Pub Date : 2014-12-01DOI: 10.1109/INTELES.2014.7008982
Tatiana Djaba Nya, S. Stilkerich, Christian Siemers
The growing complexity and size of computing systems as well as the unpredictability about changes in their deployment environment make their design increasingly challenging; especially for safety critical systems. Specifically the recognition of a fault within a system might be not only time consuming but also difficult in terms of reliability and completeness. This paper presents an approach to fault tolerance based on statistical features using the concepts of self-awareness and self-expression. These features characterize the behaviour of components, they are weighted and can be compared to measured values during runtime to characterize the well-behaviour of the system. Simulations show that this approach, used with the self-awareness and self-expression system layers, combines failure recognition and recovery with effective system design.
{"title":"Self-aware and self-expressive driven fault tolerance for embedded systems","authors":"Tatiana Djaba Nya, S. Stilkerich, Christian Siemers","doi":"10.1109/INTELES.2014.7008982","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008982","url":null,"abstract":"The growing complexity and size of computing systems as well as the unpredictability about changes in their deployment environment make their design increasingly challenging; especially for safety critical systems. Specifically the recognition of a fault within a system might be not only time consuming but also difficult in terms of reliability and completeness. This paper presents an approach to fault tolerance based on statistical features using the concepts of self-awareness and self-expression. These features characterize the behaviour of components, they are weighted and can be compared to measured values during runtime to characterize the well-behaviour of the system. Simulations show that this approach, used with the self-awareness and self-expression system layers, combines failure recognition and recovery with effective system design.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127596525","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008980
András Kokuti, E. Gelenbe
We present a novel direction based shortest path search algorithm to guide evacuees during an emergency. It uses opportunistic communications (oppcomms) with low-cost wearable mobile nodes that can exchange packets at close range of a few to some tens of meters without help of an infrastructure. The algorithm seeks the shortest path to exits which are safest with regard to a hazard, and is integrated into an autonomous Emergency Support System (ESS) to guide evacuees in a built environment. The ESS that we propose, that includes the directional algorithm and the Oppcomms, are evaluated using simulation experiments with the DBES (Distributed Building Evacuation Simulator) tool by simulating a shopping centre where fire is spreading. The results show that the directional path finding algorithm can offer significant improvements for the evacuees. In particular, we see that the improved and more reliable communications offered by Oppcomms, especially when the number of evacuees is larger, can help to compensate for the effects of congestion and improve the overall success of the evacuation scheme. Throughout the simulations we observe improvements of a few percent, which can translate into a valuable number of more people that are safely evacuated when human lives and safety are at risk.
{"title":"Directional enhancements for emergency navigation","authors":"András Kokuti, E. Gelenbe","doi":"10.1109/INTELES.2014.7008980","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008980","url":null,"abstract":"We present a novel direction based shortest path search algorithm to guide evacuees during an emergency. It uses opportunistic communications (oppcomms) with low-cost wearable mobile nodes that can exchange packets at close range of a few to some tens of meters without help of an infrastructure. The algorithm seeks the shortest path to exits which are safest with regard to a hazard, and is integrated into an autonomous Emergency Support System (ESS) to guide evacuees in a built environment. The ESS that we propose, that includes the directional algorithm and the Oppcomms, are evaluated using simulation experiments with the DBES (Distributed Building Evacuation Simulator) tool by simulating a shopping centre where fire is spreading. The results show that the directional path finding algorithm can offer significant improvements for the evacuees. In particular, we see that the improved and more reliable communications offered by Oppcomms, especially when the number of evacuees is larger, can help to compensate for the effects of congestion and improve the overall success of the evacuation scheme. Throughout the simulations we observe improvements of a few percent, which can translate into a valuable number of more people that are safely evacuated when human lives and safety are at risk.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114934448","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008985
G. Boracchi, Diego Carrera, B. Wohlberg
We address the problem of automatically detecting anomalies in images, i.e., patterns that do not conform to those appearing in a reference training set. This is a very important feature for enabling an intelligent system to autonomously check the validity of acquired data, thus performing a preliminary, automatic, diagnosis. We approach this problem in a patch-wise manner, by learning a model to represent patches belonging to a training set of normal images. Here, we consider a model based on sparse representations, and we show that jointly monitoring the sparsity and the reconstruction error of such representation substantially improves the detection performance with respect to other approaches leveraging sparse models. As an illustrative application, we consider the detection of anomalies in scanning electron microscope (SEM) images, which is essential for supervising the production of nanofibrous materials.
