Mitigating smart card fault injection with link-time code rewriting: a feasibility study

We present a feasibility study to protect smart card software against fault-injection attacks by means of binary code rewriting. We implemented a range of protection techniques in a link-time rewriter and evaluate and discuss the obtained coverage, the associated overhead and engineering effort, as well as its practical usability

Обеспечение устойчивости к сбоям Smart-M3 приложения на уровне программной инфраструктуры

The Smart-M3 platform allows constructing software applications consisting of agents that interact by sharing information in a smart space. An important problem is dependability of the application in case of failures, which is a common place for existing networked environments. In this paper, we consider a generic software infrastructure for Smart-M3 applications and propose two solutions to support the application fault tolerance. Our first solution is introduction of a content service, which provides safety of volumetric data and their integrity due to delegation of storage functions to a separate element of the application infrastructure. The second solution is mechanisms for network connections recovery. For experimental case study, we use an existing Smart-M3 application — the SmartRoom system. Based on this case we show effectiveness of the proposed solutions.Платформа Smart-M3 позволяет создавать программные приложения как интеллектуальное пространство, в котором агенты, выполняемые на разнообразных устройствах вычислительной среды, взаимодействуют через совместное накопление и использование информации. Актуальной задачей является поддержка работоспособности приложения в условиях возникновения сбоев в сетевых вычислительных средах. В данной статье рассматривается понятие программной инфраструктуры для Smart-M3 приложения и предлагаются два решения для обеспечения его устойчивости к сбоям. Первое решение определяет сервис управления содержимым, который обеспечивает сохранность объемных данных и их целостность за счет делегирования функций хранения выделенному элементу инфраструктуры приложения. Второе решение состоит из механизмов восстановления сетевых соединений. Для экспериментального исследования используется существующее Smart-M3 приложение — система интеллектуального зала SmartRoom. На ее примере показана эффективность применения предлагаемых решений

IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications

The evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the problem of separate software stacks between the edge and the cloud with no unified fault-tolerant management, hindering dynamic relocation of data processing. In such systems, the data must also be preserved from being corrupted or duplicated in the case of intermittent long-distance network connectivity issues, malicious harming of edge devices, or other hostile environments. Within this context, the contributions of this paper are threefold: (i) to propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications by adapting our earlier Cloud and Edge Fault-Tolerant IoT (CEFIoT) layered design. We address the fault tolerance issue by employing the Apache Kafka publish/subscribe platform as the unified data replication solution. We also deploy Kubernetes for fault-tolerant management, combined with the federated scheme, offering a single management interface and allowing automatic reconfiguration of the data processing pipeline, (ii) to formulate functional and non-functional requirements of our proposed solution by comparing several IoT architectures, and (iii) to implement a smart buildings use case of the ongoing Otaniemi3D project as proof-of-concept for assessing IoTEF capabilities. The experimental results conclude that the architecture minimizes latency, saves network bandwidth, and handles both hardware and network connectivity based failures.Peer reviewe

The Merits of a Decentralized Pollution-Monitoring System Based on Distributed Ledger Technology

Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to manipulation. Moreover, the analysis of pollution data often lacks transparency to outsiders, which may lead to wrong decisions regarding environmental regulations. To address these challenges, we propose a software design for PMSs based on distributed ledger technology (DLT) and the long-range (LoRa) protocol for flexible, transparent, and energy-efficient environment monitoring and data management. To design the PMS, we conducted a comprehensive requirements analysis for PMSs. We benchmarked different consensus mechanisms (e.g., BFT-SMaRt and Raft) and digital signature schemes (e.g., ECDSA and EdDSA) to adequately design the PMS and fulfill the identified requirements

MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloud-native application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel microservice placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework providing a scalable, easy-to-configure control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition. The framework is evaluated using multiple use cases. The results demonstrate that MicroFog is a scalable, extensible and easy-to-configure framework that can integrate and evaluate novel placement policies for deploying microservice-based applications within multi-fog multi-cloud environments. We integrate multiple microservice placement policies to demonstrate MicroFog's ability to support horizontally scaled placement, thus reducing the application service response time up to 54%

