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Multi-Layer Cross Domain Reasoning over Distributed Autonomous IoT Applications
Full text linkDue 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
Sensing as a Service: Secure Wireless Sensor Network Infrastructure Sharing for the Internet of Things
Full text linkInternet 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
An NVM Aware MariaDB Database System and Associated IO Workload on File Systems
Full text linkMariaDB is a community-developed fork of the MySQL relational database management system and originally designed and implemented in order to use the traditional spinning disk architecture. With Non-Volatile memory (NVM) technology now in the forefront and main stream for server storage (Data centers), MariaDB addresses the need by adding support for NVM devices and introduces NVM Compression method. NVM Compression is a novel hybrid technique that combines application level compression with flash awareness for optimal performance and storage efficiency. Utilizing new interface primitives exported by Flash Translation Layers (FTLs), we leverage the garbage collection available in flash devices to optimize the capacity management required by compression systems. We implement NVM Compression in the popular MariaDB database and use variants of commonly available POSIX file system interfaces to provide the extended FTL capabilities to the user space application. The experimental results show that the hybrid approach of NVM Compression can improve compression performance by 2-7x, deliver compression performance for flash devices that is within 5% of uncompressed performance, improve storage efficiency by 19% over legacy Row-Compression, reduce data writes by up to 4x when combined with other flash aware techniques such as Atomic Writes, and deliver further advantages in power efficiency and CPU utilization. Various micro benchmark measurement and findings on sparse files call for required improvement in file systems for handling of punch hole operations on files
First Edition of the Very Large Internet of Things Workshop (VLIoT)
Full text linkThis 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)
Cyber Supply Chain Risks in Cloud Computing - Bridging the Risk Assessment Gap
Full text linkCloud computing represents a significant paradigm shift in the delivery of information technology (IT) services. The rapid growth of the cloud and the increasing security concerns associated with the delivery of cloud services has led many researchers to study cloud risks and risk assessments. Some of these studies highlight the inability of current risk assessments to cope with the dynamic nature of the cloud, a gap we believe is as a result of the lack of consideration for the inherent risk of the supply chain. This paper, therefore, describes the cloud supply chain and investigates the effect of supply chain transparency in conducting a comprehensive risk assessment. We conducted an industry survey to gauge stakeholder awareness of supply chain risks, seeking to find out the risk assessment methods commonly used, factors that hindered a comprehensive evaluation and how the current state-of-the-art can be improved. The analysis of the survey dataset showed the lack of flexibility of the popular qualitative assessment methods in coping with the risks associated with the dynamic supply chain of cloud services, typically made up of an average of eight suppliers. To address these gaps, we propose a Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, a quantitative risk assessment model which is supported by decision support analysis and supply chain mapping in the identification, analysis and evaluation of cloud risks
Security and Compliance Ontology for Cloud Service Agreements
Full text linkCloud computing is a business paradigm where two important roles must be defined: provider and consumer. Providers offer services (e.g. web application, web services, and databases) and consumers pay for using them. The goal of this research is to focus on security and compliance aspects of cloud service. An ontology is introduced, which is the conceptualization of cloud domain, for analyzing different compliance aspects of cloud agreements. The terms, properties and relations are shown in a diagram. The proposed ontology can help service consumers to extract relevant data from service level agreements, to interpret compliance regulations, and to compare different contractual terms. Finally, some recommendations are presented for cloud consumers to adopt services and evaluate security risks
Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements
Full text linkMachine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper
Scalable Generation of Type Embeddings Using the ABox
Full text linkStructured knowledge bases gain their expressive power from both the ABox and TBox. While the ABox is rich in data, the TBox contains the ontological assertions that are often necessary for logical inference. The crucial links between the ABox and the TBox are served by is-a statements (formally a part of the ABox) that connect instances to types, also referred to as classes or concepts. Latent space embedding algorithms, such as RDF2Vec and TransE, have been used to great effect to model instances in the ABox. Such algorithms work well on large-scale knowledge bases like DBpedia and Geonames, as they are robust to noise and are low-dimensional and real-valued. In this paper, we investigate a supervised algorithm for deriving type embeddings in the same latent space as a given set of entity embeddings. We show that our algorithm generalizes to hundreds of types, and via incremental execution, achieves near-linear scaling on graphs with millions of instances and facts. We also present a theoretical foundation for our proposed model, and the means of validating the model. The empirical utility of the embeddings is illustrated on five partitions of the English DBpedia ABox. We use visualization and clustering to show that our embeddings are in good agreement with the manually curated TBox. We also use the embeddings to perform a soft clustering on 4 million DBpedia instances in terms of the 415 types explicitly participating in is-a relationships in the DBpedia ABox. Lastly, we present a set of results obtained by using the embeddings to recommend types for untyped instances. Our method is shown to outperform another feature-agnostic baseline while achieving 15x speedup without any growth in memory usage
Latency Optimization in Large-Scale Cloud-Sensor Systems
Full text linkWith 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
A Classification Framework for Beacon Applications
Full text linkBeacons have received considerable attention in recent years, which is partially due to the fact that they serve as a flexible and versatile replacement for RFIDs in many applications. However, beacons are mostly considered from a purely technical perspective. This paper provides a conceptual view on application scenarios for beacons and introduces a novel framework for characterizing these. The framework consists of four dimensions: device movement, action trigger, purpose type, and connectivity requirements. Based on these, three archetypical scenarios are described. Finally, event-condition-action rules and online algorithms are used to formalize the backend of a beacon architecture