Martin Alexander Neumann

Preserving scientific processes from design to publications

Digital Preservation has so far focused mainly on digital objects that are static in their nature, such as text and multimedia documents. However, there is an increasing demand to extend the applications towards dynamic objects and whole processes, such as scientific workflows in the domain of E-Science. This calls for a revision and extension of current concepts, methods and practices. Important questions to address are e.g. what needs to be captured at ingest, how do the digital objects need to be described, which preservation actions are applicable and how can the preserved objects be evaluated. In this paper we present a conceptual model for capturing the required information and show how this can be linked to evaluating the re-invocation of a preserved process.

Dinam: A wireless sensor network concept and platform for rapid development

Dinam is a novel approach to simplified rapid prototyping of wireless sensor network applications as well as an according WSN platform. As opposed to the traditional mote-based development archetype, dinam proposes combining the development steps into a single continuous, fluid process that is completely integrated into the node. The dinam concept sensor node integrates all development tools, source code and other data into the sensor node system. It is claimed that this concept will greatly reduce the amount of effort required to develop wireless sensor network applications by removing the overhead of installation, iterative development steps and complexity of the development process. In order to confirm or refute this claim, a first prototype for educational purposes is developed and presented which implements the dinam approach. The development of applications is evaluated in terms of time required for a specific scenario with a user study. The results presented here indicate that an integrated instruction and development period of 10 minutes is sufficient for simple applications using the dinam approach.

A framework for short-term activity-aware load forecasting

In this paper, we present a framework for implementing short-term load forecasting, in which statistical time series prediction methods and machine learning-based regression methods, can be configured to benchmark their performance against each other on given data of smart meters and other related exogenous variables. Besides the prediction methods, forecasting performance also depends on the quality of training data. This is addressed by two characteristics of our framework on data collection and preprocessing. The first one is to introduce a human activity variable as an additional load influencing factor which reflects anomalous load patterns by aperiodic human activity. The second characteristic is to wavelet transform training data during the preprocessing stage to better extract redundant information from meter data. To investigate the feasibility of the proposed framework, a preliminary case study for predicting daily power consumption of several individual smart meters, using real-world data, is presented. The results indicate that, in general, the aggregation level of meter data and activity data matters.

A Platform-as-a-Service for in-situ development of wireless sensor network applications

In this paper we present a Platform-as-a-Service (PaaS) approach for rapid development of wireless sensor network (WSN) applications based on the dinam-mite concept, i.e. an embedded web-based development environment and run-time platform for WSN systems integrated in a single information appliance. The PaaS is hosted by a cloud of dinam-mite nodes which facilitates the on-demand development, deployment and integration of WSN applications. We introduce the dinam Cloud architecture and focus, in this paper, on the PaaS layer established by the dinam-mite nodes. In addition to the description of this so-called dinam PaaS, a performance analysis of the dinam-mite node towards its applicability to forming a dinam PaaS layer is demonstrated. We then present the MASON mobile vehicular network as an example of such a WSN which delivers spatially and temporally fine-grained environmental measurements within the city of Beijing, and illustrate how to utilize the dinam PaaS for integrating the data from the MASON network into its back-end business system. Finally, we discuss the five essential properties of the Cloud Computing stack, according to the NIST definition, with respect to the dinam PaaS and illustrate the benefits of the dinam PaaS for system integration as well as WSN application development.

A control loop approach for integrating the future decentralized power markets and grids

We are facing a restructuring of the power industry towards a smart grid. The vision of the smart grid represents not only the creation of intelligent power supply networks to allow efficient and reliable use of energy resources, but also the redesign of the market structure coupled with it. In order to develop a smart grid-ready power market, the integration of the physical reality of the power grid into the economic market model has been set as the first requirement. To address this problem, we present a feedback control model to interconnect the physical grid and the economic market in a decoupled control loop. Our proposed control loop consists of two subsystems, namely an Optimal Power Flow (OPF)-based physical system and a Continuous Double Auction (CDA)-based economic system. A dynamic coefficient matrix generated by the Locational Marginal Pricing (LMP) algorithm is adopted for the market clearing mechanism to account for the real-time power flow and transmission constraints. Finally, we demonstrate some initial experiments for a feasibility test of the interaction between the proposed physical power system and economic power market.

