BibTex:

    @Article{OJIOT_2020v6i1n01e_VLIOT2020,
        title     = {Due to COVID-19 the World's Activities Stopped, but not Research: Workshop on Very Large Internet of Things (VLIoT 2020)},
        author    = {Sven Groppe and
                     Mu-Chun Su},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {1--5},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219331432369272},
        urn       = {urn:nbn:de:101:1-2020080219331432369272},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Very Large Internet of Things (VLIoT) workshop aims at discussing the solutions of problems arising especially for large-scale configurations. After continuously monitoring the global COVID-19 pandemic this year, the workshop changes the format the first time to an online event in order to overcome problems like travel restrictions. Besides missing face-to-face meetings the online format also has chances like an increased number of participants, less travel burdens and saving budget. Hence we received many high-quality submissions, from which we accepted 9 to be introduced in this editorial.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n02_BoMa,
        title     = {Assuring Privacy-Preservation in Mining Medical Text Materials for COVID-19 Cases - A Natural Language Processing Perspective},
        author    = {Bo Ma and
                     Jinsong Wu and
                     Shuang Song and
                     William Liu},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {6--13},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219332153513307},
        urn       = {urn:nbn:de:101:1-2020080219332153513307},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Currently, there is a very large volume of Covid-19 related medical data that have been stored in cloud based systems and made available for studing the disease dynamics. without any privacy-preservation. In order to reduce possible privacy leakage and also accommodate massive medical reports with high efficiencies, we proposed a privacypreserving word embody-based text classification method for mining COVID-19 medical documents. It uses the recurrent neural network deep learning algorithm according to the identified internal hiding centralization pattern. In addition, a new model-fusion method is proposed for the continuous improvement of the system performance.The extensive numerical studies have demonstrated that the classifier of the proposed system has superior performance via integrating with the keywords extraction approach. Moreover, the advanced new model does not only accurately capture the keyword patterns but also effectively capture the analogical hierarchy structure of the pathology related datasets with lower computational complexity.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n03_Huu,
        title     = {MASCARA (ModulAr Semantic CAching fRAmework) towards FPGA Acceleration for IoT Security Monitoring},
        author    = {Van Long Nguyen Huu and
                     Julien Lallet and
                     Emmanuel Casseau and
                     Laurent d'Orazio},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {14--23},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219332912798426},
        urn       = {urn:nbn:de:101:1-2020080219332912798426},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {With the explosive growth of the Internet Of Things (IOTs), emergency security monitoring becomes essential to efficiently manage an enormous amount of information from heterogeneous systems. In concern of increasing the performance for the sequence of online queries on long-term historical data, query caching with semantic organization, called Semantic Query Caching or Semantic Caching (SC), can play a vital role. SC is implemented mostly in software perspective without providing a generic description of modules or cache services in the given context. Hardware acceleration with FPGA opens new research directions to achieve better performance for SC. Hence, our work aims to propose a flexible, adaptable, and tunable ModulAr Semantic CAching fRAmework (MASCARA) towards FPGA acceleration for fast and accurate massive logs processing applications.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n04_Fuerst,
        title     = {Towards Knowledge Infusion for Robust and Transferable Machine Learning in IoT},
        author    = {Jonathan F{\"u}rst and
                     Mauricio Fadel Argerich and
                     Bin Cheng and
                     Ern{\"o} Kovacs},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {24--34},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219333632380804},
        urn       = {urn:nbn:de:101:1-2020080219333632380804},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Machine learning (ML) applications in Internet of Things (IoT) scenarios face the issue that supervision signals, such as labeled data, are scarce and expensive to obtain. For example, it often requires a human to manually label events in a data stream by observing the same events in the real world. In addition, the performance of trained models usually depends on a specific context: (1) location, (2) time and (3) data quality. This context is not static in reality, making it hard to achieve robust and transferable machine learning for IoT systems in practice. In this paper, we address these challenges with an envisioned method that we name Knowledge Infusion. First, we present two past case studies in which we combined external knowledge with traditional data-driven machine learning in IoT scenarios to ease the supervision effort: (1) a weak-supervision approach for the IoT domain to auto-generate labels based on external knowledge (e.g., domain knowledge) encoded in simple labeling functions. Our evaluation for transport mode classification achieves a micro-F1 score of 80.2\%, with only seven labeling functions, on par with a fully supervised model that relies on hand-labeled data. (2) We introduce guiding functions to Reinforcement Learning (RL) to guide the agents' decisions and experience. In initial experiments, our guided reinforcement learning achieves more than three times higher reward in the beginning of its training than an agent with no external knowledge. We use the lessons learned from these experiences to develop our vision of knowledge infusion. In knowledge infusion, we aim to automate the inclusion of knowledge from existing knowledge bases and domain experts to combine it with traditional data-driven machine learning techniques during setup/training phase, but also during the execution phase.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n05_Ruta,
        title     = {Information-Centric Semantic Web of Things},
        author    = {Michele Ruta and
                     Floriano Scioscia},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {35--52},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219334321163627},
        urn       = {urn:nbn:de:101:1-2020080219334321163627},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In the Semantic Web of Things (SWoT) paradigm, a plethora of micro-devices permeates an environment. Storage and information processing are decentralized: each component conveys and even processes a (very) small amount of annotated metadata. In this perspective, the node-centric Internet networking model is inadequate. This paper presents a framework for resource discovery in semantic-enhanced pervasive environments leveraging an information-centric networking approach. Information gathered through different Internet of Things (IoT) technologies can be exploited by both ubiquitous and Web-based semantic-aware applications through a uniform set of operations. Experimental results and a case study support sustainability and effectiveness of the proposal.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n06_Heuer,
        title     = {The Internet of Things as a Privacy-Aware Database Machine},
        author    = {Andreas Heuer and
                     Hannes Grunert},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {53--65},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219335124583702},
        urn       = {urn:nbn:de:101:1-2020080219335124583702},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory.}
    }

