When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

When things matter: A survey on data-centric Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Efficient State Retention for Transiently-powered Embedded Sensing

We present state retention techniques to support embedded sensing applications on 32-bit microcontrollers whose energy provisioning is assisted through ambient harvesting or wireless energy transfer. As energy availability is likely erratic in these settings, applications may be unpredictably interrupted. To behave dependably, applications should resume from where they left as soon as energy is newly available. We investigate the fundamental building block necessary to this end, and conceive three mechanisms to checkpoint and restore a device's state on stable storage quickly and in an energy-efficient manner. The problem is unique in many regards; for example, because of the distinctive performance vs. energy trade-offs of modern 32-bit microcontrollers and the peculiar characteristics of current flash chips. Our results, obtained from real experiments using two different platforms, crucially indicate that there is no ``one-size-fits-all'' solution. The performance depends on factors such as the amount of data to handle, how in memory the data is laid out, as well as an application's read/write patterns

Digitalization of Offshore Wind Farm Systems

Master's thesis in Offshore Technology: Industrial asset managementThis thesis investigates how new digital technologies and digitalization can help further evolve the offshore wind industry using the Industry 4.0 concept as a basis and explores how technologies within this concept can contribute to an offshore wind farm that overcomes some of these challenges. The study focuses on an offshore wind farm from a systems perspective, including respective modules, and where the Industry 4.0 technologies can be applied. Following this is the establishment of a systematic digitalization framework and a proposal on how to cope with increased volumes of data, connectivity, and complexity.publishedVersio

Secure Time-Aware Provenance for Distributed Systems

Operators of distributed systems often find themselves needing to answer forensic questions, to perform a variety of managerial tasks including fault detection, system debugging, accountability enforcement, and attack analysis. In this dissertation, we present Secure Time-Aware Provenance (STAP), a novel approach that provides the fundamental functionality required to answer such forensic questions – the capability to “explain” the existence (or change) of a certain distributed system state at a given time in a potentially adversarial environment. This dissertation makes the following contributions. First, we propose the STAP model, to explicitly represent time and state changes. The STAP model allows consistent and complete explanations of system state (and changes) in dynamic environments. Second, we show that it is both possible and practical to efficiently and scalably maintain and query provenance in a distributed fashion, where provenance maintenance and querying are modeled as recursive continuous queries over distributed relations. Third, we present security extensions that allow operators to reliably query provenance information in adversarial environments. Our extensions incorporate tamper-evident properties that guarantee eventual detection of compromised nodes that lie or falsely implicate correct nodes. Finally, the proposed research results in a proof-of-concept prototype, which includes a declarative query language for specifying a range of useful provenance queries, an interactive exploration tool, and a distributed provenance engine for operators to conduct analysis of their distributed systems. We discuss the applicability of this tool in several use cases, including Internet routing, overlay routing, and cloud data processing

A bottom-up approach to emulating emotions using neuromodulation in agents

A bottom-up approach to emulating emotions is expounded in this thesis. This is intended to be useful in research where a phenomenon is to be emulated but the nature of it can not easily be defined. This approach not only advocates emulating the underlying mechanisms that are proposed to give rise to emotion in natural agents, but also advocates applying an open-mind as to what the phenomenon actually is. There is evidence to suggest that neuromodulation is inherently responsible for giving rise to emotions in natural agents and that emotions consequently modulate the behaviour of the agent. The functionality provided by neuromodulation, when applied to agents with self-organising biologically plausible neural networks, is isolated and studied. In research efforts such as this the definition should emerge from the evidence rather than postulate that the definition, derived from limited information, is correct and should be implemented. An implementation of a working definition only tells us that the definition can be implemented. It does not tell us whether that working definition is itself correct and matches the phenomenon in the real world. If this model of emotions was assumed to be true and implemented in an agent, there would be a danger of precluding implementations that could offer alternative theories as to the relevance of neuromodulation to emotions. By isolating and studying different mechanisms such as neuromodulation that are thought to give rise to emotions, theories can arise as to what emotions are and the functionality that they provide. The application of this approach concludes with a theory as to how some emotions can operate via the use of neuromodulators. The theory is explained using the concepts of dynamical systems, free-energy and entropy.EPSRC Stirling University, Computing Science departmental gran