{"title":"Novelty detection in images by sparse representations","authors":"G. Boracchi, Diego Carrera, B. Wohlberg","doi":"10.1109/INTELES.2014.7008985","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008985","url":null,"abstract":"We address the problem of automatically detecting anomalies in images, i.e., patterns that do not conform to those appearing in a reference training set. This is a very important feature for enabling an intelligent system to autonomously check the validity of acquired data, thus performing a preliminary, automatic, diagnosis. We approach this problem in a patch-wise manner, by learning a model to represent patches belonging to a training set of normal images. Here, we consider a model based on sparse representations, and we show that jointly monitoring the sparsity and the reconstruction error of such representation substantially improves the detection performance with respect to other approaches leveraging sparse models. As an illustrative application, we consider the detection of anomalies in scanning electron microscope (SEM) images, which is essential for supervising the production of nanofibrous materials.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130047777","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008978
Michal Prauzek, P. Musílek, A. G. Watts
Wireless sensors are sophisticated embedded systems designed for collecting data on systems or processes of interest. In many cases, they are expected to operate in inaccessible locations, without user supervision. As a result, such monitoring systems need to operate autonomously and independently of external sources of energy. To achieve long-lived sustainability, monitoring systems often rely on energy extracted from the environment, e.g. through solar harvesting. Their design is a challenging problem with several conflicting goals and a number of design and implementation possibilities. For obvious reasons, these devices must be designed in an energy efficient way. As a result, they usually have low computational performance and cannot implement complicated control algorithms. At the same time, due to the requirements for autonomy and dependability, they must be endowed with certain degree of adaptability and fault tolerance - properties typically found in intelligent systems. In this contribution, we describe the design flow of an intelligent embedded control system for management of energy use in wireless monitoring systems. The paper also provides a simulation-based analysis of the control system performance.
{"title":"Fuzzy algorithm for intelligent wireless sensors with solar harvesting","authors":"Michal Prauzek, P. Musílek, A. G. Watts","doi":"10.1109/INTELES.2014.7008978","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008978","url":null,"abstract":"Wireless sensors are sophisticated embedded systems designed for collecting data on systems or processes of interest. In many cases, they are expected to operate in inaccessible locations, without user supervision. As a result, such monitoring systems need to operate autonomously and independently of external sources of energy. To achieve long-lived sustainability, monitoring systems often rely on energy extracted from the environment, e.g. through solar harvesting. Their design is a challenging problem with several conflicting goals and a number of design and implementation possibilities. For obvious reasons, these devices must be designed in an energy efficient way. As a result, they usually have low computational performance and cannot implement complicated control algorithms. At the same time, due to the requirements for autonomy and dependability, they must be endowed with certain degree of adaptability and fault tolerance - properties typically found in intelligent systems. In this contribution, we describe the design flow of an intelligent embedded control system for management of energy use in wireless monitoring systems. The paper also provides a simulation-based analysis of the control system performance.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116362427","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008983
C. Alippi, M. Roveri, F. Trovò
Exploiting spatial and temporal relationships in acquired datastreams is a primary ability of Cognitive Fault Detection and Diagnosis Systems (FDDSs) for sensor networks. In fact, this novel generation of FDDSs relies on the ability to correctly characterize the existing relationships among acquired datastreams to provide prompt detections of faults (while reducing false positives) and guarantee an effective isolation/identification of the sensor affected by the fault (once discriminated from a change in the environment or a model bias). The paper suggests a novel framework to automatically learn temporal and spatial relationships existing among streams of data to detect and diagnose faults. The suggested learning framework is based on a theoretically grounded hypothesis test, able to capture the Granger causal dependency existing among datastreams. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed solution for fault detection.
