BibTex:

    @Article{OJIOT_2017v3i1n02e_VLIoT2017,
        title     = {First Edition of the Very Large Internet of Things Workshop (VLIoT)},
        author    = {Sven Groppe and
                     Carlo Alberto Boano},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {12--17},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613397},
        urn       = {urn:nbn:de:101:1-2017080613397},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {This article is an editorial for the proceedings of the "Very Large Internet of Things (VLIoT 2017)" workshop in conjunction with the 43th International Conference on Very Large Data Bases (VLDB 2017), which takes place in Munich, Germany, from August 28th to September 1, 2017. The editorial of VLIoT@VLDB 2017 provides an overview over the aims and scope of the workshop, the review procedure, and the accepted papers. The proceedings of VLIoT@VLDB 2017 are published as special issue in the Open Journal of Internet of Things (OJIOT) (www.ronpub.com/ojiot), and the publisher of OJIOT is RonPub (www.ronpub.com).}
    }

BibTex:

    @Article{OJIOT_2017v3i1n03_Balasubramanian,
        title     = {Latency Optimization in Large-Scale Cloud-Sensor Systems},
        author    = {Adhithya Balasubramanian and
                     Sumi Helal and
                     Yi Xu},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {18--30},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613410},
        urn       = {urn:nbn:de:101:1-2017080613410},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {With the advent of the Internet of Things and smart city applications, massive cyber-physical interactions between the applications hosted in the cloud and a huge number of external physical sensors and devices is an inevitable situation. This raises two main challenges: cloud cost affordability as the smart city grows (referred to as economical cloud scalability) and the energy-efficient operation of sensor hardware. We have developed Cloud-Edge-Beneath (CEB), a multi-tier architecture for large-scale IoT deployments, embodying distributed optimizations, which address these two major challenges. In this article, we summarize our prior work on CEB to set context for presenting a third major challenge for cloud sensor-systems, which is latency. Prolonged latency can potentially arise in servicing requests from cloud applications, especially given our primary focus on optimizing energy and cloud scalability. Latency, however, is an important factor to optimize for real-time and cyber-physical applications with limited tolerance to delays. Also, improving the responsiveness of any IoT application is bound to improve the user experience and hence the acceptability and adoption of smart city solutions by the city citizens. In this article, we aim to give a formal definition and formulation for the latency optimization problem under CEB. We propose a Prioritized Application Fragment Caching Algorithm (PAFCA) to selectively cache application fragments from the cloud to lower layers of CEB, as a key measure to optimize latency. The algorithm itself is an extension of one of the existing optimization algorithms of CEB (AFCA-1). As will be shown, PAFCA takes into account the expectations of cloud applications on real-timeliness of responses. Through experiments, we measure and validate the effect of PAFCA on latency and cloud scalability. We also introduce and discuss the trade-off between latency and sensor energy in this given context.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n04_Grunert,
        title     = {Rewriting Complex Queries from Cloud to Fog under Capability Constraints to Protect the Users' Privacy},
        author    = {Hannes Grunert and
                     Andreas Heuer},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {31--45},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613421},
        urn       = {urn:nbn:de:101:1-2017080613421},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In this paper we show how existing query rewriting and query containment techniques can be used to achieve an efficient and privacy-aware processing of queries. To achieve this, the whole network structure, from data producing sensors up to cloud computers, is utilized to create a database machine consisting of billions of devices from the Internet of Things. Based on previous research in the field of database theory, especially query rewriting, we present a concept to split a query into fragment and remainder queries. Fragment queries can operate on resource limited devices to filter and preaggregate data. Remainder queries take these data and execute the last, complex part of the original queries on more powerful devices. As a result, less data is processed and forwarded in the network and the privacy principle of data minimization is accomplished.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n05_Ruta,
        title     = {Semantic Blockchain to Improve Scalability in the Internet of Things},
        author    = {Michele Ruta and
                     Floriano Scioscia and
                     Saverio Ieva and
                     Giovanna Capurso and
                     Eugenio Di Sciascio},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {46--61},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613488},
        urn       = {urn:nbn:de:101:1-2017080613488},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Generally scarce computational and memory resource availability is a well known problem for the IoT, whose intrinsic volatility makes complex applications unfeasible. Noteworthy efforts in overcoming unpredictability (particularly in case of large dimensions) are the ones integrating Knowledge Representation technologies to build the so-called Semantic Web of Things (SWoT). In spite of allowed advanced discovery features, transactions in the SWoT still suffer from not viable trust management strategies. Given its intrinsic characteristics, blockchain technology appears as interesting from this perspective: a semantic resource/service discovery layer built upon a basic blockchain infrastructure gains a consensus validation. This paper proposes a novel Service-Oriented Architecture (SOA) based on a semantic blockchain for registration, discovery, selection and payment. Such operations are implemented as smart contracts, allowing distributed execution and trust. Reported experiments early assess the sustainability of the proposal.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n06_Hegedus,
        title     = {Differentially Private Linear Models for Gossip Learning through Data Perturbation},
        author    = {Istv\'{a}n Hegedus and
                     M\'{a}rk Jelasity},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {62--74},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613445},
        urn       = {urn:nbn:de:101:1-2017080613445},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Privacy is a key concern in many distributed systems that are rich in personal data such as networks of smart meters or smartphones. Decentralizing the processing of personal data in such systems is a promising first step towards achieving privacy through avoiding the collection of data altogether. However, decentralization in itself is not enough: Additional guarantees such as differential privacy are highly desirable. Here, we focus on stochastic gradient descent (SGD), a popular approach to implement distributed learning. Our goal is to design differentially private variants of SGD to be applied in gossip learning, a decentralized learning framework. Known approaches that are suitable for our scenario focus on protecting the gradient that is being computed in each iteration of SGD. This has the drawback that each data point can be accessed only a small number of times. We propose a solution in which we effectively publish the entire database in a differentially private way so that linear learners could be run that are allowed to access any (perturbed) data point any number of times. This flexibility is very useful when using the method in combination with distributed learning environments. We show empirically that the performance of the obtained model is comparable to that of previous gradient-based approaches and it is even superior in certain scenarios.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n07_Ali,
