business objectives is a good strategic practice. To spread risks and reduce costs, a company may contract some services to outside providers – in what is known as outsourcing, while insourcing some other services, by leveraging its own capabilities. Deciding on sourcing options to add business value is not trivial. It requires knowing and leveraging the company’s own capabilities and effectively managing sourcing customer-provider complex dynamics. Said dynamics are often subject to varying degrees of feedback and delays that evolve from outsourcing contracts. Such complexity causes managers’ decision making to be usually ill-informed about implications to the business. The specialized literature offers little insight on how and with what to assess such implications. This paper contributes to the literature by presenting a System Dynamics (SD) simulation model for the interactions among the customer’s capabilities and those of the provider. The model breaks down complexity by offering complementary SD views, allowing for what-if analyses to inform decisions on sourcing options that favor intended business results. The model aligns sourcing strategies with the business through (dynamic) Balanced Scorecard concepts. The paper assesses how capabilities of Contract Monitoring and Service Delivery affect the earned business value of IT service outsourcing orders. Validation of the model was carried out in a multi-year case study at a taxation agency in Brazil.

Several places spread over developing countries have no telecommunications infrastructure, leaving their population disconnected from the world. Internet Service Providers have minimal incentives to establish themselves in such areas due to low expectations on return of investments. Given recent advances in LoRa mesh networking, this paper evaluates the applicability of a wireless infrastructure based on LoRa to distribute WiFi access points to users in remote locations. To this extent, the proposed architecture relies on low-cost ESP32 devices with LoRa and WiFi transceivers, whose low power consumption enables nodes to become off-grid by the usage of solar panels. Preliminary results of the aforementioned mesh network are presented to verify the validity of this solution, which aims to connect those people in need of technology and information.

With the rapid growth of vehicles, the vehicular networks meet main challenges such as dynamic, heterogeneous, and large scaled. In addition, the cellular-based vehicular networks must satisfy further strict requirements, including ultralow latency, high reliability, high spectrum efficiency, and massive connections of the next-generation (6G) network. Recently, by exploiting vehicle clustering utilized for reducing the complexity of vehicle-to-everything (V2X) systems, it could ultimately improve road traffic efficiency. In some specific scenarios related to Internet of Things, a group of vehicles can be served effectively in terms of spectrum efficiency when two key techniques are enabled, i.e., nonorthogonal multiple access and cognitive radio (CR) schemes are joint deployed. These techniques certainly benefit to 6G V2X services to reduce specific challenges, such as traffic congestion and massive connections. Different from existing works, we propose wireless power transfer applied to the roadside unit to improve the situation that energy shortening in small devices deployed in V2X communications. In particular, we derive expressions of throughput to exhibit performance of the two grouped vehicles. To further indicate advantages of spectrum efficiency, we compare two schemes of V2X systems with and without CR schemes. The numerical results demonstrate that the non-CR NOMA-V2X scheme outperforms the CR-based NOMA-V2X scheme with fixed power allocation, but the non-CR NOMA-V2X scheme costs higher spectrum resource compared with the counterpart. Besides, by comparing with orthogonal multiple access (OMA)-assisted V2X, NOMA-V2X schemes demonstrate superiority in terms of throughput performance whilst achieving the benefits of both NOMA and CR schemes.

The papers in this special section focus on artificial intelligence-drive applications for synthetic biology that promote human well being. Synthetic biology is an important branch of biological science, which is different and even completely opposite from traditional research direction on biology. It is obvious that synthetic biology will promote the next biotechnology revolution. At present, related researches are not limited in the painstaking splicing of genes, but have begun to construct genetic codes in order to construct new organisms using synthetic genetic factors. Specially, it is estimated that synthetic biology will have excellent application prospects in many fields, including the production of more effective vaccines, new drugs, biology based manufacturing, the production of sustainable energy, the biological treatment of environmental pollution, and biosensors that can detect toxic chemicals. In this way, synthetic biology is expected to make rapid progress in the next few years.

