Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964853
J. Vasconcelos, George Lima
Hardware architectures equipped with multiple cores, cache memory and branch prediction usually preclude the application of classical methods for determining execution time bounds for real-time tasks. As such bounds are fundamental in the designing of real-time system, Measurement-Based Probabilistic Timing Analysis (MBPTA) has been employed. A common choice is towards the derivation of probabilistic Worst-Case Execution Time (pWCET) via the use of Extreme Value Theory (EVT), a branch of statistics for modeling the maximum of a random variable. However, pWCET estimations are usually reported taking a controlled or simulated environment. In this paper we rather apply MBPTA in a real multi-core platform, namely Raspberry Pi 3B, taking into consideration possible interference due to operating system and concurrent activities. The found results indicate that although EVT is a robust technique, it does not always produce adequate models and coherent pWCET estimations. As MBPTA is primarily called for when classical methods are not applicable, as it is the case for the studied platform, the results reported in this paper highlight possible risks in when applying MBPTA for pWCET estimations.
{"title":"Possible risks with EVT-based timing analysis: an experimental study on a multi-core platform","authors":"J. Vasconcelos, George Lima","doi":"10.1109/SBESC56799.2022.9964853","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964853","url":null,"abstract":"Hardware architectures equipped with multiple cores, cache memory and branch prediction usually preclude the application of classical methods for determining execution time bounds for real-time tasks. As such bounds are fundamental in the designing of real-time system, Measurement-Based Probabilistic Timing Analysis (MBPTA) has been employed. A common choice is towards the derivation of probabilistic Worst-Case Execution Time (pWCET) via the use of Extreme Value Theory (EVT), a branch of statistics for modeling the maximum of a random variable. However, pWCET estimations are usually reported taking a controlled or simulated environment. In this paper we rather apply MBPTA in a real multi-core platform, namely Raspberry Pi 3B, taking into consideration possible interference due to operating system and concurrent activities. The found results indicate that although EVT is a robust technique, it does not always produce adequate models and coherent pWCET estimations. As MBPTA is primarily called for when classical methods are not applicable, as it is the case for the studied platform, the results reported in this paper highlight possible risks in when applying MBPTA for pWCET estimations.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114962202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9965059
Leonardo Francis, Victor Elízio Pierozan, G. Gracioli, G. Araújo
In the automotive industry, the study of internal combustion engines (ICE) has massively been studied to identify the occurrence of some failures, such as engine knock [1], [2]. The occurrence of this phenomenon on the engine directly affects the engine maintenance cost and longer engine life. The use of machine learning for failure detection is highlighted [3]–[6]. An investigation was carried out by performing experiments with a Renault Sandero car, collecting some sets of variables for batch analysis. In this paper, we use artificial intelligence techniques with a data-driven approach, more specifically, machine learning, to detect the phenomenon of engine knock. The investigation was conducted with a feature extraction classifier, AutoEnconder Dense and Convolutional, SVM, and Isolated Forest. Finally, the best result achieved was 81% considering a feature extraction classifier on the collection of variables defined.
在汽车工业中，对内燃机(ICE)进行了大量的研究，以确定一些故障的发生，如发动机爆震[1]，[2]。这种现象在发动机上的发生直接影响到发动机的维修成本和更长的发动机寿命。机器学习在故障检测中的应用[3]-[6]突出显示。通过对雷诺桑德罗汽车进行实验，收集了一些变量集进行批量分析，进行了调查。在本文中，我们使用数据驱动方法的人工智能技术，更具体地说，机器学习，来检测发动机爆震现象。研究采用了特征提取分类器、autoencoder Dense and Convolutional、SVM和Isolated Forest。最后，考虑到在定义的变量集合上使用特征提取分类器，获得的最佳结果为81%。
{"title":"Data-driven Anomaly Detection of Engine Knock based on Automotive ECU","authors":"Leonardo Francis, Victor Elízio Pierozan, G. Gracioli, G. Araújo","doi":"10.1109/SBESC56799.2022.9965059","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9965059","url":null,"abstract":"In the automotive industry, the study of internal combustion engines (ICE) has massively been studied to identify the occurrence of some failures, such as engine knock [1], [2]. The occurrence of this phenomenon on the engine directly affects the engine maintenance cost and longer engine life. The use of machine learning for failure detection is highlighted [3]–[6]. An investigation was carried out by performing experiments with a Renault Sandero car, collecting some sets of variables for batch analysis. In this paper, we use artificial intelligence techniques with a data-driven approach, more specifically, machine learning, to detect the phenomenon of engine knock. The investigation was conducted with a feature extraction classifier, AutoEnconder Dense and Convolutional, SVM, and Isolated Forest. Finally, the best result achieved was 81% considering a feature extraction classifier on the collection of variables defined.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115085605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964784
Luiz Fernando Altran, Guilherme Galante, M. Oyamada
The goal of this paper is to present a scheduling strategy that addresses a set of customer-specific business requirements when deploying containers, targeting multi-user and multi-cloud environments. This is done by extending the label scheme used to specify attributes for compute nodes and requirements for applications, and by associating workloads to the nodes with the highest affinity. The proposal is validated by implementing a custom scheduler for Kubernetes orchestrator. The custom scheduler was validated in an environment consisting of 25 nodes distributed across 4 providers with different hardware configurations and geographical locations. The results confirm the effectiveness of our scheduler in different scenarios, granting the business requirements assigned to each application.