        title     = {Multi-Layer Cross Domain Reasoning over Distributed Autonomous IoT Applications},
        author    = {Muhammad Intizar Ali and
                     Pankesh Patel and
                     Soumya Kanti Datta and
                     Amelie Gyrard},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {75--90},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613451},
        urn       = {urn:nbn:de:101:1-2017080613451},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Due to the rapid advancements in the sensor technologies and IoT, we are witnessing a rapid growth in the use of sensors and relevant IoT applications. A very large number of sensors and IoT devices are in place in our surroundings which keep sensing dynamic contextual information. A true potential of the wide-spread of IoT devices can only be realized by designing and deploying a large number of smart IoT applications which can provide insights on the data collected from IoT devices and support decision making by converting raw sensor data into actionable knowledge. However, the process of getting value from sensor data streams and converting these raw sensor values into actionable knowledge requires extensive efforts from IoT application developers and domain experts. In this paper, our main aim is to propose a multi-layer cross domain reasoning framework, which can support application developers, end-users and domain experts to automatically understand relevant events and extract actionable knowledge with minimal efforts. Our framework reduces the efforts required for IoT applications development (i) by supporting automated application code generation and access mechanisms using IoTSuite, (ii) by leveraging from Machine-to-Machine Measurement (M3) framework to exploit semantic technologies and domain knowledge, and (iii) by using automated sensor discovery and complex event processing of relevant events (ACEIS Middleware) at the multiple data processing layers and different stages of the IoT application development life cycle. In the essence, our framework supports the end-users and IoT application developers to design innovative IoT applications by reducing the programming efforts, by identifying relevant events and by suggesting potential actions based on complex event processing and reasoning for cross-domain IoT applications.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n08_Margi,
        title     = {Sensing as a Service: Secure Wireless Sensor Network Infrastructure Sharing for the Internet of Things},
        author    = {Cintia B. Margi and
                     Renan C. A. Alves and
                     Johanna Sepulveda},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {91--102},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613467},
        urn       = {urn:nbn:de:101:1-2017080613467},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Internet of Things (IoT) andWireless Sensor Networks (WSN) are composed of devices capable of sensing/actuation, communication and processing. They are valuable technology for the development of applications in several areas, such as environmental, industrial and urban monitoring and processes controlling. Given the challenges of different protocols and technologies used for communication, resource constrained devices nature, high connectivity and security requirements for the applications, the main challenges that need to be addressed include: secure communication between IoT devices, network resource management and the protected implementation of the security mechanisms. In this paper, we present a secure Software-Defined Networking (SDN) based framework that includes: communication protocols, node task programming middleware, communication and computation resource management features and security services. The communication layer for the constrained devices considers IT-SDN as its basis. Concerning security, we address the main services, the type of algorithms to achieve them, and why their secure implementation is needed. Lastly, we showcase how the Sensing as a Service paradigm could enable WSN usage in more environments.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n09_Sparber,
        title     = {Mitigating Radio Interference in Large IoT Networks through Dynamic CCA Adjustment},
        author    = {Tommy Sparber and
                     Carlo Alberto Boano and
                     Salil S. Kanhere and
                     Kay R{\"o}mer},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {103--113},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613511},
        urn       = {urn:nbn:de:101:1-2017080613511},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The performance of low-power wireless sensor networks used to build Internet of Things applications often suffers from radio interference generated by co-located wireless devices or from jammers maliciously placed in their proximity. As IoT devices typically operate in unsupervised large-scale installations, and as radio interference is typically localized and hence affects only a portion of the nodes in the network, it is important to give low-power wireless sensors and actuators the ability to autonomously mitigate the impact of surrounding interference. In this paper we present our approach DynCCA, which dynamically adapts the clear channel assessment threshold of IoT devices to minimize the impact of malicious or unintentional interference on both network reliability and energy efficiency. First, we describe how varying the clear channel assessment threshold at run-time using only information computed locally can help to minimize the impact of unintentional interference from surrounding devices and to escape jamming attacks. We then present the design and implementation of DynCCA on top of ContikiMAC and evaluate its performance on wireless sensor nodes equipped with IEEE 802.15.4 radios. Our experimental investigation shows that the use of DynCCA in dense IoT networks can increase the packet reception rate by up to 50\% and reduce the energy consumption by a factor of 4.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n10_Zadorozhny,
        title     = {Data Credence in IoT: Vision and Challenges},
        author    = {Vladimir I. Zadorozhny and
                     Prashant Krishnamurthy and
                     Mai Abdelhakim and
                     Konstantinos Pelechrinis and
                     Jiawei Xu},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {114--126},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613498},
        urn       = {urn:nbn:de:101:1-2017080613498},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n11_Miladinovic,
        title     = {A Highly Scalable IoT Architecture through Network Function Virtualization},
        author    = {Igor Miladinovic and
                     Sigrid Schefer-Wenzl},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {127--135},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613543},
        urn       = {urn:nbn:de:101:1-2017080613543},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {As the number of devices for Internet of Things (IoT) is rapidly growing, existing communication infrastructures are forced to continually evolve. The next generation network infrastructure is expected to be virtualized and able to integrate different kinds of information technology resources. Network Functions Virtualization (NFV) is one of the leading concepts facilitating the operation of network services in a scalable manner. In this paper, we present an architecture involving NFV to meet the requirements of highly scalable IoT scenarios. We highlight the benefits and challenges of our approach for IoT stakeholders. Finally, the paper illustrates our vision of how the proposed architecture can be applied in the context of a state-of-the-art high-tech operating room, which we are going to realize in future work.}
    }

