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    @Article{OJIOT_2019v5i1n03_Santos,
        title     = {Data-Centric Resource Management in Edge-Cloud Systems for the IoT},
        author    = {Igor Le\~{a}o dos Santos and
                     Fl\'{a}via C. Delicato and
                     Paulo F. Pires and
                     Marcelo Pitanga Alves and
                     Ana Oliveira and
                     Tiago Salviano Calmon},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2019},
        volume    = {5},
        number    = {1},
        pages     = {29--46},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2019092919334248197873},
        urn       = {urn:nbn:de:101:1-2019092919334248197873},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {A major challenge in emergent scenarios such as the Cloud-assisted Internet of Things is efficiently managing the resources involved in the system while meeting requirements of applications. From the acquisition of physical data to its transformation into valuable services or information, several steps must be performed, involving the various players in such a complex ecosystem. Support for decentralized data processing on IoT devices and other devices near the edge of the network, in combination with the benefits of cloud technologies has been identified as a promising approach to reduce communication overhead, thus reducing delay for time sensitive IoT applications. The interplay of IoT, edge and cloud to achieve the final goal of producing useful information and value-added services to end user gives rise to a management problem that needs to be wisely tackled. The goal of this work is to propose a novel resource management framework for edge-cloud systems that supports heterogeneity of both devices and application requirements. The framework aims to promote the efficient usage of the system resources while leveraging the Edge Computing features, to meet the low latency requirements of emergent IoT applications. The proposed framework encompasses (i) a lightweight and data-centric virtualization model for edge devices, (ii) a set of components responsible for the resource management and the provisioning of services from the virtualized edge-cloud resources.}
    }
    @Article{OJIOT_2020v6i1n09_Neto,
        title     = {An Architecture for Distributed Video Stream Processing in IoMT Systems},
        author    = {Aluizio Rocha Neto and
                     Thiago P. Silva and
                     Thais V. Batista and
                     Flavia C. Delicato and
                     Paulo F. Pires and
                     Frederico Lopes},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {89--104},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219341417043601},
        urn       = {urn:nbn:de:101:1-2020080219341417043601},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In Internet of Multimedia Things (IoMT) systems, Internet cameras installed in buildings and streets are major sources of sensing data. From these large-scale video streams, it is possible to infer various information providing the current status of the monitored environments. Some events of interest that have occurred in these observed locations produce insights that might demand near real-time responses from the system. In this context, the event processing depends on data freshness, and computation time, otherwise, the processing results and activities become less valuable or even worthless. An encouraging plan to support the computational demand for latency-sensitive applications of largely geo-distributed systems is applying Edge Computing resources to perform the video stream processing stages. However, some of these stages use deep learning methods for the detection and identification of objects of interest, which are voracious consumers of computational resources. To address these issues, this work proposes an architecture to distribute the video stream processing stages in multiple tasks running on different edge nodes, reducing network overhead and consequent delays. The Multilevel Information Fusion Edge Architecture (MELINDA) encapsulates the data analytics algorithms provided by machine learning methods in different types of processing tasks organized by multiple data-abstraction levels. This distribution strategy, combined with the new category of Edge AI hardware specifically designed to develop smart systems, is a promising approach to address the resource limitations of edge devices.}
    }
    @Article{OJIOT_2021v7i1n10_Xavier,
        title     = {Data-Centric Edge Federation: A Multi-Edge Architecture for Data Stream Processing of IoT Applications},
        author    = {Tiago C. S. Xavier and
                     Paulo F. Pires and
                     Flavia C. Delicato},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2021},
        volume    = {7},
        number    = {1},
        pages     = {104--115},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2021082919334774324679},
        urn       = {urn:nbn:de:101:1-2021082919334774324679},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Emerging Internet of Things (IoT) applications demand data stream processing with low latency and high processing power. Although the cloud naturally provides huge processing capacity, high latency to move data to the datacenter is prohibitive. Edge computing is a recent paradigm where part of computing and storage resources are pushed from the cloud to the edge of the network. In edge computing, edge providers manage their resources near to IoT devices to meet low latency application requirements and reduce the network core bandwidth. To reach the maximum potential of edge computing, a big challenge is to promote the cooperation between edge providers. Currently, edge computing architectures are severely limited for providing cooperation mechanisms between distinct edge providers. In this paper, we propose a edge federation to leverage the cooperation between different edge providers. The edge federation uses interest information propagated in data streams that travel between edge providers to allow an stakeholder to react to inefficient resource allocation and service provision. The main objective of the federation is to create a consortium of edge providers to provide cooperation mechanisms and define and standardize the application interests. The proposed edge federation is (i) data-centric, since edge providers can share common interests and data and, thus, establish cooperation to increase the capacity to  provide services for applications; (ii) distributed, since no assumption is made concerning the geo-location of the edge providers and their logical connections; (iii) opportunistic, because an edge provider can react dynamically to the environment change ; (iv) scalable, since the edge provider has the ability to analyze a data flow passing by its infrastructure and make decisions to increase network performance locally, which impacts the global performance}
    }