BibTex:

    @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.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n08_Alves,
        title     = {Can You Hear Me? A Metric for Link Asymmetry},
        author    = {Renan C. A. Alves and
                     Cintia B. Margi},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {82--88},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219340603524937},
        urn       = {urn:nbn:de:101:1-2020080219340603524937},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Internet of Things is a networking paradigm aiming to provide computing pervasiveness to our everyday lives. A key component to the Internet of Things is low power networks that gather information from the environment. Low power networks are prone to asymmetric and unidirectional links. Measuring the level of asymmetry and understanding its sources are key steps to successfully deploying sensor networks and the Internet of Things. Our first contribution is a new metric to assess link asymmetry, one which takes into account the instantaneous delivery success probability. Next, we study the influence of four factors on link asymmetry in light of our asymmetry metric, namely, relative distance, output power, relative position, and hardware heterogeneity. With our unique method, we show that all four factors impact link asymmetry.}
    }

BibTex:

    @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.}
    }

BibTex:

    @Article{OJIOT_2020v6i1n10_Ramachandran,
        title     = {ParkingJSON: An Open Standard Format for Parking Data in Smart Cities},
        author    = {Gowri Sankar Ramachandran and
                     Jeremy Stout and
                     Joyce J. Edson and
                     Bhaskar Krishnamachari},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {105--118},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219342154738409},
        urn       = {urn:nbn:de:101:1-2020080219342154738409},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Data marketplaces and data management platforms offer a viable solution to build large city-scale Internet of Things (IoT) applications. Contemporary data marketplaces and data management platforms for smart cities such as Intelligent IoT Integrator (I3), Cisco Kinetic, Terbine, and Streamr present a middleware platform to help the data owners to provide their data to the application developers. However, such platforms suffer from adoption issues because of the interoperability concerns that stem from heterogeneous data formats. On the one hand, the IoT devices and the software used by the device owners follow either a custom data standard or a proprietary industrial standard. On the other hand, the application developers consuming data from multiple device owners expect the data to follow one common standard to process the data without developing custom software for each data feed. Therefore, a common data standard is desired to enable interoperable data exchange through data marketplace and data management platforms while promoting adoption. We present our experiences from developing a city-scale real-time parking application for a smart city. We also introduce ParkingJSON, a new open standard format for parking data in smart cities, which could help the parking data providers to cover all types of parking infrastructures through a single JSON schema. To the best of our knowledge, this is the first parking data standard proposed that a) covers a wide range of parking spaces and structures, b) integrates spatial information, and c) provides support for data integrity and authenticity.}
    }