Prototype Implementation Research Articles

A Resilient Group-Based Multisubset Data Aggregation Scheme for Smart Grid

Smart meters are deployed in the smart grid to achieve bidirectional communication, the control center can monitor, predict energy consumption data in real-time, and adjust energy supply dynamically. Unfortunately, real-time data may divulge users private information. To protect the privacy of real-time data, data aggregation schemes have been proposed to assist the control center in adjusting the supply to meet users electricity demands without sacrificing data privacy. However, extreme weather events and potential attacks may damage the meters and change the structure of the smart grid dynamically, it is important to improve the reliability of the data aggregation scheme. Even if some schemes have improved reliability, but the scalability is poor, they are not suitable for the dynamically changing smart grid network structure. To meet this end, a resilient data aggregation scheme for the smart grid is proposed, which (1) offers better reliability by defending against malicious attacks, group management techniques are proposed, and meters can update their keys when the smart grid network structure changes; (2) affords higher scalability; (3) enables the control center to make fine-grained adjustments. A prototype implementation shows that the proposed scheme is efficient enough for smart meters.

Model-Based Diagnosis of Real-Time Systems: Robustness Against Varying Latency, Clock Drift, and Out-of-Order Observations

Online fault diagnosis techniques are a key enabler of effective failure mitigation. For real-time systems, the problem of identifying faults is aggravated by timing imprecisions such as varying latency between events and their observation. This paper tackles the challenge of diagnosing faults based on partial observations which are subject to timing imprecisions and potentially made out-of-order due to latency. In this paper, we develop a theory of robust real-time diagnosis importing well-established notions from timed automata theory and the diagnosis of discrete event systems. The theory itself enables a foundational understanding and investigation of the problem and its intricacies. Based on this theory, we further devise an online diagnosis algorithm consuming observations incrementally as they are made and enabling diagnosis, whenever possible, within a bounded worst-case delay. We prove the correctness of the algorithm and its properties with respect to the theory. Aiming at practical feasibility, we also show how to obtain sound but not necessarily complete diagnosis results with space and time requirements bounded by the size of the system model and independent of the number of observations. Finally, using a prototypical implementation, we report on first empirical results obtained by simulation of a small excerpt of an industrial automation example.

More or less discrimination? Practical feasibility of fairness auditing of technologies for personnel selection

The use of technologies in personnel selection has come under increased scrutiny in recent years, revealing their potential to amplify existing inequalities in recruitment processes. To date, however, there has been a lack of comprehensive assessments of respective discriminatory potentials and no legal or practical standards have been explicitly established for fairness auditing. The current proposal of the Artificial Intelligence Act classifies numerous applications in personnel selection and recruitment as high-risk technologies, and while it requires quality standards to protect the fundamental rights of those involved, particularly during development, it does not provide concrete guidance on how to ensure this, especially once the technologies are commercially available. We argue that comprehensive and reliable auditing of personnel selection technologies must be contextual, that is, embedded in existing processes and based on real data, as well as participative, involving various stakeholders beyond technology vendors and customers, such as advocacy organizations and researchers. We propose an architectural draft that employs a data trustee to provide independent, fiduciary management of personal and corporate data to audit the fairness of technologies used in personnel selection. Drawing on a case study conducted with two state-owned companies in Berlin, Germany, we discuss challenges and approaches related to suitable fairness metrics, operationalization of vague concepts such as migration* and applicable legal foundations that can be utilized to overcome the fairness-privacy-dilemma arising from uncertainties associated with current laws. We highlight issues that require further interdisciplinary research to enable a prototypical implementation of the auditing concept in the mid-term.

NFV-empowered digital twin cyber platform: Architecture, prototype, and a use case

Building, managing, upgrading, and diagnosing networks becomes increasingly difficult due to the increased complexity in protocol design, enlarged network scale, rapidly-growing types of network applications, and the uncertainty of network faults and cyber attacks. It is not satisfactory to rely on network simulators/emulators for conducting these tasks because of different defects including poor fidelity, long implementing time, and weak interconnection with physical networks. To enable the interplay of networks and their virtual counterparts, this paper aims to build a Network Functions Virtualization (NFV)-empowered digital twin cyber platform (DTCP) for enabling network innovation and test with high fidelity, low complexity, and high timeliness. First, after regulating the paradigm of DTCP, a six-dimensional model is defined to profile a DTCP. Second, the architecture of the DTCP is detailed along with the key enabling technologies. Third, a prototype implementation of the DTCP is shortly introduced. Lastly, a use case based on fault diagnosis for video conference system is tested on the prototype. Results show that our DTCP prototype is a good platform for conducting different types of network operations without explicitly providing the mapping model between physical and virtual networks.

