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    @Article{OJIOT_2020v6i1n07_Zeuch,
        title     = {NebulaStream: Complex Analytics Beyond the Cloud},
        author    = {Steffen Zeuch and
                     Eleni Tzirita Zacharatou and
                     Shuhao Zhang and
                     Xenofon Chatziliadis and
                     Ankit Chaudhary and
                     Bonaventura Del Monte and
                     Dimitrios Giouroukis and
                     Philipp M. Grulich and
                     Ariane Ziehn and
                     Volker Mark},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {66--81},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219335991237696},
        urn       = {urn:nbn:de:101:1-2020080219335991237696},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The arising Internet of Things (IoT) will require significant changes to current stream processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present challenges and opportunities for an IoT data management system to enable complex analytics beyond the cloud. As one of the most important upcoming IoT applications, we focus on the vision of a smart city. The goal of this paper is to bridge the gap between the requirements of upcoming IoT applications and the supported features of an IoT data management system. To this end, we outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system, NebulaStream. This paper lays the foundation for a new type of systems that leverages the IoT to enable large-scale applications over millions of IoT devices in highly dynamic and geo-distributed environments.}
    }
    @Article{OJIOT_2021v7i1n06_Giouroukis,
        title     = {Streaming Data through the IoT via Actor-Based Semantic Routing Trees},
        author    = {Dimitrios Giouroukis and
                     Johannes Jestram and
                     Steffen Zeuch and
                     Volker Markl},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2021},
        volume    = {7},
        number    = {1},
        pages     = {59--70},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2021082919332566828749},
        urn       = {urn:nbn:de:101:1-2021082919332566828749},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Internet of Things (IoT) enables the usage of resources at the edge of the network for various data management tasks that are traditionally executed in the cloud. However, the heterogeneity of devices and communication methods in a multi-tiered IoT environment (cloud/fog/edge) exacerbates the problem of deciding which nodes to use for processing and how to route data. In addition, both decisions cannot be made only statically for the entire lifetime of an application, as an IoT environment is highly dynamic and nodes in the same topology can be both stationary and mobile as well as reliable and volatile. As a result of these different characteristics, an IoT data management system that spans across all tiers of an IoT network cannot meet the same availability assumptions for all its nodes. To address the problem of choosing ad-hoc which nodes to use and include in a processing workload, we propose a networking component that uses a-priori as well as ad-hoc routing information from the network. Our approach, called Rime, relies on keeping track of nodes at the gateway level and exchanging routing information with other nodes in the network. By tracking nodes while the topology evolves in a geo-distributed manner, we enable efficient communication even in the case of frequent node failures. Our evaluation shows that Rime keeps in check communication costs and message transmissions by reducing unnecessary message exchange by up to 82:65\%.}
    }