Monitoring for digital preservation of processes

Digital Preservation is an important challenge for the information society. Reliable information and communication technology is crucial for most companies and software failure, is a considerable risk. Use of technologies such as Software as a Service (SaaS) and Internet of Services (IoS) means that business processes are increasingly supported by distributed, service oriented systems. We propose a concept and methods for capturing of contextual information, event causality and timing for Digital Preservation of distributed business processes and services. The architecture is derived from an architecture for monitoring sensing systems. We add a reasoner that can check whether processes adhere to explicit contracts and detect behavior anomalies, and we sketch how an inductive learner can be used to detect anomalies not covered by these contracts.

Always-On Web of Things Infrastructure using Dynamic Software Updating

Applications in the Internet of Things require security, high availability and real-time communications for reliable operation. But their software contains issues that need to be fixed. Timely installation of software updates allows securing vulnerable software quickly but conventionally disrupts availability and communications. Rolling update schemes prevent disruptions, but have to be performed carefully. Dynamic software updating significantly shortens the installation duration of updates by implementing them in-memory, allowing timely hot fixing and installation of new features without service disruption or degradation in soft real-time communications. As the Web of Things settles on common technologies, we see the need for quick hot fixing of security vulnerabilities in widespread components. To demonstrate the benefits, we present a case study in which the moquette message broker has been retrofitted for dynamic updating with our update system. We provide dynamic patches for all three releases of moquette and perform these updates on moquette at saturated load stressed by a 1:10 fan-out benchmark with 100 simulated publishers. While no connections or messages are lost, it demonstrates that the throughput drops only for 1-2s and that average message latency peaks up to 1000ms during this time.

Towards the Shop Floor App Ecosystem: Using the Semantic Web for Gluing Together Apps into Mashups

Any upcoming industrial revolution will rely on the ability to harness software as the nervous system of future production environments. This paper proposes an app ecosystem as the potential key enabler of industry digitization and argues for the need of semantic web technologies as primary enablers for app interoperability. We shortly discuss how we envision the emergence of semantically annotated apps on the manufacturing shop floor. Subsequently, we demonstrate how a loosely coupled mashup of apps can easily form a full stack internet-of-things solution that covers sensor data from its origin toward its visualization in a (mobile) web browser.

From Load Forecasting to Demand Response - A Web of Things Use Case

This paper provides a Web of Things use case from a personalized load forecasting service to a gamified demand response program. Combining real-world measuring applications with web-based applications opens new opportunities to the smart grid. For this purpose, we propose a Web of Things framework for a novel load forecasting process at the appliance level. Firstly, we illustrate the concept design of the Web of Things framework consisting of the sensing infrastructure, the activity recognition and the load forecasting modules. Secondly, we show how we guarantee the modularity and flexibility for implementing all the three modules in a web-based manner. On top of our infrastructure, we propose an extended Web of Things use case by integrating our load forecasting approach into a demand response concept.

Predicting Target Events in Industrial Domains

In industrial environments, machine faults have a high impact on productivity due to the high costs it can cause. Machine generated event logs are a abundant source of information for understanding the causes and events that led to a critical event in the machine. In this work, we present a Sequence-Mining based technique to automatically extract sequential patterns of events from machine log data for understanding and predicting machine critical events. By experiments using real data with millions of log entries from over 150 industrial computer numerical control (CNC) cutting machines, we show how our technique can be successfully used for understanding the root causes of certain critical events, as well as for building monitors for predicting them long before they happen, outperforming existing techniques.