{"title":"Label-affinity-Scheduler: Considering Business Requirements in Container Scheduling for Multi-Cloud and Multi-Tenant Environments","authors":"Luiz Fernando Altran, Guilherme Galante, M. Oyamada","doi":"10.1109/SBESC56799.2022.9964784","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964784","url":null,"abstract":"The goal of this paper is to present a scheduling strategy that addresses a set of customer-specific business requirements when deploying containers, targeting multi-user and multi-cloud environments. This is done by extending the label scheme used to specify attributes for compute nodes and requirements for applications, and by associating workloads to the nodes with the highest affinity. The proposal is validated by implementing a custom scheduler for Kubernetes orchestrator. The custom scheduler was validated in an environment consisting of 25 nodes distributed across 4 providers with different hardware configurations and geographical locations. The results confirm the effectiveness of our scheduler in different scenarios, granting the business requirements assigned to each application.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122225923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9965085
Lavinia Miranda, M. Pereira, Jorgiano Vidal
Approximate Computing is currently an emerging paradigm that seeks to replace some data accuracy with aspects such as performance and energy efficiency. There are tools within this scope that apply some approximate computation techniques at software computational level. However, these tools are limited in a way that they only cover some specific scope, apply only one of the known techniques and/or need manual code annotations to work out. Thus, this work proposes the implementation of a tool that, according to the application profiling, chooses the most appropriate approximate computing technique to be applied. LLVM-ACT uses the LLVM compilation infrastructure, where each step is implemented as a code analysis or transformation LLVM Pass. The results show that the technique chosen by LLVM-ACT is cost-effective if low error rates and high speedup are taken into account, with an 8x speedup with 22% error rate on average with the Fluidanimate application.
{"title":"LLVM-ACT: Profiling Based Tool for Approximate Computing Technique Selection","authors":"Lavinia Miranda, M. Pereira, Jorgiano Vidal","doi":"10.1109/SBESC56799.2022.9965085","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9965085","url":null,"abstract":"Approximate Computing is currently an emerging paradigm that seeks to replace some data accuracy with aspects such as performance and energy efficiency. There are tools within this scope that apply some approximate computation techniques at software computational level. However, these tools are limited in a way that they only cover some specific scope, apply only one of the known techniques and/or need manual code annotations to work out. Thus, this work proposes the implementation of a tool that, according to the application profiling, chooses the most appropriate approximate computing technique to be applied. LLVM-ACT uses the LLVM compilation infrastructure, where each step is implemented as a code analysis or transformation LLVM Pass. The results show that the technique chosen by LLVM-ACT is cost-effective if low error rates and high speedup are taken into account, with an 8x speedup with 22% error rate on average with the Fluidanimate application.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126550207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964539
Rodrigo M. Sokulski, P. C. Santos, Sairo R. dos Santos, M. Alves
Larger vector extensions are one of the commonly used techniques to meet the growing demands from computational systems. These extensions, capable of operating over multiple data elements with a single instruction, exert a lot of pressure on the memory hierarchy, increasing the impact of growing problems such as Memory-Wall and von Neumann bottleneck. An alternative to work around these problems would be adding processing elements close to the memory, known as Processing-In-Memory (PIM). As with processor vector extensions, the most efficient PIM techniques use in-memory vector processing units. There are several ways to convert a code into in-memory vector processing, such as binary hardware translation, which may not depend on programmers or adapted software and can be carried out transparently to its users. However, in the context of in-memory processing, this conversion technique presents some challenges related to the PIM instructions format and the structure of the loops present in each application. Thus, this article proposes and evaluates Simple AVX to PIM Vectorizer (SAPIVe), a hardware binary translation mechanism from processor vector instructions into in-memory vector instructions, which, in addition to processing more data, also performs loads, operations, and stores at once. Our results show that our mechanism can accelerate kernels up to 5 times with possible performance losses prevented using loop predictors.
