Flavia Delicato | UFF - Universidade Federal Fluminense (original) (raw)

Papers by Flavia Delicato

SN Computer Science, Dec 1, 2023

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizi... more The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms are unsuitable for data streams, as they require existing datasets to build prediction models. As an embodiment of ML, which embraces the fact that learning environments change over time, in Online Machine Learning (OML) the model is trained with each new observation in production time. Most OML algorithms are developed after the offline version and present different behaviors, considering bias and variance. Finding a suitable estimator to solve a ML problem is still a challenge. In this context, ensemble learning emerges as a promising approach for balancing the bias-variance tradeoff and improving prediction accuracy by aggregating outputs from multiple ML models. This paper introduces a novel ensemble method tailored for edge computing envi...

Multimedia sensors have recently become a major data source, giving rise to the Internet of Multi... more Multimedia sensors have recently become a major data source, giving rise to the Internet of Multimedia Things. Since multimedia applications are usually latency-sensitive, data processing in the cloud is not always effective. A strategy to minimize delay is to process the streams closer to the data sources, exploiting the resources at the edge of the network. We propose V-PRISM, an architecture to virtualize multimedia sensors with components deployed and executed at the edge tier. V-PRISM can reduce the resource consumption of IoT devices, the network traffic, and the end-to-end delay while increasing the ROI (Return On Investment) for infrastructure providers.

Blockchain has catched significant attention and has been used in several contexts, like Internet... more Blockchain has catched significant attention and has been used in several contexts, like Internet of Things (IoT). Anyway, blockchain has a scalability problem that limits its capability to sustain services with several operations. Edge computing is an emerging computing paradigm introduced to distribute services at the network edge, leveraged by the need of meeting low response requirements, mainly for IoT applications. However, edge systems currently face challenges in their decentralized administration and security. The combination of edge computing and blockchain in IoT gives the possibility to solve these challenges. In this paper, we inquire the synergistic combination of blockchain with edge computing in the context of IoT systems. In detail, we first discuss about Edge Computing and its challenges. Then, we introduce the blockchain in Edge-based IoT and analyze several proposal present in the literature. In the end, we draw the open research direction.

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier... more The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

There is a wide range of applications for wireless sensor networks (WSNs) with different needs. T... more There is a wide range of applications for wireless sensor networks (WSNs) with different needs. The WSN infrastructure and protocols change according to the application needs. To achieve the best performance of the WSN, its operation should be adapted to the application needs. We propose a middleware for WSNs that provides a layer between applications and the network. The middleware offers a standard mechanism for representing user queries, sensor tasks and data. It also provides an automatic choice of the best network configuration and data dissemination strategy. Users are able to access the WSN without worrying about the underlying infrastructure and software. From the WSN perspective, the system provides the best match between communication protocols and application requirements. Resumo. Há uma ampla gama de aplicações para redes de sensores sem fio (RSSF)s, com diferentes necessidades. A infraestrutura e o protocolo de disseminação de dados da rede variam de acordo com a aplicação. Para o melhor desempenho quanto ao consumo de energia e à qualidade do serviço fornecido pela rede, seu funcionamento deve ser adaptado às necessidades da aplicação. Este trabalho propõe um middleware que oferece uma camada entre aplicações e a rede de sensores e oferece um mecanismo padrão para representar consultas, tarefas e dados. Além disso, fornece a escolha automatizada da configuração da rede e da estratégia de disseminação de dados usada, permitindo ao usuário acessar a rede sem tomar conhecimento de infraestrutura e software subjacentes. Do ponto de vista da rede, o sistema visa obter a melhor combinação entre protocolos de comunicação e requisitos da aplicação.