Providing Infrastructure as a Service (IaaS) is becoming increasingly competitive due to its growing world market.IaaSrequires significant physical resources, e.g., network connections, bandwidth, load balancers, and servers.IaaSproviders of any size and reach, but especially private and public regional ones, have to manage the capacity of their resources properly to reduce costs and meet other business goals. Comprehensive capacity management byIaaSproviders involves objective and subjective analyses and it is thus, challenging. Complexity arises, for instance, as one tries to factor management behavior in analyses of business impact and infrastructure performance to produce indicators in support of interventions related to service capacity.IaaSmanagers frequently rely on their own expertise, experience, and knowledge to identify whether an apparent capacity deficiency may be due to an infrastructure problem or to an unexpected demand burst. This article presents a business-driven simulation model based on System Dynamics (SD), Balanced Scorecard (BSC) and Analytic Hierarchy Process (AHP) concepts to support capacity management decisions byIaaSproviders in real-life operating scenarios. A case study in a real-world, public regionalIaaSprovider illustrates the model’s support to non-trivial decisions that are aligned with managers’ experiences and business expectations.

E-health can be defined as a set of health-related computing solutions that uses the Internet to provide services. In these systems, managing the sharing of the various types of generated data is a challenge, making it difficult to develop and maintain. A software intensive system is a system in which software essentially influences the design, construction, deployment, and evolution of the system as a whole to encompass individual applications, subsystems, systems of systems, and aggregations of interest. Blockchain has emerged as a solution to provide privacy and data security, providing interesting solutions for E-health applications. The objective of this work is to present architectural and behavioral aspects of E-health systems, using Blockchain. In addition, some research opportunities are also presented.

Vehicular applications are becoming increasingly popular. Some of them are known to be compute-intensive as to require real-time processing. One technique used to improve the performance of these applications is task offloading, which allows computational tasks to be processed on remote servers. In vehicular environments, these servers can be the vehicles themselves or edge servers coupled to base stations. But it is challenging to apply this technique in these environments, where frequent changes in network topology occur, and there is no central coordination point. Therefore, we propose a scheme to improve the performance of computationally intensive applications while dealing with the mobility challenges of vehicular environments. The proposed scheme runs on multi-interface networks WAVE and 5G and searches and prioritizes, through a lightweight greedy algorithm, for servers with high processing capabilities available and shorter distances. The results show that the proposed scheme reduces up to 54.1 % the total offloading time and increases up to 71.8 % the offloading success rate compared to other schemes. In addition, this is one of the first works to compare different task offloading schemes for vehicles using WAVE and 5G multi-interface networks.

Right after the Chinese example in conducting COVID-19 epidemic originated in Wuhan, the readiness to detect and respond by health authorities to local (sometimes global) epidemics has become central lately. Within the idea of health 4.0, information about the individual is essential in supporting public community health policies. This paper presents a proposal for an epidemiological surveillance system applied to arboviruses. Data mining techniques and Machine Learning (ML) are used to design mathematical models for detecting epidemics enhanced by Aedes Aegypti (vector for dengue, chikungunaya, yellow fever and zica). Based on data, it is proposed an adaptive manner to reach better stability on results. A Prove of Concept (PoC) is presented for dengue epidemics detection, a common endemic disease in the semiarid region of Brazil.

Given the advance of the Smart Grid technology, the electrical grid is now in a process of transformation never seen in history. This new grid model allows customers and providers to generate and manage electrical power efficiently. To support such transformation, a key element of the model is the communication network responsible for delivering measurement data from sensors on the grid to the applications that analyze and adapt distribution according to demands. This work proposes and evaluates a system architecture that copes with reliability and latency demands that are of much importance for a successful Smart Grid deployment, also considering the internal design of distributed applications in the context of Smart Grids. The architecture adopts a Mobile Edge Computing based infrastructure that is suitable for 4G and 5G mobile cellular networks. Experiments made on NS-3 shows that the proposed system architecture is capable of handling a realistic number of smart meters while providing low latency and network reliability, supporting many use cases of a Smart Grid deployment.