{"title":"Learning causal dependencies to detect and diagnose faults in sensor networks","authors":"C. Alippi, M. Roveri, F. Trovò","doi":"10.1109/INTELES.2014.7008983","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008983","url":null,"abstract":"Exploiting spatial and temporal relationships in acquired datastreams is a primary ability of Cognitive Fault Detection and Diagnosis Systems (FDDSs) for sensor networks. In fact, this novel generation of FDDSs relies on the ability to correctly characterize the existing relationships among acquired datastreams to provide prompt detections of faults (while reducing false positives) and guarantee an effective isolation/identification of the sensor affected by the fault (once discriminated from a change in the environment or a model bias). The paper suggests a novel framework to automatically learn temporal and spatial relationships existing among streams of data to detect and diagnose faults. The suggested learning framework is based on a theoretically grounded hypothesis test, able to capture the Granger causal dependency existing among datastreams. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed solution for fault detection.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134017222","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008979
P. Musílek, P. Krömer, Michal Prauzek
Environmental sensing is necessary for air quality monitoring, assessment of ecosystem health, or climate change tracking. Environmental monitoring systems can take a form of standalone monitoring stations or networks of individual sensor nodes with wireless connectivity. The latter approach allows high resolution mapping of spatiotemporal characteristics of the environment. To allow their autonomous operation and to minimize their maintenance costs, such systems are often powered using energy harvested from the environment itself. Due to the scarcity and intermittency of the environmental energy, operation of energy harvesting monitoring systems is not a trivial task. Their sensing, transmitting, and housekeeping activities must be carefully managed to extend their lifetime while providing desired quality of service. As the environmental conditions change with the region of deployment, the strategies for energy management must change accordingly to match the energy availability. In this work, we examine how geographic location affects the operations and quality of data collected by a solar-powered monitoring system. In particular, we use node/network simulation tools to follow the performance of energy-harvesting environmental monitoring sensor nodes at different latitudes, from equator to the pole. Static parameters of the simulated sensor nodes are determined for each latitude using an intelligent optimization method. The results show a clear dependence of the monitoring system performance on its deployment location. This encourages location-specific optimization of sensor node properties and parameters.
{"title":"Location-specific optimization of energy harvesting environmental monitoring systems","authors":"P. Musílek, P. Krömer, Michal Prauzek","doi":"10.1109/INTELES.2014.7008979","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008979","url":null,"abstract":"Environmental sensing is necessary for air quality monitoring, assessment of ecosystem health, or climate change tracking. Environmental monitoring systems can take a form of standalone monitoring stations or networks of individual sensor nodes with wireless connectivity. The latter approach allows high resolution mapping of spatiotemporal characteristics of the environment. To allow their autonomous operation and to minimize their maintenance costs, such systems are often powered using energy harvested from the environment itself. Due to the scarcity and intermittency of the environmental energy, operation of energy harvesting monitoring systems is not a trivial task. Their sensing, transmitting, and housekeeping activities must be carefully managed to extend their lifetime while providing desired quality of service. As the environmental conditions change with the region of deployment, the strategies for energy management must change accordingly to match the energy availability. In this work, we examine how geographic location affects the operations and quality of data collected by a solar-powered monitoring system. In particular, we use node/network simulation tools to follow the performance of energy-harvesting environmental monitoring sensor nodes at different latitudes, from equator to the pole. Static parameters of the simulated sensor nodes are determined for each latitude using an intelligent optimization method. The results show a clear dependence of the monitoring system performance on its deployment location. This encourages location-specific optimization of sensor node properties and parameters.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124410963","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008988
G. Rigatos
A solution to the problem of control of nonlinear chaotic dynamical systems, is proposed with the use of differential flatness theory and of adaptive fuzzy control theory. Considering that the dynamical model of chaotic systems is unknown, an adaptive fuzzy controller is designed. By applying differential flatness theory the chaotic system's model is written in a linear form, and the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs of the transformed dynamical model are approximated with the use of neuro-fuzzy networks. It is proven that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, with the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. Simulation experiments confirm the efficiency of the proposed adaptive fuzzy control method, using as a case study the model of the Lorenz chaotic oscillator.
{"title":"A differential flatness theory approach to adaptive fuzzy control of chaotic dynamical systems","authors":"G. Rigatos","doi":"10.1109/INTELES.2014.7008988","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008988","url":null,"abstract":"A solution to the problem of control of nonlinear chaotic dynamical systems, is proposed with the use of differential flatness theory and of adaptive fuzzy control theory. Considering that the dynamical model of chaotic systems is unknown, an adaptive fuzzy controller is designed. By applying differential flatness theory the chaotic system's model is written in a linear form, and the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs of the transformed dynamical model are approximated with the use of neuro-fuzzy networks. It is proven that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, with the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. Simulation experiments confirm the efficiency of the proposed adaptive fuzzy control method, using as a case study the model of the Lorenz chaotic oscillator.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918704","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008981
Jongwoon Yoo, Taemin Kim, C. Provencher, T. Fong
This paper explores the possibility of using WiFi localization techniques for autonomous free-flying robots on the International Space Station (ISS). We have collected signal strength samples from the ISS, built the WiFi map using Gaussian processes, implemented a localizer based on particle filters, and evaluated the performance. Our results show the average error of 1.59 meters, which is accurate enough to identify which ISS module the robot is currently in. However, we found that most errors occurred in some specific modules under the current WiFi settings. This paper describes the challenges of applying WiFi localization techniques to the ISS and suggests several approaches to achieve better performance.