arXiv (Cornell University), Jul 3, 2020

A common feature of the Internet of Things (IoT) is the high heterogeneity, regarding network pro... more A common feature of the Internet of Things (IoT) is the high heterogeneity, regarding network protocols, data formats, hardware and software platforms. Aiming to deal with such a degree of heterogeneity, several frameworks have applied the Model-Driven Development (MDD) to build IoT applications. On the software architecture viewpoint, the literature has shown that the Service-Oriented Architecture (SOA) is a promising style to address the interoperability of entities composing these solutions. Some features of IoT make it challenging to analyze the impact of design decisions on the SOAbased IoT applications behavior. Thus, it is a key requirement to simulate the model to verify whether the system performs as expected before its implementation. Although the literature has identified that the SOA style is suitable for addressing the interoperability, existing modelling languages do not consider SOA elements as first-class citizens when designing IoT applications. Furthermore, although existing MDD frameworks provide modeling languages comprising well-defined syntax, they lack execution semantics, thus, are not suitable for model execution and analysis. This work aims at addressing these issues by introducing IoTDraw. The framework provides a fully OMG-compliant executable modeling language for SOA-based IoT systems; thus, its specifications can be implemented by any tool implementing OMG standards. Keywords-internet of things, application, model-driven development, service-oriented architecture given rise to the recent paradigm known as Sensor-Cloud or Cloud of Things, which aims to make the most of both technologies. IoT-generated data that requires a lot of processing or long-term storage can be sent to the cloud, which in turn extends its service portfolio to encompass sensing and actuation capabilities. Cloud platforms follow a service-based resource provisioning model. Cloud platforms providing infrastructure for IoT systems naturally adopt the services approach (e.g., [17], [18]). In a recent survey that investigated middleware technologies for Cloud of Things [19], the authors have identified that SOA is the main architectural style in CoT-based Middleware, due to is capability of promoting interoperability and reusability in IoT systems. 1.1 Issues in designing MDD & SOA-based IoT Systems Any DSL must provide a coherent set of concepts related to the domain of interest [8]. That is, the DSL must be expressive, aiming to allow the precise identification of multiple aspects regarding the application domain. Otherwise, if the DSL lacks essential elements of the underlying domain, the models may be at least ambiguous, or the modelers may not be able to represent all components of the applications. Although the literature has identified the suitability of the SOA style for addressing the interoperability in the IoT domain, to the best of our knowledge, there is no DSL tailored for the modeling of SOA-based IoT applications. SOA is a proven architectural pattern, with a well-known set of concepts. Existing DSLs for IoT (e.g., UML4IoT [9] and ThingML [20]) do not fully contemplate such concepts, which impacts on the architectural design of IoT applications following the SOA approach. This is a critical problem since even crucial but straightforward design questions related to SOA style cannot be answered by the model, such as, "What are the services, and by which interface they are exposed?", "Who are the provider/consumer participants that are part of the application?", "How the participants interact with each other?", "What is the choreography of the services?", or "What is the protocol used by the interfaces." A DSL that does not provide concepts to allow answering these design questions possibly hampers the design of SOA-based IoT applications. Another important design issue refers to the simulation and analysis of IoT systems. In the domain of Software and Systems engineering, a rule of thumb states that the best system representation is the simplest model that answers a set of design questions [21]. This means that, besides identifying precisely the multiple components, another reason for modeling a system is to support the architectural decision-making process [21], [22]. Otherwise, the system model would serve only as documentation and would not support a more in-depth analysis, which may not justify the modeling effort. A modeling approach helps to answer design questions and supports the architectural decision-making process by its ability to predict, at design-time, the properties of an artifact based on its design. This prediction is possible when the DSL allows its compliant models to be executed and simulated. When specifying IoT systems, some features of the IoT domain makes challenging to analyze the impact of design decisions on the system behavior. Examples of such features are the resource constraints of devices and