Operationally-based program equivalence proofs using LCTRSs

We propose an operationally-based framework for deductive proofs of program equivalence. It is based on encoding the language semantics as logically constrained term rewriting systems (LCTRSs) and the two programs as terms. As a novelty of our method, we show that it enables relational reasoning about programs in various settings, which are encoded in the operational semantics. For example, we show how our method can be used to prove programs that are equivalent when considering an unbounded stack, but where the equivalence fails to hold if the stack is bounded. We also show how to formalize read-sets and write-sets of symbolic expressions and statements by extending the operational semantics in a conservative way. This enables the relational verification of program schemas, which we exploit to prove compiler optimizations that cannot be handled by other tools. Our method requires an extension of standard LCTRSs with axiomatized symbols, which generate new research questions. We also present a prototype implementation that proves the practical feasibility of our approach.

Towards Logistics 4.0: A Skill-Based OPC UA Communication between WMS and the PLC of an Automated Storage and Retrieval System

In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection ofmanufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UAbased approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation.

HANNA: Human-friendly provisioning and configuration of smart devices

Today, there are billions of connected IoT devices and their number continues to grow as they contribute to the digitalization of infrastructures. However, the deployment process of these smart wireless devices when delivered to customer premises is slow and error prone as each of them needs to be provisioned with authentication credentials to access the corporate network. In this paper, we propose HANNA, a human-friendly provisioning and configuration framework for smart devices, that extends the zero-touch paradigm to large IoT deployments by introducing voice assisted configuration in combination with large scale ad-hoc communications to overcome the initial installation effort of IoT deployments. The most prominent role in HANNA is played by the assisting device, which includes a voice assistant capable of correctly understanding a minimum number of keywords required for initial provisioning and configuration of the devices. The device’s role is to interact with the user and ensure that all provisioning details are received. These are then converted into appropriate machine instructions for further use by the mass provisioning mechanism. We provide an example prototype implementation of HANNA and evaluate the performance of the assisting device in the human-to-machine communication phase and the performance of the selected communication technique in the machine-to-machine communication phase. Our results show the potential of existing speech-to-text engines for this application area and also reveal shortcomings with respect to the robustness of the engines in office-like working environments as well as with respect to user’s gender and language proficiency level. Additionally we show that the proposed machine-to-machine provisioning approach is always faster compared to manual provisioning for cases with more than ten devices.

MSV: a modular structural variant caller that reveals nested and complex rearrangements by unifying breakends inferred directly from reads

Structural variant (SV) calling belongs to the standard tools of modern bioinformatics for identifying and describing alterations in genomes. Initially, this work presents several complex genomic rearrangements that reveal conceptual ambiguities inherent to the representation via basic SV. We contextualize these ambiguities theoretically as well as practically and propose a graph-based approach for resolving them. For various yeast genomes, we practically compute adjacency matrices of our graph model and demonstrate that they provide highly accurate descriptions of one genome in terms of another. An open-source prototype implementation of our approach is available under the MIT license at https://github.com/ITBE-Lab/MA.

Automated translation from domain knowledge to software model: EXCEL2UML in the tunneling domain

The development of software tools is a collaborative process involving both the domain experts and the software engineers. This requires efficient communication considering different expertise and perspectives. Additionally, the two groups utilize language and communication tools in disparate ways. This, in turn, may lead to hidden misunderstandings in the requirement analysis phase and potentially result in implementation problems later on, that is difficult and costly to correct. In this paper, we demonstrate the above mentioned challenge via a use case from the tunneling domain. In particular, during the requirement analysis phase for a software capable of handling the data model of the subsoil. The domain experts in the field can best express the complexity of their domain by describing its artifacts, which in most cases are incomprehensible to the software engineers. We outline a method that interleaves requirement analysis and software modeling to enable an iterative increase of the accuracy and completeness of the information extracted from those artifacts and integrated into a flexible software model, which can produce testable software code automatically. Furthermore, we present a prototypical implementation of our method and a preliminary evaluation of the approach.

Managing data replication and distribution in the fog with FReD

SummaryThe heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re‐implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low‐latency, high‐bandwidth, and privacy‐sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications.