With the increasing number of devices connected to the internet and scarcity of IPv4 addresses, the IPv6 protocol was designed to address various of the limitations of IPv4 networks, supporting 128-bit addresses, deploying a fragmentation mechanism aiming better routers performance, use of multicast instead of broadcast and more. However, challenging security issues have been discovered on its design, with researches actively working to reduce the impact of cyber attacks exploring such breaches. One of these issues allows malicious IPv6 nodes to pretend they are actual gateways of a network, allowing for Denial-of-Service and Man-in-the-Middle attacks. This work proposes the Fingerprint-based Router Advertisement Guard (FibRA-Guard), a mechanism to avoid the spread of malicious Router Advertisements on wireless networks, relying on non-intrusive fingerprint authentication of sources of suspicious Router Advertisements based on the time each device takes to generate and send IPv6 packets. On a testbed with 15 devices, FibRA-Guard was able to identify sources of packets with an average accuracy of 96%, proving to be a reliable solution towards wireless IPv6 networks secured against malicious Router Advertisements.

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.

Mobile cloud computing is an approach for mobile devices with processing and storage limitations to take advantage of remote resources that assist in performing computationally intensive or data-intensive tasks. The migration of tasks or data is commonly referred to as offloading, and its proper use can bring benefits such as performance improvement or reduced power consumption on mobile devices. In this paper, we face three challenges for any offloading solution: the decision of when and where to perform offloading, the decision of which metrics must be monitored by the offloading system, and the support for user’s mobility in a hybrid environment composed of cloudlets and public cloud instances. We introduce novel approaches based on machine learning and software-defined networking techniques for handling these challenges. In addition, we present details of our offloading system and the experiments conducted to assess the proposed approaches.

Fog and Mist computing exploits computational resources of IoT devices located at the edge of the network. This new infrastructure brings benefits related to latency and Internet bandwidth utilization when compared with Cloud computing. However, this scenario is quite challenging mainly due to the considerable heterogeneity of the devices and to the dynamicity of the network topology what adds new issues related to security, privacy, data availability, and service availability, among others. Despite the theme of data dissemination and data storage had been relatively explored in fields of Sensor Wireless Network (WSN) and Vehicular Ad Hoc Networks (VANETs), we found few studies in the Fog computing research area. Then, this work focuses on the problem of data availability and dissemination in this environment by proposing a distributed algorithm using ideas from evolutionary computation and epidemic models. Results obtained through simulation suggest that the algorithm presents proper results in maintaining data availability in the Fog and Mist Computing environments, even under severe conditions.

Hybrid computational clouds are considered an interesting choice in the usage of computational resources in terms of elasticity. However, proper management of elasticity is necessary because computational resources can be provisioned inefficiently, resulting in idle or waste. This paper proposes an evaluation of the elasticity effects on a hybrid cloud environment. As the main result, we found that the elasticity keeps the quality level set to meet the applications running in the hybrid cloud with different workloads.

With the goal of simplifying the design and development of applications that use computation offloading, we developed the CAOS D2D platform to provide an abstraction layer for dealing with low-level tasks related to offloading of methods among Android mobile devices. This paper presents experiments performed to evaluate different aspects of the CAOS D2D, such as execution time and energy consumption of devices during method offloading. Besides that, we also performed experiments to evaluate the process of applications' dependency deployment. The experiments show that computation offloading can speedup the execution time by up to 4.55 times and reduce the power consumption by up to 88% in comparison to local executions of the same methods.

Internet of Things (IoT) has emerged due to several advances in microelectronics and wireless communication technologies. IoT can be considered as a set of small devices, called smart objects, capable of monitor and act in the environment in which they are inserted. Many research has come up from IoT, such as precision farming, smart cities, healthcare, and wearable. In IoT domain, healthcare has been highlight due to the growing demand of the population for monitoring people belonging to health risk groups. In this paper, we propose an approach for monitoring people who are part of risk groups, for example, sedentary people, using IoT technologies. Our approach monitors users' physiological data through wearable devices and sends this data to a computational cloud. In the cloud, these data are processed, stored and presented to the healthcare professional, who will monitor and establish new activities for the user. In our work, we built a mobile and web applications, and we used collected data from sensors and a dataset of health data.