multiple deployment scenarios. An IoT application can be composed of tens, hundreds, or even thousands of devices, many of them being powered by limited batteries and with constrained resources (in terms of processing speed and memory size), which may impact on the availability or performance of the IoT application [1]. On the other hand, the multiple components of an application may be deployed on different platforms (varying from resource-constrained devices to Cloud computing platforms), which result in a considerable number of eligible deployment scenarios. Since each deployment scenario has a different impact on application requirements and system performance, it may become humanly infeasible to identify the best deployment alternative. These issues hamper making proper design decisions, being necessary to execute and simulate the model to verify whether the specified application performs as expected before its implementation. Otherwise, if problems caused by wrong design decisions are found only after the system implementation or deployment, it may result in financial loss or, worse, risks to human lives, for instance in failures in e-Health applications. Considering the system verification and the problems that may occur whether it is not realized at design time, a typical analysis that must be performed refers to non-functional or Quality of Service (QoS) properties. The study of Udoh and Kotonya [23] presents a comprehensive review of existing IoT application frameworks and toolkits. An interesting finding of such study is that existent frameworks ignore the support of verification of Quality of Service (QoS) properties at design time. Another study ([24]) has shown that only around 2% of the companies that are developing IoT solutions are able to test the specified systems before undertaking the system deployment. 1.2 Contributions and Roadmap Aiming to tackle the abovementioned issues, the core contribution of our work is proposing an MDD framework, which we called as IoTDraw. The framework provides a fully OMG-compliant executable modeling language (DSL), SoaML4IoT [25], for SOA-based IoT systems; thus, its specifications can be implemented by any tool implementing OMG standards. The DSL was built on the Service-oriented architecture Modeling Language (SoaML) [26], the Object Management Group (OMG) standard for designing SOA solutions. SoaML consists of an extension of UML [27] (i.e., a UML profile), which was conceived through the collaboration of experts from academia and industry. As stated by the SoaML specification, the modeling language focuses on the basic service modeling concepts, and the intention is to use it as a foundation for further domain extensions. Thus, based on our experience and proven domain models for IoT (e.g., IoT-ARM [11], WSO2 [28]), we extended this language with concepts of the IoT domain, allowing the identification of the multiple entities composing SOA-based IoT applications. 2 Foundations In this Section, we present an overview of the basics of MDD engineering, with focus on the elements on which our approach is built on, that is, SoaML and fUML. 2.1 SoaML SoaML [26] is the standard graphical modeling language proposed by the OMG for designing systems that follow the services approach. It consists of a metamodel and a UML profile for the specification of services within SOA style. An extensive example of modeling with SoaML is given by Elvesaeter and colleagues [37]. In the following, we present key concepts of the SoaML metamodel. In parenthesis, we show the UML element that SoaML profile extends. Participants (Class) are entities that provide or use services. Capability (Class), refer to functions required by stakeholders and provided by a participant through a service. A Service (Port) denotes value delivered to another through a well-defined interface. Provider and Consumer (Interface) are roles of participants that provide or consumes services, respectively. The description of how the participants interact to provide or use a service can be specified as Service Contracts (Collaboration). It specifies the roles each participant plays in the serviceprovider or consumerand the choreography of the service, that is, what information is sent between the provider and consumer and in what order. When specifying the choreography of a given service contract, any UML behavior specification can be used, such as interaction and activity diagrams. Finally, the Services Architecture (Collaboration) describes how participants work together for a purpose by providing and using services expressed in the service contracts. There are a variety of approaches for identifying services that are supported by SoaML. However, regardless of how the services are identified, they are formalized by service descriptions. In turn, the services architecture aims at structuring such services with the interacting participants and their ports, which realize the roles of the system. 2.1.1 SoaML and Microservices Microservices is an architectural style that has...