BibTex:

    @Article{OJIOT_2017v3i1n12_Kross,
        title     = {Towards a Model-driven Performance Prediction Approach for Internet of Things Architectures},
        author    = {Johannes Kro{\ss} and
                     Sebastian Voss and
                     Helmut Krcmar},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2017},
        volume    = {3},
        number    = {1},
        pages     = {136--141},
        note      = {Special Issue: Proceedings of the International Workshop on Very Large Internet of Things (VLIoT 2017) in conjunction with the VLDB 2017 Conference in Munich, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017080613524},
        urn       = {urn:nbn:de:101:1-2017080613524},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Indisputable, security and interoperability play major concerns in Internet of Things (IoT) architectures and applications. In this paper, however, we emphasize the role and importance of performance and scalability as additional, crucial aspects in planning and building sustainable IoT solutions. IoT architectures are complicated system-of-systems that include different developer roles, development processes, organizational units, and a multilateral governance. Its performance is often neglected during development but becomes a major concern at the end of development and results in supplemental efforts, costs, and refactoring. It should not be relied on linearly scaling for such systems only by using up-to-date technologies that may promote such behavior. Furthermore, different security or interoperability choices also have a considerable impact on performance and may result in unforeseen trade-offs. Therefore, we propose and pursue the vision of a model-driven approach to predict and evaluate the performance of IoT architectures early in the system lifecylce in order to guarantee efficient and scalable systems reaching from sensors to business applications.}
    }