A scientometric analysis of the emerging topics in general computer science

Citations have been an acceptable journal performance metric used by many indexing databases for inclusion and discontinuation of journals in their list. Therefore, editorial teams must maintain their journal performance by increasing article citations for continuous content indexing in the databases. With this aim in hand, this study intended to assist the editorial team of the Journal of Information and Communication Technology (JICT) in increasing the performance and impact of the journal. Currently, the journal has suffered from low citation count, which may jeopardise its sustainability. Past studies in library science suggested a positive correlation between keywords and citations. Therefore, keyword and topic analyses could be a solution to address the issue of journal citation. This article described a scientometric analysis of emerging topics in general computer science, the Scopus subject area for which JICT is indexed. This study extracted bibliometric data of the top 10% journals in the subject area to create a dataset of 5,546 articles. The results of the study suggested ten emerging topics in computer science that can be considered by the journal editorial team in selecting articles and a list of highly used keywords in articles published in 2019 and 2020 (as of 15 April 2020). The outcome of this study might be considered by the JICT editorial team and other journals in general computer science that suffer from a similar issue

Social media mining as an opportunistic citizen science model in ecological monitoring: a case study using invasive alien species in forest ecosystems.

Dramatische ökologische, ökonomische und soziale Veränderungen bedrohen die Stabilität von Ökosystemen weltweit und stellen zusammen mit neuen Ansprüchen an die vielfältigen Ökosystemdienstleistungen von Wäldern neue Herausforderungen für das forstliche Management und Monitoring dar. Neue Risiken und Gefahren, wie zum Beispiel eingebürgerte invasive Arten (Neobiota), werfen grundsätzliche Fragen hinsichtlich etablierter forstlicher Managementstrategien auf, da diese Strategien auf der Annahme stabiler Ökosysteme basieren. Anpassungsfähige Management- und Monitoringstrategien sind deshalb notwendig, um diese neuen Bedrohungen und Veränderungen frühzeitig zu erkennen. Dies erfordert jedoch ein großflächiges und umfassendes Monitoring, was unter Maßgabe begrenzter Ressourcen nur bedingt möglich ist. Angesichts dieser Herausforderungen haben Forstpraktiker und Wissenschaftler begonnen auch auf die Unterstützung von Freiwilligen in Form sogenannter „Citizen Science“-Projekte (Bürgerwissenschaft) zurückzugreifen, um zusätzliche Informationen zu sammeln und flexibel auf spezifische Fragestellungen reagieren zu können. Mit der allgemeinen Verfügbarkeit des Internets und mobiler Geräte ist in Form sogenannter sozialer Medien zudem eine neue digitale Informationsquelle entstanden. Mittels dieser Technologien übernehmen Nutzer prinzipiell die Funktion von Umweltsensoren und erzeugen indirekt ein ungeheures Volumen allgemein zugänglicher Umgebungs- und Umweltinformationen. Die automatische Analyse von sozialen Medien wie Facebook, Twitter, Wikis oder Blogs, leistet inzwischen wichtige Beiträge zu Bereichen wie dem Monitoring von Infektionskrankheiten, Katastrophenschutz oder der Erkennung von Erdbeben. Anwendungen mit einem ökologischen Bezug existieren jedoch nur vereinzelt, und eine methodische Bearbeitung dieses Anwendungsbereichs fand bisher nicht statt. Unter Anwendung des Mikroblogging-Dienstes Twitter und des Beispiels eingebürgerter invasiver Arten in Waldökosystemen, verfolgt die vorliegende Arbeit eine solche methodische Bearbeitung und Bewertung sozialer Medien im Monitoring von Wäldern. Die automatische Analyse sozialer Medien wird dabei als opportunistisches „Citizen Science“-Modell betrachtet und die verfügbaren Daten, Aktivitäten und Teilnehmer einer vergleichenden Analyse mit existierenden bewusst geplanten „Citizen Science“-Projekten im Umweltmonitoring unterzogen. Die vorliegenden Ergebnisse zeigen, dass