Currently, mobile applications have become increasingly complex. One trend is the usage of contextual information gathered from device sensors and external data sources, which are used to improve the user experience when using mobile applications. As the amount of sensing data increases as well as the complexity of context inference procedures, it leads to an undesirable situation where the mobile device has not enough resources (e.g., memory capacity, processing speed, and battery power) to perform some computation tasks. This paper addresses this issue by proposing CAOS, a software platform that employs the offloading approach to enhance mobile context-aware applications. CAOS allows mobile application developers to offload rocesses and data into cloud and cloudlets infrastructures, aiming at improving the overall application performance and saving battery power. In order to assess our proposal, a reference implementation was built, and experiments were performed in order to evaluate performance and energy consumption of our proposal. Results showed gains in both aspects in most of scenarios, which denote the feasibility of the proposed solution.

In the ecosystem of Internet of Things, many applications require external computational resources with quick response. In this context, a possible solution is to use computational resources from other devices that are located nearby the host device. This new environment is named Fog computing and creates several opportunities and challenges. One these challenges, which is the focus of this work, is how to allocate available resources in a simple and effective way for a client device that you want to use these features. The proposal "Smart Shadow" is an autonomous platform availability allocation of computer resources in a Fog computing infrastructure based on learning from previous event to support mobile hosts devices.

In this paper, we present an innovative mixed pedagogical method to improve teaching and learning in undergraduate courses related to computer area. The method proposes learning multi-criteria assessments and pedagogical strategies, such as problem-based learning, that should be carried out during the activities. This research was performed in a three-year case study in courses undertaken at the Federal University of Ceará (UFC), Brazil. The main contribution of this work is the mixed pedagogical method that uses the combination of important pedagogical strategies, with promising results. There was observed an improvement in student results and professor evaluations in four of the five cases. In these four courses, researchers used the method with all requirements being met.

Media streaming services are responsible for the significant increase of Internet traffic. This fact generates the need for better computing resources management in order to maintain such services always available. Cloud computing offers an elastic infrastructure that provides computing resources on demand in order to maintain such services always available. Moreover, emerging network architectures, such as Software Defined Networks, provide mechanisms that simplify the implementation of advanced network functions. In this context, this paper proposes an elastic approach to manage a video streaming service. The solution includes an OpenFlow-based load balancer, and an elasticity management mechanism that increases or decreases the amount of active streaming servers, considering customer's demand. A prototype was developed using a private cloud infrastructure and experiments were performed to show the viability and effectiveness of our solution, in terms of load balancing and elasticity functionality.

Information technology (IT) services have become more complex and diversified. The service desk establishes a single channel of communication between the internal IT provider and its users, through which incidents and problems are analyzed, escalated and resolved. Managers need to analyze different operating scenarios regarding service desk capacity to make effective decisions about what should be improved to meet the business demands. This paper presents a system dynamics model for evaluating an organization's service desk's service capacity. The model allows the performance simulation of scenarios, estimating the service capacity. Preliminary results are promising and seem to contribute to the improvement of capacity management processes.

An effective decision-making about using Infrastructure as a Service (IaaS) resources in cloud computing projects is still a challenge to managers. We need to optimize resources use in cloud services, to obtain financial success in cloud projects. In this work, we propose a Petri net framework to model possible cost savings using public clouds spot instances pricing scheme. The results from initial simulations indicate that spot instances can be a very interesting option for savings in autoscaling process.

The search for better execution times was one of the motivations for the emergence of High Performance Computing (HPC) whose importance has obtained significative preponderance in the domain of scientific computing, either in applications for an academic or industrial environment. Cloud Computing proposes the integration of various technological models for the provision of a hardware infrastructure, development platforms and applications as services based on-demand. However, with the emergence of cloud computing, a new challenge arose which was to allow the execution of HPC applications in this new environment. This paper aims to demonstrate the feasibility of using a local cluster built on a private cloud for HPC experiments, and compare with applications running in a real environment for HPC.