Simple odd β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-cycle inequalities for binary polynomial optimization

We consider the multilinear polytope which arises naturally in binary polynomial optimization. Del Pia and Di Gregorio introduced the class of odd β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-cycle inequalities valid for this polytope, showed that these generally have Chvátal rank 2 with respect to the standard relaxation and that, together with flower inequalities, they yield a perfect formulation for cycle hypergraph instances. Moreover, they describe a separation algorithm in case the instance is a cycle hypergraph. We introduce a weaker version, called simple odd β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-cycle inequalities, for which we establish a strongly polynomial-time separation algorithm for arbitrary instances. These inequalities still have Chvátal rank 2 in general and still suffice to describe the multilinear polytope for cycle hypergraphs. Finally, we report about computational results of our prototype implementation. The simple odd β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-cycle inequalities sometimes help to close more of the integrality gap in the experiments; however, the preliminary implementation has substantial computational cost, suggesting room for improvement in the separation algorithm.

Secure Sensor Prototype Using Hardware Security Modules and Trusted Execution Environments in a Blockchain Application: Wine Logistic Use Case

The security of Industrial Internet of Things (IIoT) systems is a challenge that needs to be addressed immediately, as the increasing use of new communication paradigms and the abundant use of sensors opens up new opportunities to compromise these types of systems. In this sense, technologies such as Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs) become crucial for adding new layers of security to IIoT systems, especially to edge nodes that incorporate sensors and perform continuous measurements. These technologies, coupled with new communication paradigms such as Blockchain, offer a high reliability, robustness and good interoperability between them. This paper proposes the design of a secure sensor incorporating the above mentioned technologies—HSMs and a TEE—in a hardware device based on a dual-core architecture. Through this combination of technologies, one of the cores collects the data extracted by the sensors and implements the security mechanisms to guarantee the integrity of these data, while the remaining core is responsible for sending these data through the appropriate communication protocol. This proposed approach fits into the Blockchain networks, which act as an Oracle. Finally, to illustrate the application of this concept, a use case applied to wine logistics is described, where this secure sensor is integrated into a Blockchain that collects data from the storage and transport of barrels, and a performance evaluation of the implemented prototype is provided.

Reestablishing Page Placement Mechanisms for Nested Virtualization

Page placement mechanisms have long been used to reduce cache conflict misses. They become more important in clouds where the emerging way-based cache partitioning is used for better workload isolation but at a cost of increased cache conflicts. However, page placement mechanisms become ineffective in virtualized environments, such as clouds, because the real locations of memory pages (i.e., their host physical addresses) are hidden from guest OSs. The paper proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xPlace</small> as a solution to reestablish page placement mechanisms under the nested virtualization configuration. To keep high portability and low overhead, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xPlace</small> follows an approach that creates a synergy between the host and guest VMs, such that the page placement mechanism inside each guest VM becomes effective even if its page placement decisions are made based on the guest physical addresses of memory pages. The paper addresses the technical issues for implementing this approach in the nested virtualization setting, particularly how to create the synergy with the obstacle created by guest hypervisors sitting between the host and guest VMs. Evaluation based on the prototype implementation and diverse real world applications shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xPlace</small> can greatly reduce cache conflicts and improve application performance in the nested environment.

Enabling Real-Time Remote Monitoring of Ships by Lossless Protocol Transformations

This paper uses data processing techniques to reduce the required transmission bandwidth in ship-to-shore communications. The proposed framework (ONline Efficient Sources Transmission Optimizer -) leverages state-of-the-art technologies and novel algorithms to automatically optimize transmissions under structural (e.g., available bandwidth, fixed packet overhead) and user-defined (e.g., maximum latency) constraints. In addition, authenticates and encrypts the communication between the ship and the shore via mainstream free and open-source software components. Initially, we present the abstract mathematical formulation of the problem, with its assumptions, goal function, constraints, and significant quantities. Then, we introduce the architecture of a system capable of continuously estimating the compressibility, processing and transmission time of streaming data. Such estimations allow to calculate and apply optimal parameters for achieving the best compression ratio. Lastly, using a prototypical implementation, we evaluate the system performance with a Class B ship simulator on two realistic use cases. Our experiments show an excellent compression ratio with maritime protocols (more than 40:1) and a limited latency impact, demonstrating the approach’s viability.