{"title":"WiFi localization on the International Space Station","authors":"Jongwoon Yoo, Taemin Kim, C. Provencher, T. Fong","doi":"10.1109/INTELES.2014.7008981","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008981","url":null,"abstract":"This paper explores the possibility of using WiFi localization techniques for autonomous free-flying robots on the International Space Station (ISS). We have collected signal strength samples from the ISS, built the WiFi map using Gaussian processes, implemented a localizer based on particle filters, and evaluated the performance. Our results show the average error of 1.59 meters, which is accurate enough to identify which ISS module the robot is currently in. However, we found that most errors occurred in some specific modules under the current WiFi settings. This paper describes the challenges of applying WiFi localization techniques to the ISS and suggests several approaches to achieve better performance.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432163","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008987
Raul Finker, I. D. Campo, J. Echanobe, M. V. Martínez
Extreme learning machine (ELM) is an emerging approach that has attracted the attention of the research community because it outperforms conventional back-propagation feed-forward neural networks and support vector machines (SVM) in some aspects. ELM provides a robust learning algorithm, free of local minima, suitable for high speed computation, and less dependant on human intervention than the above methods. ELM is appropriate for the implementation of intelligent embedded systems with real-time learning capability. Moreover, a number of cutting-edge applications demanding a high performance solution could benefit from this approach. In this work, a scalable hardware/software architecture for ELM is presented, and the details of its implementation on a field programmable gate array (FPGA) are analyzed. The proposed solution provides high speed, small size, low power consumption, autonomy, and true capability for real-time adaptation (i.e. the learning stage is performed on-chip). The developed system is able to deal with highly demanding multiclass classification problems. Two real-world applications are presented, a benchmark problem of the Landsat images database, and a novel driver identification system for smart car applications. Experimental results that validate the proposal are provided.
{"title":"An intelligent embedded system for real-time adaptive extreme learning machine","authors":"Raul Finker, I. D. Campo, J. Echanobe, M. V. Martínez","doi":"10.1109/INTELES.2014.7008987","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008987","url":null,"abstract":"Extreme learning machine (ELM) is an emerging approach that has attracted the attention of the research community because it outperforms conventional back-propagation feed-forward neural networks and support vector machines (SVM) in some aspects. ELM provides a robust learning algorithm, free of local minima, suitable for high speed computation, and less dependant on human intervention than the above methods. ELM is appropriate for the implementation of intelligent embedded systems with real-time learning capability. Moreover, a number of cutting-edge applications demanding a high performance solution could benefit from this approach. In this work, a scalable hardware/software architecture for ELM is presented, and the details of its implementation on a field programmable gate array (FPGA) are analyzed. The proposed solution provides high speed, small size, low power consumption, autonomy, and true capability for real-time adaptation (i.e. the learning stage is performed on-chip). The developed system is able to deal with highly demanding multiclass classification problems. Two real-world applications are presented, a benchmark problem of the Landsat images database, and a novel driver identification system for smart car applications. Experimental results that validate the proposal are provided.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134344840","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 : 2014-12-01DOI: 10.1109/INTELES.2014.7008984
Rene Romann, R. Salomon
Intelligent embedded systems become more and more widespread. Especially in the field of smart environments, such as smart homes, the systems are communicating with each other. If wireless communication is used, security becomes important. This paper explores to what extent salted hashes might be used on tiny embedded systems to provide message authentication. To this end, this paper uses two very different microcontrollers for calculating salted hases using SHA-1 and SHA-256. The execution times vary between 2.5 and 160 milliseconds, which is fast enough to provide user responses in time.
{"title":"Salted hashes for message authentication - proof of concept on tiny embedded systems","authors":"Rene Romann, R. Salomon","doi":"10.1109/INTELES.2014.7008984","DOIUrl":"https://doi.org/10.1109/INTELES.2014.7008984","url":null,"abstract":"Intelligent embedded systems become more and more widespread. Especially in the field of smart environments, such as smart homes, the systems are communicating with each other. If wireless communication is used, security becomes important. This paper explores to what extent salted hashes might be used on tiny embedded systems to provide message authentication. To this end, this paper uses two very different microcontrollers for calculating salted hases using SHA-1 and SHA-256. The execution times vary between 2.5 and 160 milliseconds, which is fast enough to provide user responses in time.","PeriodicalId":345619,"journal":{"name":"2014 IEEE Symposium on Intelligent Embedded Systems (IES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279340","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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