更大的向量扩展是满足计算系统日益增长的需求的常用技术之一。这些扩展能够用一条指令操作多个数据元素，对内存层次结构施加了很大的压力，增加了诸如内存墙和冯·诺伊曼瓶颈等日益严重的问题的影响。解决这些问题的另一种方法是在内存附近添加处理元素，称为内存中处理(PIM)。与处理器向量扩展一样，最有效的PIM技术使用内存中的向量处理单元。有几种方法可以将代码转换为内存中的矢量处理，例如二进制硬件转换，它可能不依赖于程序员或改编的软件，并且可以对其用户透明地执行。然而，在内存处理的上下文中，这种转换技术提出了一些与PIM指令格式和每个应用程序中存在的循环结构相关的挑战。因此，本文提出并评估了Simple AVX to PIM Vectorizer (SAPIVe)，这是一种从处理器矢量指令到内存中的矢量指令的硬件二进制转换机制，它除了处理更多数据外，还可以一次执行加载、操作和存储。我们的结果表明，我们的机制可以将内核加速多达5倍，并且使用循环预测器可以防止可能的性能损失。
{"title":"SAPIVe: Simple AVX to PIM Vectorizer","authors":"Rodrigo M. Sokulski, P. C. Santos, Sairo R. dos Santos, M. Alves","doi":"10.1109/SBESC56799.2022.9964539","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964539","url":null,"abstract":"Larger vector extensions are one of the commonly used techniques to meet the growing demands from computational systems. These extensions, capable of operating over multiple data elements with a single instruction, exert a lot of pressure on the memory hierarchy, increasing the impact of growing problems such as Memory-Wall and von Neumann bottleneck. An alternative to work around these problems would be adding processing elements close to the memory, known as Processing-In-Memory (PIM). As with processor vector extensions, the most efficient PIM techniques use in-memory vector processing units. There are several ways to convert a code into in-memory vector processing, such as binary hardware translation, which may not depend on programmers or adapted software and can be carried out transparently to its users. However, in the context of in-memory processing, this conversion technique presents some challenges related to the PIM instructions format and the structure of the loops present in each application. Thus, this article proposes and evaluates Simple AVX to PIM Vectorizer (SAPIVe), a hardware binary translation mechanism from processor vector instructions into in-memory vector instructions, which, in addition to processing more data, also performs loads, operations, and stores at once. Our results show that our mechanism can accelerate kernels up to 5 times with possible performance losses prevented using loop predictors.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128067480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964703
Mohammad Ibrahim Alkoudsi, G. Fohler, M. Völp
The Time-Triggered Architecture (TTA) presents a blueprint for building safe and real-time constrained distributed systems, based on a set of orthogonal concepts that make extensive use of the availability of a globally consistent notion of time and a priori knowledge of events. Although the TTA tolerates arbitrary failures of any of its nodes by architectural means (active node replication, a membership service, and bus guardians), the design of these means considers only accidental faults. However, distributed safety- and real-time critical systems have been emerging into more open and interconnected systems, operating autonomously for prolonged times and interfacing with other possibly non-real-time systems. Therefore, the existence of vulnerabilities that adversaries may exploit to compromise system safety cannot be ruled out. In this paper, we discuss potential targeted attacks capable of bypassing TTA's fault-tolerance mechanisms and demonstrate how two well-known recovery techniques - proactive and reactive rejuvenation - can be incorporated into TTA to reduce the window of vulnerability for attacks without introducing extensive and costly changes.