Twitter eine wertvolle Informationsquelle über invasive Arten darstellt und dass soziale Medien im Allgemeinen traditionelle Umweltinformationen ergänzen könnten. Twitter ist eine reichhaltige Quelle von primären Biodiversitätsbeobachtungen, einschließlich solcher zu eingebürgerten invasiven Arten. Zusätzlich kann gezeigt werden, dass die analysierten Twitterinhalte für die untersuchten Arten markante Themen- und Informationsprofile aufweisen, die wichtige Beiträge im Management invasiver Arten leisten können. Allgemein zeigt die Studie, dass einerseits das Potential von „Citizen Science“ im forstlichen Monitoring derzeit nicht ausgeschöpft wird, aber andererseits mit denjenigen Nutzern, die Biodiversitätsbeobachtungen auf Twitter teilen, eine große Zahl von Individuen mit einem Interesse an Umweltbeobachtungen zur Verfügung steht, die auf der Basis ihres dokumentierten Interesses unter Umständen für bewusst geplante „Citizen Science“-Projekte mobilisiert werden könnten. Zusammenfassend dokumentiert diese Studie, dass soziale Medien eine wertvolle Quelle für Umweltinformationen allgemein sind und eine verstärkte Untersuchung verdienen, letztlich mit dem Ziel, operative Systeme zur Unterstützung von Risikobewertungen in Echtzeit zu entwickeln.Major environmental, social and economic changes threatening the resilience of ecosystems world-wide and new demands on a broad range of forest ecosystem services present new challenges for forest management and monitoring. New risks and threats such as invasive alien species imply fundamental challenges for traditional forest management strategies, which have been based on assumptions of permanent ecosystem stability. Adaptive management and monitoring is called for to detect new threats and changes as early as possible, but this requires large-scale monitoring and monitoring resources remain a limiting factor. Accordingly, forest practitioners and scientists have begun to turn to public support in the form of “citizen science” to react flexibly to specific challenges and gather critical information. The emergence of ubiquitous mobile and internet technologies provides a new digital source of information in the form of so-called social media that essentially turns users of these media into environmental sensors and provides an immense volume of publicly accessible, ambient environmental information. Mining social media content, such as Facebook, Twitter, Wikis or Blogs, has been shown to make critical contributions to epidemic disease monitoring, emergency management or earthquake detection. Applications in the ecological domain remain anecdotal and a methodical exploration for this domain is lacking. Using the example of the micro-blogging service Twitter and invasive alien species in forest ecosystems, this study provides a methodical exploration and assessment of social media for forest monitoring. Social media mining is approached as an opportunistic citizen science model and the data, activities and contributors are analyzed in comparison to deliberate ecological citizen science monitoring. The results show that Twitter is a valuable source of information on invasive alien species and that social media in general could be a supplement to traditional monitoring data. Twitter proves to be a rich source of primary biodiversity observations including those of the selected invasive species. In addition, it is shown that Twitter content provides distinctive thematic profiles that relate closely to key characteristics of the explored invasive alien species and provide valuable insights for invasive species management. Furthermore, the study shows that while there are underutilized opportunities for citizen science in forest monitoring, the contributors of biodiversity observations on Twitter show a more than casual interest in this subject and represent a large pool of potential contributors to deliberate citizen science monitoring efforts. In summary, social online media are a valuable source for ecological monitoring information in general and deserve intensified exploration to arrive at operational systems supporting real-time risk assessments