An Anthropomorphic Robotic Hand With a Soft-Rigid Hybrid Structure and Positive- Negative Pneumatic Actuation

Anthropomorphic robotic hands are seeking to achieve key features such as multi-degree-of-freedom motion ability, bi-directional actuation, high adaptability, and sufficient stiffness. In this research, we propose a 10 active degrees-of-freedom anthropomorphic robotic hand with a soft-rigid hybrid structure and positive-negative pneumatic actuation. The fingers of the hand utilize pneumatic actuators composed of soft bellows to actuate a rigid skeleton for bending motion with unidirectional-stretchable nylon elastic fabric as a strain limiting layer. The positive-negative pneumatic actuation mimics the flexors and extensors of the human hand to implement bi-directional finger actuation. We present a prototype implementation of this robotic hand and provide a preliminary evaluation. The experimental results show that the combined positive-negative pneumatic actuation can extend the workspace of the finger and increase the finger stiffness compared to using only positive pneumatic actuation. The proposed robotic hand has good comprehensive performance (generated a blocking forceof 7.5 N, scored 7 in theKapandji test, achieved32 of the 33 grasp postures from the Feix taxonomy, and could implement 5 human grasping strategies) compared to other robotic hands in literatures.

Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L2UniFrac.

Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community. To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific "representative samples." We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac. A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper.

Design and implementation of a bioclimatic housing prototype for the Bolivian highlands

The Mechanical - Electromechanical Engineering research institute, made up of a multidisciplinary team, designs and implements a bioclimatic housing prototype for the Bolivian highlands, located near the Andes Mountains at an altitude of 3,000 to 4,000 meters above sea level. This project was developed and implemented with financing from the Swiss Cooperation for Bolivia, within the Climate Change Adaptation projects.&#x0D; The design process includes: the sociocultural study of the inhabitants of the region, where direct information is collected from the peasants on the conception of housing in their culture and ancestral construction characteristics of their houses. Subsequently, the climate of the region (temperatures, solar radiation, winds and relative humidity) is characterized with the help of a meteorological station, which provided historical records of 5 years. Those data were sufficient to be able to carry out a reliable characterization, with which the Typical Meteorological Year is determined. Likewise, local construction materials that may be useful in the design are studied, analyzing their thermal and mechanical properties in the laboratory.&#x0D; Finally, with all the information described, an architectural design is carried out taking into account bioclimatic aspects, ways of harnessing solar energy, distribution of the room and a viable construction cost for the economic reality of the region. The developed architectural model is optimized in an energy simulation program (Siter Vs. 1.2), achieving energy savings of 65%. The software used is owned property of the research institute&#x0D; The house has been monitored with temperature sensors for about a year and six months, to date it shows good results, where the correct functioning of the elements and constructive forms is evidenced, reflecting a temperature inside the house of around 10 °C above room temperature 24 hours a day.

Monitoring Arithmetic Temporal Properties on Finite Traces

We study monitoring of linear-time arithmetic properties against finite traces generated by an unknown dynamic system. The monitoring state is determined by considering at once the trace prefix seen so far, and all its possible finite-length, future continuations. This makes monitoring at least as hard as satisfiability and validity. Traces consist of finite sequences of assignments of a fixed set of variables to numerical values. Properties are specified in a logic we call ALTLf, combining LTLf (LTL on finite traces) with linear arithmetic constraints that may carry lookahead, i.e., variables may be compared over multiple instants of the trace. While the monitoring problem for this setting is undecidable in general, we show decidability for (a) properties without lookahead, and (b) properties with lookahead that satisfy the abstract, semantic condition of finite summary, studied before in the context of model checking. We then single out concrete, practically relevant classes of constraints guaranteeing finite summary. Feasibility is witnessed by a prototype implementation.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Scalable and Efficient Full-Graph GNN Training for Large Graphs

Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural information from graph-structured data, achieving state-of-the-art performance on applications such as recommendation, knowledge graph, and search. Graphs in these domains typically contain hundreds of millions of nodes and billions of edges. However, previous GNN systems demonstrate poor scalability because large and interleaved computation dependencies in GNN training cause significant overhead in current parallelization methods. We present G3, a distributed system that can efficiently train GNNs over billion-edge graphs at scale. G3 introduces GNN hybrid parallelism which synthesizes three dimensions of parallelism to scale out GNN training by sharing intermediate results peer-to-peer in fine granularity, eliminating layer-wise barriers for global collective communication or neighbor replications as seen in prior works. G3 leverages locality-aware iterative partitioning and multi-level pipeline scheduling to exploit acceleration opportunities by distributing balanced workload among workers and overlapping computation with communication in both inter-layer and intra-layer training processes. We show via a prototype implementation and comprehensive experiments that G3 can achieve as much as 2.24x speedup in a 16-node cluster, and better final accuracy over prior works.