{"title":"Tolerating Resource Exhaustion Attacks in the Time-Triggered Architecture","authors":"Mohammad Ibrahim Alkoudsi, G. Fohler, M. Völp","doi":"10.1109/SBESC56799.2022.9964703","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964703","url":null,"abstract":"The Time-Triggered Architecture (TTA) presents a blueprint for building safe and real-time constrained distributed systems, based on a set of orthogonal concepts that make extensive use of the availability of a globally consistent notion of time and a priori knowledge of events. Although the TTA tolerates arbitrary failures of any of its nodes by architectural means (active node replication, a membership service, and bus guardians), the design of these means considers only accidental faults. However, distributed safety- and real-time critical systems have been emerging into more open and interconnected systems, operating autonomously for prolonged times and interfacing with other possibly non-real-time systems. Therefore, the existence of vulnerabilities that adversaries may exploit to compromise system safety cannot be ruled out. In this paper, we discuss potential targeted attacks capable of bypassing TTA's fault-tolerance mechanisms and demonstrate how two well-known recovery techniques - proactive and reactive rejuvenation - can be incorporated into TTA to reduce the window of vulnerability for attacks without introducing extensive and costly changes.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133341953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964869
Benedikt Jung, Christian Eichler, Jonas Röckl, R. Schlenk, Timo Hönig, Tilo Müller
As trusted computing becomes increasingly important, Trusted Execution Environments (TEEs) see more widespread use. A particular high demand for security arises in the context of embedded systems in critical infrastructures. We present a novel intrusion detection system called the Trusted Monitor (TM) that protects its integrity even in the presence of a system-level attacker by running inside the ARM TrustZone TEE. The TM constantly monitors the system using hardware performance counters and detects intrusions based on the classification by an application-specific machine learning model. Our evaluation shows that the TM correctly classifies 86% of 183 evaluated workloads, while the performance overhead stays below 2%. In particular, we show that a real-world kernel-level rootkit observably influences the hardware performance counters and, thus, can be detected.
{"title":"Trusted Monitor: TEE-Based System Monitoring","authors":"Benedikt Jung, Christian Eichler, Jonas Röckl, R. Schlenk, Timo Hönig, Tilo Müller","doi":"10.1109/SBESC56799.2022.9964869","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964869","url":null,"abstract":"As trusted computing becomes increasingly important, Trusted Execution Environments (TEEs) see more widespread use. A particular high demand for security arises in the context of embedded systems in critical infrastructures. We present a novel intrusion detection system called the Trusted Monitor (TM) that protects its integrity even in the presence of a system-level attacker by running inside the ARM TrustZone TEE. The TM constantly monitors the system using hardware performance counters and detects intrusions based on the classification by an application-specific machine learning model. Our evaluation shows that the TM correctly classifies 86% of 183 evaluated workloads, while the performance overhead stays below 2%. In particular, we show that a real-world kernel-level rootkit observably influences the hardware performance counters and, thus, can be detected.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114495041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964629
Benedict Herzog, S. Reif, Fabian Hügel, Wolfgang Schröder-Preikschat, Timo Hönig
Energy efficiency has developed to one of the most important non-functional system properties. One keystone to building an energy-efficient system is the right system configuration, which is tailored to the currently running application and hardware. Finding such a right system configuration manually, however, is a complex and often unfeasible task due to the vast configuration space on the one side and the required hardware and application knowledge on the other side. This paper presents and refines an approach to automatically identify and select energy-efficient configurations in re-configurable systems. The approach relies on different machine-learning techniques and achieves energy efficiency improvements of up to 10.8 % out of 13.3 % by automatically adapting the system configuration on a Linux system. Additionally, we analyse the application knowledge required for selecting the configuration and make a proposal how to generate sufficient training data.