SpringerBriefs in computer science, 2017

In this book, we investigate the core activities encompassed in a holistic resource management pr... more In this book, we investigate the core activities encompassed in a holistic resource management process for Internet of Things (IoT ), focusing on the different functionalities and architectural approaches involved on a basic workflow for managing the lifecycle of resources in an IoT system. We identified the following activities as the main components of a typical workflow for a resource management system (RMS) in IoT: resource modelling, resource discovery , resource estimation , resource allocation and resource monitoring . In this chapter, we address the first activity, namely the resource modelling . The resource model is a vital part of any RMS that aims at properly representing the elements from the different layers of an IoT ecosystem . In this chapter, we analyse existing proposals for resource modelling, focusing on solutions for both resource representation and application representation. Proposals for resource representation can be grouped into three main categories: attribute-based , semantic-based and virtualization-based . After presenting some examples for each category, we briefly discuss them under three aspects: the degrees of abstraction, granularity and formalism with which the elements are represented in the models. It is important to mention that, although several works implicitly assume some type of representation for the system resources and also for the application, in our discussion we included only the proposals that explicitly define their models, with sufficient details to shed light in the relevant aspects of such models.

Future Internet, Nov 30, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The Internet of Things (IoT) represents the next significant step in the evolution of the Interne... more The Internet of Things (IoT) represents the next significant step in the evolution of the Internet. It will allow “things” to be connected anytime, anywhere, with anything and anyone, providing a myriad of novel applications and augmented services to citizens, governments, and enterprises. We believe that for the IoT to reach its full potential, it will be necessary to advance the investigation of techniques and technologies to build systems in such a brand-new scenario. In the IoT, the collaborating entities encompass both physical and virtual resources, interactions occur both in an active and programmed way as well as by chance, and therefore it is necessary to deal with expected but also emerging behaviors of the collaborating parties. In this paper, we first discuss the main features that make IoT a unique ecosystem, and as such, calls for new software development solutions. We claim that there is a need to rethinking the techniques and methodologies for developing IoT systems and applications. Novel models, architectural approaches and techniques should be proposed, or existing ones should be adapted to deal with the high heterogeneity, dynamism, serendipity and interdependencies that are typical of the IoT ecosystem. We then analyze and discuss potential key design solutions that deserve a deeper understanding in order to pave the way for the building of this new generation of systems. Our discussion is presented from a bottom-up perspective: from the modeling of devices that make up the mathrmIoT\mathrm{IoT}mathrmIoT to the representation of requirements that must be addressed when engineering IoT systems.

Minicursos do VII Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais

... milenefc@posgrad.nce.ufrj.br, carlo@nce.ufrj.br, paulo.pires@dimap.ufrn.br, flavia.delicato@ ... more ... milenefc@posgrad.nce.ufrj.br, carlo@nce.ufrj.br, paulo.pires@dimap.ufrn.br, flavia.delicato@ dimap.ufrn.br Abstract Modern Information systems are highly concerned with security and it is not rare the occurrence of security failures and vulnerabilities. ...

2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2016

The Internet of Things (IoT) is a new paradigm consisting of heterogeneous entities that communic... more The Internet of Things (IoT) is a new paradigm consisting of heterogeneous entities that communicate with each other by sending and receiving messages in heterogeneous formats through heterogeneous protocols to achieve a common goal. When designing IoT applications, there are two main challenges: the complexity to represent such heterogeneous entities, message formats, and protocols in an unambiguous manner, and the lack of methodologies to verify QoS (Quality of Service) properties. This paper introduces a design and analysis process supported by a framework to assist IoT application engineers to precisely model IoT applications and verify their properties. The framework is composed of the SysML4IoT, a SysML profile based on the IoT-A Reference Model, and the SysML2NuSMV, a model-to-text translator that converts the model and QoS properties specified on it to be executed by NuSMV, a mature model checker that allows entering a system model comprising a number of communicating Finite State Machines (FSM) and automatically checks its properties specified as Computational Tree Logic (CTL) or Linear Temporal Logic (LTL) formulas. Our approach is evaluated through a proof of concept implementation that analyzes the QoS property of reliability in a Building Energy Conservation (BEC) IoT application.

2018 IEEE International Conference on Cloud Engineering (IC2E), 2018

In the context of the Internet of Things (IoT), a new service model has emerged, namely, the Sens... more In the context of the Internet of Things (IoT), a new service model has emerged, namely, the Sensing as a Service (S²aaS). In such model, sensing data is available for external users as resources, and can be provided on demand, as services. Typically, the devices providing sensing resources are powered by batteries, thus the lifetime of devices becomes a concern to IoT service providers. A common strategy to reduce the number of tasks to be performed by the devices, thus saving energy, is caching data, reusing a prior response message to satisfy a current request. However, on the side of IoT service consumers, the freshness of the data is also a critical concern. That is, the time elapsed since the data is collected until it is delivered to the requesting user should be small. In high workload scenarios, addressing both lifetime and data freshness requirements may be challenging, mainly because commonly used application protocols, such as the Constrained Application Protocol (CoAP),...