Cloud computing has become a very popular technology for IT infrastructure (infrastructure as a service - IaaS), with big players offering attractive pricing schemes. Software vendors are interested in offering products in the cloud as services. However, in order to evaluate software as an offer of service (SaaS) for a given enterprise application, we need to assess some qualitative factors other than just price-security and trust being the most relevant factors. In this work, we propose a Petri net multi-criteria decision making (MCDM) framework to evaluate SaaS as opposed to an on-premises alternative. The proposed framework can help IT managers in deciding between the two options, as well as being able to help software vendors in their efforts to establish SaaS pricing.

The eHealth technology will bring many benefits for patients, healthcare professionals, and institutions, especially in developing countries, such as Brazil. The eHealth solutions will enable faster, safer, and better healthcare services through the integration of Information and Communication Technologies (ICTs) with eHealth issue. This paper gives an overview of the Context-Aware Mobile Approach (CAMA) on going research activities for eHealth environments. CAMA seeks to improve the Brazilian primary healthcare system through context-aware eHealth facilities, expecting to change the way that healthcare services are applied, managed, and maintained to achieve efficiency, safety, and reliability for both patients and hospital staffs.

Wireless Sensor Networks and Radio Frequency Identification are two technologies that are being used together to monitor and identify objects and people. Their application may cover from a supermarket storage section to a forest monitoring process, having to face a diversity of problems within the scope of Computer Science and Engineering. The present work proposes the use of these technologies in the scope of Education - more specifically in the area of laboratory practice, using a formal modeling technique called Coloured Petri Networks for creating the initial models that would form the architecture of the proposed solution.

Cloud computing has been established in recent years as an important area of research. This reality has been consolidated because, currently, tasks such as obtainment, manipulation, sharing and exploitation of large amounts of data are common and require many computational resources, so cloud computing can contribute because it can provide this resources indefinitely, including processing, memory, storage and others, all them for immediate use. Cloud computing has many challenges, among them, the specification of work platforms in a cloud environment. In this context, the use of models can help insofar as they may contain several types of information that can be handled with the intention of automating the process of creation of an environment. A template can contain information about the user, hardware and software that can be used by computer systems to automatically build the necessary infrastructure for the operation of a virtual machine on a cloud computing environment. In addition, models can be easily exported to other formats used by cloud services providers. The use of concepts of Service Level Agreements - SLA to control the utilization of computational resources of a provider is common in computing environments and can be used to ensure the quality of services that being offered. The objective of this work is to combine these paradigms, cloud, models and SLA, in order to use the resources of a computational cloud to provide automatically a platform as a service - PaaS. This project will be developed in a web environment, using a model-driven approach to create the platforms and the management of resources and quality will be made by SLA.

In an IT Service Management setting, current approaches to support Continual Service Improvement (CSI) suffer from many deficiencies, including lack of or weak visibility of business impacts, no modeling of uncertainty, difficulty in combining heterogeneous metrics, among others. The result is that CSI activities typically rely on unstructured procedures based on weak data. We offer a model to capture service quality and the business value delivered by a service; the model aims to solve or diminish these deficiencies. A case study was performed in a large bank with very promising results: although model completeness and complexity need to improve, a face validity exercise was performed during the case study and found that the model is sufficiently useful, trustworthy, reliable and accurate.

Under current approaches, service performance is generally evaluated using technical metrics, for the following two basic reasons: 1) They are easily understood by technical people and 2) They are typically easy to measure directly. The actual impact on the business of low value and quality are not apparent. Service metrics are objectively measured, however, subjective criteria - those that depend on the subject (person) being polled - are not easily brought into the picture. Uncertainty is not typically considered in the measurement process. Service metrics are gathered strictly on an operational level without any effective information available at a strategic level. Within this scope of work, we propose a model in order to quantify an IT service aggregated value and a hierarchical quality indicator within a time snapshot, which can be used to help managers in activities relating to continual service improvement. We carried out a case-study in a real environment of a Brazilian Bank whereby the results indicate that our non-intrusive model is useful and can be adopted by corporations in addition to their actual continual service improvement rules.