{"title":"Bears: Building Energy-Aware Reconfigurable Systems","authors":"Benedict Herzog, S. Reif, Fabian Hügel, Wolfgang Schröder-Preikschat, Timo Hönig","doi":"10.1109/SBESC56799.2022.9964629","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964629","url":null,"abstract":"Energy efficiency has developed to one of the most important non-functional system properties. One keystone to building an energy-efficient system is the right system configuration, which is tailored to the currently running application and hardware. Finding such a right system configuration manually, however, is a complex and often unfeasible task due to the vast configuration space on the one side and the required hardware and application knowledge on the other side. This paper presents and refines an approach to automatically identify and select energy-efficient configurations in re-configurable systems. The approach relies on different machine-learning techniques and achieves energy efficiency improvements of up to 10.8 % out of 13.3 % by automatically adapting the system configuration on a Linux system. Additionally, we analyse the application knowledge required for selecting the configuration and make a proposal how to generate sufficient training data.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130084579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964983
André F. Pastório, F. Spanhol, L. Martins, E. T. Camargo
Low-cost particulate matter (LC-PM) sensors have been studied around the world as a viable alternative to expensive reference stations for monitoring air quality. However, LC-PM sensors require periodic calibration, since their data are often inaccurate and subject to uncertainty. Sensors calibration can be performed through machine learning methods where the sensor is placed in a real environment subject to the local environmental conditions of the place and its measurement compared to a reference equipment. This work evaluates different machine learning methods in five different models of LC-PM sensors, aiming to select the most appropriate sensor and a calibration method to be used in a low-cost air quality station in the context of smart cities.
{"title":"A Machine Learning-Based Approach to Calibrate Low-Cost Particulate Matter Sensors","authors":"André F. Pastório, F. Spanhol, L. Martins, E. T. Camargo","doi":"10.1109/SBESC56799.2022.9964983","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964983","url":null,"abstract":"Low-cost particulate matter (LC-PM) sensors have been studied around the world as a viable alternative to expensive reference stations for monitoring air quality. However, LC-PM sensors require periodic calibration, since their data are often inaccurate and subject to uncertainty. Sensors calibration can be performed through machine learning methods where the sensor is placed in a real environment subject to the local environmental conditions of the place and its measurement compared to a reference equipment. This work evaluates different machine learning methods in five different models of LC-PM sensors, aiming to select the most appropriate sensor and a calibration method to be used in a low-cost air quality station in the context of smart cities.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127684883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1109/SBESC56799.2022.9964952
E. Broering, L. Becker
Runtime Verification (RV) is a lightweight and dynamic technique that checks the current execution of the system through structures called monitors and produces a verdict on whether or not this execution satisfies a certain property of the system correction. Pedro et.al. developed a framework for performing RV of bare-metal real-time embedded systems (RTS). It consists of a tool for automatic synthesizing C++11 targeted monitors and a runtime library to support docking monitors on bare metal boards. The framework is capable of handling explicit time and durations, two of the essential concepts for anomaly detection of hard real-time systems. A limitation of this tool is that it only follows the POSIX standard. However, many real-time bare-metal embedded system applications do not support this architecture, such as those using FreeRTOS. This work aims to complement such work. Its goal is to update the RV process, making an adaptation for using it in non-POSIX RTOS, such as the FreeRTOS. The paper details the proposed system, also presenting a study to analize the scheduling feasibility of a real-time task set.
{"title":"Applying Runtime Verification in Real-Time Systems with FreeRTOS","authors":"E. Broering, L. Becker","doi":"10.1109/SBESC56799.2022.9964952","DOIUrl":"https://doi.org/10.1109/SBESC56799.2022.9964952","url":null,"abstract":"Runtime Verification (RV) is a lightweight and dynamic technique that checks the current execution of the system through structures called monitors and produces a verdict on whether or not this execution satisfies a certain property of the system correction. Pedro et.al. developed a framework for performing RV of bare-metal real-time embedded systems (RTS). It consists of a tool for automatic synthesizing C++11 targeted monitors and a runtime library to support docking monitors on bare metal boards. The framework is capable of handling explicit time and durations, two of the essential concepts for anomaly detection of hard real-time systems. A limitation of this tool is that it only follows the POSIX standard. However, many real-time bare-metal embedded system applications do not support this architecture, such as those using FreeRTOS. This work aims to complement such work. Its goal is to update the RV process, making an adaptation for using it in non-POSIX RTOS, such as the FreeRTOS. The paper details the proposed system, also presenting a study to analize the scheduling feasibility of a real-time task set.","PeriodicalId":130479,"journal":{"name":"2022 XII Brazilian Symposium on Computing Systems Engineering (SBESC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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