SpringerBriefs in Computer Science, 2017

This book investigates the pressing issue of resource management for Internet of Things (IoT). Th... more This book investigates the pressing issue of resource management for Internet of Things (IoT). The unique IoT ecosystem poses new challenges and calls for unique and bespoke solutions to deal with these challenges. Using a holistic approach, the authors present a thorough study into the allocation of the resources available within IoT systems to accommodate application requirements. This is done by investigating different functionalities and architectural approaches involved in a basic workflow for managing the lifecycle of resources in an IoT system. Resource Management for the Internet of Things will be of interest to researchers and students as well as professional developers interested in studying the IoT paradigm from data acquisition to the delivery of value-added services for the end user.

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016

This paper introduces SAMSON (Self-Adaptive Middleware for wireless SensOr Networks), an instance... more This paper introduces SAMSON (Self-Adaptive Middleware for wireless SensOr Networks), an instance of a Reference Architecture (RA) that was designed to manage wireless sensor networks (WSN) in an autonomic way by enabling runtime adaptation of nodes' behavior according to context changes, application requirements, and predefined adaptation policies. In this paper, we propose a process, supported by model-driven transformations to generate SAMSON from the RA specification. The model-driven transformations are used to map each element of the RA specification to software components and to generate the source code to be deployed in a target WSN platform. We evaluate our proposal by presenting four application scenarios where SAMSON was employed to optimize the network lifetime and to react to runtime events according to adaptation policies previously defined by software architects and network administrators. Evaluation results shown that SAMSON was able to dynamically adapt the behavior of sensor nodes, to efficiently use the hardware resources, and to increase the WSN lifetime.

Software & Systems Modeling, 2015

As wireless sensor and actuator networks (WSANs) can be used in many different domains, WSAN appl... more As wireless sensor and actuator networks (WSANs) can be used in many different domains, WSAN applications have to be built from two viewpoints: domain and network. These different viewpoints create a gap between the abstractions handled by the application developers, namely the domain and network experts. Furthermore, there is a coupling between the application logic and the underlying sensor platform, which results in platform-dependent projects and source codes difficult to maintain, modify, and reuse. Consequently, the process of developing an application becomes cumbersome. In this paper, we propose a modeldriven architecture (MDA) approach for WSAN application development. Our approach aims to facilitate the task of the developers by: (1) enabling application design through high abstraction level models; (2) providing a specific methodology for developing WSAN applications; and (3) offering an MDA infrastructure composed of PIM, PSM, and transformation programs to support this process. Our approach Communicated by Prof. Franck Barbier.

37th Annual IEEE Conference on Local Computer Networks, 2012

We would like to extend a warm welcome to the 8th IEEE International Workshop on Performance and ... more We would like to extend a warm welcome to the 8th IEEE International Workshop on Performance and Management of Wireless and Mobile Networks (P2MNeT), held in conjunction with the 37th IEEE Conference on Local Computer Networks (LCN).

IEEE internet of things magazine, Mar 1, 2021

This position paper summarizes the main visions, opinions, and arguments of four experienced and ... more This position paper summarizes the main visions, opinions, and arguments of four experienced and well known researchers in the area of Internet of Things (IoT) and its relation to Data Science and Machine Learning (ML) as IoT permeates the globe and becomes “very large”. These visions were raised in an enthusiastic discussion panel held during the Third International Workshop on Very Large Internet of Things Systems (VLIoT 2019), in conjunction with VLDB 2019, in Los Angeles, USA. Each panelist delivered a vision statement before the floor was opened for questions and comments from the audience. Instead of reproducing ipsis literis each of the speeches, questions and replies, we decided to structure a two-part paper summarizing in-depth the panel opinions and discussions. In this first installment, we present the panelists' opening statements and views on issues related to IoT infrastructure and how it can support the growing demands for integrated intelligence, including communication, coordination and distribution challenges and how such challenges can be faced in the new generation of IoT systems.