Online disturbance prediction for enhanced availability in smart grids

A gradual move in the electric power industry towards Smart Grids brings new challenges to the system's efficiency and dependability. With a growing complexity and massive introduction of renewable generation, particularly at the distribution level, the number of faults and, consequently, disturbances (errors and failures) is expected to increase significantly. This threatens to compromise grid's availability as traditional, reactive management approaches may soon become insufficient. On the other hand, with grids' digitalization, real-time status data are becoming available. These data may be used to develop advanced management and control methods for a sustainable, more efficient and more dependable grid. A proactive management approach, based on the use of real-time data for predicting near-future disturbances and acting in their anticipation, has already been identified by the Smart Grid community as one of the main pillars of dependability of the future grid. The work presented in this dissertation focuses on predicting disturbances in Active Distributions Networks (ADNs) that are a part of the Smart Grid that evolves the most. These are distribution networks with high share of (renewable) distributed generation and with systems in place for real-time monitoring and control. Our main goal is to develop a methodology for proactive network management, in a sense of proactive mitigation of disturbances, and to design and implement a method for their prediction. We focus on predicting voltage sags as they are identified as one of the most frequent and severe disturbances in distribution networks. We address Smart Grid dependability in a holistic manner by considering its cyber and physical aspects. As a result, we identify Smart Grid dependability properties and develop a taxonomy of faults that contribute to better understanding of the overall dependability of the future grid. As the process of grid's digitization is still ongoing there is a general problem of a lack of data on the grid's status and especially disturbance-related data. These data are necessary to design an accurate disturbance predictor. To overcome this obstacle we introduce a concept of fault injection to simulation of power systems. We develop a framework to simulate a behavior of distribution networks in the presence of faults, and fluctuating generation and load that, alone or combined, may cause disturbances. With the framework we generate a large set of data that we use to develop and evaluate a voltage-sag disturbance predictor. To quantify how prediction and proactive mitigation of disturbances enhance availability we create an availability model of a proactive management. The model is generic and may be applied to evaluate the effect of proactive management on availability in other types of systems, and adapted for quantifying other types of properties as well. Also, we design a metric and a method for optimizing failure prediction to maximize availability with proactive approach. In our conclusion, the level of availability improvement with proactive approach is comparable to the one when using high-reliability and costly components. Following the results of the case study conducted for a 14-bus ADN, grid's availability may be improved by up to an order of magnitude if disturbances are managed proactively instead of reactively. The main results and contributions may be summarized as follows: (i) Taxonomy of faults in Smart Grid has been developed; (ii) Methodology and methods for proactive management of disturbances have been proposed; (iii) Model to quantify availability with proactive management has been developed; (iv) Simulation and fault-injection framework has been designed and implemented to generate disturbance-related data; (v) In the scope of a case study, a voltage-sag predictor, based on machine- learning classification algorithms, has been designed and the effect of proactive disturbance management on downtime and availability has been quantified

Review and Analysis of Failure Detection and Prevention Techniques in IT Infrastructure Monitoring

Maintaining the health of IT infrastructure components for improved reliability and availability is a research and innovation topic for many years. Identification and handling of failures are crucial and challenging due to the complexity of IT infrastructure. System logs are the primary source of information to diagnose and fix failures. In this work, we address three essential research dimensions about failures, such as the need for failure handling in IT infrastructure, understanding the contribution of system-generated log in failure detection and reactive & proactive approaches used to deal with failure situations. This study performs a comprehensive analysis of existing literature by considering three prominent aspects as log preprocessing, anomaly & failure detection, and failure prevention. With this coherent review, we (1) presume the need for IT infrastructure monitoring to avoid downtime, (2) examine the three types of approaches for anomaly and failure detection such as a rule-based, correlation method and classification, and (3) fabricate the recommendations for researchers on further research guidelines. As far as the authors\u27 knowledge, this is the first comprehensive literature review on IT infrastructure monitoring techniques. The review has been conducted with the help of meta-analysis and comparative study of machine learning and deep learning techniques. This work aims to outline significant research gaps in the area of IT infrastructure failure detection. This work will help future researchers understand the advantages and limitations of current methods and select an adequate approach to their problem

IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water.

[EN] The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods.FundingThis research was funded by Spanish Ministerio de Economia y Competitividad, Gobierno de Espana, grant number DPI2016-80303-C2-1-P.Campelo Rivadulla, JC.; Capella Hernández, JV.; Ors Carot, R.; Peris Tortajada, M.; Bonastre Pina, AM. (2022). IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water. Sensors. 22(3):1-16. https://doi.org/10.3390/s2203084211622