Definition and Empirical Evaluation of Voters for Redundant Smart Sensor Systems Definición y Evaluación Empírica de Algoritmos de Voteo para Sistemas Redundantes de Sensado Inteligente

Abstract This study is the first attempt for integration voting algorithms with fault diagnosis devices. Voting algorithms are used to arbitrate between the results of redundant modules in fault-tolerant systems. Smart sensors are used for FDI (Fault Detection and Isolation) purposes by means of their built in intelligence. Integration of fault masking and FDI strategies is necessary in the construction of ultra-available/safe systems with on-line fault detection capability. This article introduces a range of novel software voting algorithms which adjudicate among the results of redundant smart sensors in a Triple Modular Redundant (TMR) system. Techniques to integrate replicated smart sensors and fault masking approach are discussed, and a classification of hybrid voters is provided based on result and confidence values, which affect the metrics of availability and safety.Thus, voters are classified into four groups: Independent-diagnostic safety-optimised voters, Integrated-diagnostic safety-optimised voters, Independent-diagnostic availability-optimised voters and Integrated-diagnostic availability-optimised voters. The properties of each category are explained and sample versions of each class as well as their possible application areas are discussed. Keywords: Ultra-Available System, Smart Sensor, Fault Masking, Triple Modular Redundancy. Resumen Este estudio es una primer aproximación para la integración de algoritmos de voteo con dispositivos de diagnóstico de fallas. Los algoritmos de voteo son usados para arbitrar entre los resultados de elementos redundantes en sistemas tolerantes a fallas. Los sensores inteligentes son usados para propositos de detección y separación de fallas (FDI) dada la capacidad su capacidad de inteligencia construida. La integración de enmascaramiento de fallas y las estrategias de FDI is necesaria en la construcción de sistemas altamente disponibles y seguros con la capacidad de detección de fallas en línea. Este artículo introduce un rango de algoritmos de voteo los cuales adjudican un resultado entre los resultados generados por los sensores inteligentes en un módulo de redundancia triple. Las técnicas para integrar los sensores inteligentes replicados y la aproximación de enmascaramiento de fallas son revisadas en este artículo. Una clasificación de algoritmos de voteo híbrido es provista con base en el resultado y los valores de confianza los cuales afectan las métricas de disponibilidad y seguridad de estos algoritmos. De hecho los algoritmos de voteo son clasificados en cuatro grupos: Diagnóstico-Independiente con seguridad-optimizada, Diagnóstico-Integrado con seguridad-optimizada, Diagnóstico-Independiente con disponibilidad-opitimizada y Diagnóstico-Integrado con disponibilidad-optimizada. Las propiedades de cada categoria son revisadas asi como muestras de sus implementaciones son discutidas

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

State of Alaska Election Security Project Phase 2 Report

A laska’s election system is among the most secure in the country, and it has a number of safeguards other states are now adopting. But the technology Alaska uses to record and count votes could be improved— and the state’s huge size, limited road system, and scattered communities also create special challenges for insuring the integrity of the vote. In this second phase of an ongoing study of Alaska’s election security, we recommend ways of strengthening the system—not only the technology but also the election procedures. The lieutenant governor and the Division of Elections asked the University of Alaska Anchorage to do this evaluation, which began in September 2007.Lieutenant Governor Sean Parnell. State of Alaska Division of Elections.List of Appendices / Glossary / Study Team / Acknowledgments / Introduction / Summary of Recommendations / Part 1 Defense in Depth / Part 2 Fortification of Systems / Part 3 Confidence in Outcomes / Conclusions / Proposed Statement of Work for Phase 3: Implementation / Reference

DONS: Dynamic Optimized Neighbor Selection for smart blockchain networks

Blockchain (BC) systems mainly depend on the consistent state of the Distributed Ledger (DL) at different logical and physical places of the network. The majority of network nodes need to be enforced to use one or both of the following approaches to remain consistent: (i) to wait for certain delays (i.e. by requesting a hard puzzle solution as in PoW and PoUW, or to wait for random delays as in PoET, etc.) (ii) to propagate shared data through shortest possible paths within the network. The first approach may cause higher energy consumption and/or lower throughput rates if not optimized, and in many cases these features are conventionally fixed. Therefore, it is preferred to enhance the second approach with some optimization. Previous works for this approach have the following drawbacks: they may violate the identity privacy of miners, only locally optimize the Neighbor Selection method (NS), do not consider the dynamicity of the network, or require the nodes to know the precise size of the network at all times. In this paper, we address these issues by proposing a Dynamic and Optimized NS protocol called DONS, using a novel privacy-aware leader election within the public BC called AnoLE, where the leader anonymously solves the The Minimum Spanning Tree problem (MST) of the network in polynomial time. Consequently, miners are informed about the optimum NS according to the current state of network topology. We analytically evaluate the complexity, the security and the privacy of the proposed protocols against state-of-the-art MST solutions for DLs and well known attacks. Additionally, we experimentally show that the proposed protocols outperform state-of-the-art NS solutions for public BCs. Our evaluation shows that the proposed DONS and AnoLE protocols are secure, private, and they acutely outperform all current NS solutions in terms of block finality and fidelity. © 2021 The Author(s