Abstract Computational grids are characterized by the collaborative work among environment devices. Further, grid applications have been built based on reputation solutions. However, those applications can suffer due to attacks from malicious nodes. These nodes may not only corrupt the result from processed jobs, but also intentionally acquire a good reputation so as to obtain privileges to damage other nodes. In order to detect and isolate these malicious nodes (known as intelligent cheating nodes) from a P2P grid computing, this work proposes a system‐level diagnosis model, using a strategy based on voting and honeypots. The model is evaluated by means of scenarios that take into account different percentages of malicious and cheating nodes. Achieved results show the robustness and efficiency of our diagnosis model, once all cheating nodes can be detected, with an accuracy of 99.4% of jobs being correctly processed. Furthermore, a graphical user interface is implemented for visualizing the simulations. Copyright © 2009 John Wiley & Sons, Ltd.

BACKGROUNDThis special issue focuses on 'advanced scheduling strategies and Grid programming environments', and is based on selected papers from the Fifth International Workshop on Middleware for Grid Computing (MGC 2007).The authors were invited to provide extended versions of their original papers taking into account comments and suggestions raised during the peer review process and comments from the audience during the workshop.Relevant contributions have been provided by Jones, Caminero et al., Rosinha et al., Vidal et al., and Talukder et al., focusing on multisite scheduling techniques, an autonomic network-aware scheduling architecture, a work stealing programming environment, a semantic approach to improve resource scheduling, and a workflow execution planning approach using multi-objective differential evolution.Grid-enabled environments have been motivated by the work in service-oriented computing, and designed as a set of services that are provided by individuals or institutions.In this scenario, a level of automation is necessary to achieve scalability, requiring intelligent infrastructure management capabilities to be utilized alongside the existing approaches.Despite the emerging complexity and capability made available in Grid environments, it is worthy to mention that developing cross-grid applications that span different virtual organizations (VOs) has remained difficult.For instance, there is no co-scheduling of resources across more than one VO.Cloud computing concepts have recently emerged to address the limitations of Grid environments; a key focus is the use of virtualization technologies (at both platform and middleware levels) and replication through machine/service imaging.However the limitations initially found with Grid computing remain.It is useful to note that a computing Cloud can be designed on top of an existing Grid, as it also comprises heterogeneous components that have been integrated using a top-down approach.Similarly, Cloud computing systems are mostly created with the objective of providing a determined set of capabilities to a user; thus, the interface is an important part of the design.Scheduling challenges for enabling the deployment and execution of Grid-based parallel applications have been addressed by the selected papers in this special issue, with the development of models and techniques evaluated accordingly by prototyping and simulations.

In ad hoc mobile networks, communication management is done by the devices themselves (nodes) in an autonomous way. Forwarding of packets is an essential service done by the nodes to the maintenance of the network's connectivity. However, forwarding a packet demands energy, a scarce resource in a mobile device. This work presents an autonomous strategy of incentivating the cooperation in ad hoc networks based in credits and reputation. As incentivate, the nodes that forwards packets, receive credits that are used to send own packets with a better Quality of Service (QoS). The strategy uses only local information and there is no need of a central station to manage incentive application.

Malicious manipulation of jobs results endangers the efficiency and performance of grid computing applications. The presence of nodes interested in depreciating jobs results may be detected and minimized with the usage of fault tolerance techniques. In order to detect this kind of nodes, this paper presents a distributed and hierarchical diagnosis model based on comparison and reputation, which can be applied to both public and private grids. This strategy defines the status of a node according to its level of confidence, measured through its behavior. The proposed model was submitted to simulations to evaluate its effectiveness under different quota of malicious nodes. The results reveals that 8 test rounds can detect practically all malicious nodes and even with less rounds, the correctness remains high without a significant overhead increase

This article presents a voice quality measurement tool based on the ITU-T E-model. Firstly, the ITU-T and ETSI specifications of E-model are briefly reviewed and some errors found in these documents are pointed. After, a measurement tool based on the corrections is described. VoIP calls through the Brazilian National Education and research network (RNP) backbone were used to verify the tool operation.