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART\u27s availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to five nines (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability

SECURITY RESEARCH FOR BLOCKCHAIN IN SMART GRID

Smart grid is a power supply system that uses digital communication technology to detect and react to local changes for power demand. Modern and future power supply system requires a distributed system for effective communication and management. Blockchain, a distributed technology, has been applied in many fields, e.g., cryptocurrency exchange, secure sharing of medical data, and personal identity security. Much research has been done on the application of blockchain to smart grid. While blockchain has many advantages, such as security and no interference from third parties, it also has inherent disadvantages, such as untrusted network environment, lacking data source privacy, and low network throughput.In this research, three systems are designed to tackle some of these problems in blockchain technology. In the first study, Information-Centric Blockchain Model, we focus on data privacy. In this model, the transactions created by nodes in the network are categorized into separate groups, such as billing transactions, power generation transactions, etc. In this model, all transactions are first encrypted by the corresponding pairs of asymmetric keys, which guarantees that only the intended receivers can see the data so that data confidentiality is preserved. Secondly, all transactions are sent on behalf of their groups, which hides the data sources to preserve the privacy. Our preliminary implementation verified the feasibility of the model, and our analysis demonstrates its effectiveness in securing data source privacy, increasing network throughput, and reducing storage usage. In the second study, we focus on increasing the network’s trustworthiness in an untrusted network environment. A reputation system is designed to evaluate all node’s behaviors. The reputation of a node is evaluated on its computing power, online time, defense ability, function, and service quality. The performance of a node will affect its reputation scores, and a node’s reputation scores will be used to assess its qualification, privileges, and job assignments. Our design is a relatively thorough, self-operated, and closed-loop system. Continuing evaluation of all node’s abilities and behaviors guarantees that only nodes with good scores are qualified to handle certain tasks. Thus, the reputation system helps enhance network security by preventing both internal and external attacks. Preliminary implementation and security analysis showed that the reputation model is feasible and enhances blockchain system’s security. In the third research, a countermeasure was designed for double spending. Double spending is one of the two most concerned security attacks in blockchain. In this study, one of the most reputable nodes was selected as detection node, which keeps checking for conflict transactions in two consecutive blocks. Upon a problematic transaction was discovered, two punishment transactions were created to punish the current attack behavior and to prevent it to happen in future. The experiment shows our design can detect the double spending effectively while using much less detection time and resources

Smart Sensing: Selection, Prediction and Monitoring

A sensor is a device which is used to detect physical parameters of interest like temperature, pressure, or strain, performing the so called sensing process. This kind of device has been widely adopted in different fields such as aeronautics, automotive, security, logistics, health-care and more. The essential difference between a smart sensor and a standard sensor is its intelligence capability: smart sensors are able to capture and elaborate data from the environment while communicating and interacting with other systems in order to make predictions and find intelligent solutions based on the application needs. The first part of this thesis is focused on the problem of sensor selection in the context of virtual sensing of temperature in indoor environments, a topic of paramount importance which allows to increase the accuracy of the predictive models employed in the following phases by providing more informative data. In particular, virtual sensing refers to the process of estimating or predicting physical parameters without relying on physical sensors, using computational algorithms and predictive models to gather and analyze data for accurate predictions. We analyze the literature, propose and evaluate methodologies and solutions for sensor selection and placement based on machine learning techniques, including evolutionary algorithms. Thereafter, once determined which physical sensors to wield, the focus shifts to the actual methodology for virtual sensing strategies for the prediction of temperatures allowing to uniformly monitor uncovered or unreachable locations, reducing the sensors deployment costs and providing, at the same time, a fallback solution in case of sensor failures. For this purpose, we conduct a comprehensive assessment of different virtual sensing strategies including novel solutions proposed based on recurrent neural networks and graph neural networks able to effectively exploit spatio-temporal features. The methodologies considered so far are able to accurately complete the information coming from real physical sensors, allowing us to effectively carry out monitoring tasks such as anomaly or event detection. Therefore, the final part of this work looks at sensors from another, more formal, point of view. Specifically, it is devoted to the study and design of a framework aimed at pairing monitoring and machine learning techniques in order to detect, in a preemptive manner, critical behaviours of a system that could lead to a failure. This is done extracting interpretable properties, expressed in a given temporal logic formalism, from sensor data. The proposed framework is evaluated through an experimental assessment performed on benchmark datasets, and then compared to previous approaches from the literature