Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912761
C. Titouna, Farid Naït-Abdesselam
With the growing use of Unmanned Aerial Vehicles (UAVs) in military and civilian applications, cyber-attacks are increasing significantly. Therefore, detection of attacks becomes indispensable for such systems. In this paper, we focus on the detection of False Data Injection (FDI) attacks in Unmanned Aerial Systems (UASs). Considered to be the most performed attack, an attacker injects fake data into the system in order to disrupt the final decision. To combat this threat, our proposal is built on image analysis and classification. First, we resize the received image in order to adapt it to feed the classifier using the Nearest Neighbor Interpolation (NNI). Second, we train, validate, and test a Convolutional Neural Network (CNN) to perform the image classification. Finally, we compare each classification result classes to a neighborhood using Euclidean distance. Numerical results on the VisDrone dataset demonstrate the efficiency of our proposal under a set of metrics.
{"title":"A False Data Injection Attack Detection Approach Using Convolutional Neural Networks in Unmanned Aerial Systems","authors":"C. Titouna, Farid Naït-Abdesselam","doi":"10.1109/ISCC55528.2022.9912761","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912761","url":null,"abstract":"With the growing use of Unmanned Aerial Vehicles (UAVs) in military and civilian applications, cyber-attacks are increasing significantly. Therefore, detection of attacks becomes indispensable for such systems. In this paper, we focus on the detection of False Data Injection (FDI) attacks in Unmanned Aerial Systems (UASs). Considered to be the most performed attack, an attacker injects fake data into the system in order to disrupt the final decision. To combat this threat, our proposal is built on image analysis and classification. First, we resize the received image in order to adapt it to feed the classifier using the Nearest Neighbor Interpolation (NNI). Second, we train, validate, and test a Convolutional Neural Network (CNN) to perform the image classification. Finally, we compare each classification result classes to a neighborhood using Euclidean distance. Numerical results on the VisDrone dataset demonstrate the efficiency of our proposal under a set of metrics.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278820","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912818
J. Vargas, C. Thienot, X. Lagrange
Reducing mobile network and device energy consumption is one of the main objectives of the fifth and future sixth-generation (5G and 6G) cellular networks. This paper studies the Base Station (BS) and User Equipment (UE) energy consumption in scenarios where many users demand the same content and broadcast transmission might be used. We present analytical models to calculate the energy consumption in uni-cast, Multicast Broadcast Single Frequency Network (MBSFN), and Single-Cell Point-to-Multipoint (SC-PTM). Furthermore, we calculate the number of users per cell from which SC-PTM or MBSFN help to reduce BS and UE energy consumption compared to unicast.
{"title":"MBSFN and SC-PTM as Solutions to Reduce Energy Consumption in Cellular Networks","authors":"J. Vargas, C. Thienot, X. Lagrange","doi":"10.1109/ISCC55528.2022.9912818","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912818","url":null,"abstract":"Reducing mobile network and device energy consumption is one of the main objectives of the fifth and future sixth-generation (5G and 6G) cellular networks. This paper studies the Base Station (BS) and User Equipment (UE) energy consumption in scenarios where many users demand the same content and broadcast transmission might be used. We present analytical models to calculate the energy consumption in uni-cast, Multicast Broadcast Single Frequency Network (MBSFN), and Single-Cell Point-to-Multipoint (SC-PTM). Furthermore, we calculate the number of users per cell from which SC-PTM or MBSFN help to reduce BS and UE energy consumption compared to unicast.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128612922","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9913016
Ziwen An, Yanheng Liu, Geng Sun, Hongyang Pan, Aimin Wang
Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCN) are promising technologies in Internet of Things (IoTs). However, energy-constrained devices and connectivity in complex environments are two major challenges for IoTs. We consider a UAV-enabled WPCN scenario that a UAV can connect with the ground IoT devices (IoTDs). To connect and fly faster, UAV needs to be scheduled reasonably and the corresponding trajectory should be optimized. Thus, we formulate a UAV scheduling and trajectory optimization problem (USTOP) to minimize the total time so that improving the charging and transmission efficiency. Since conventional methods are difficult to solve USTOP, we propose an improved simulated annealing (ISA) with the variable size changing mechanism, the conflict resolution mechanism and the hybrid evolution method to solve it. Simulation results verify the effectiveness and performance of ISA under different scales of the network, and the stability of the proposed algorithm is verified.
{"title":"UAV-enabled Wireless Powered Communication Networks: A Joint Scheduling and Trajectory Optimization Approach","authors":"Ziwen An, Yanheng Liu, Geng Sun, Hongyang Pan, Aimin Wang","doi":"10.1109/ISCC55528.2022.9913016","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913016","url":null,"abstract":"Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCN) are promising technologies in Internet of Things (IoTs). However, energy-constrained devices and connectivity in complex environments are two major challenges for IoTs. We consider a UAV-enabled WPCN scenario that a UAV can connect with the ground IoT devices (IoTDs). To connect and fly faster, UAV needs to be scheduled reasonably and the corresponding trajectory should be optimized. Thus, we formulate a UAV scheduling and trajectory optimization problem (USTOP) to minimize the total time so that improving the charging and transmission efficiency. Since conventional methods are difficult to solve USTOP, we propose an improved simulated annealing (ISA) with the variable size changing mechanism, the conflict resolution mechanism and the hybrid evolution method to solve it. Simulation results verify the effectiveness and performance of ISA under different scales of the network, and the stability of the proposed algorithm is verified.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134017981","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912826
R. Zakrzewski, Trevor P. Martin, G. Oikonomou
Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.
{"title":"Anomaly Detection in Logical Sub-Views of WSNs","authors":"R. Zakrzewski, Trevor P. Martin, G. Oikonomou","doi":"10.1109/ISCC55528.2022.9912826","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912826","url":null,"abstract":"Wireless sensor networks are often distributed, diverse, and large making their monitoring hard. One way to tackle it is to focus on part of the system by creating logical sub-views which can be seen as proxies of the overall system operations. In this manuscript, logical sub-views consist of traffic aggregators and their topology which are monitored for anomaly. The aggregators are selected based on diversity and importance in the system and they are modelled as graphs to capture aggregation topology and data distributions. The aggregators' selection criteria, the method for comparison of partially overlapping sub-views, normal aggregation profiles acquisition, and measures of anomaly are proposed. A simulated wireless sensor network is used to acquire data at the edge and apply the method to demonstrate that focusing on system sub-views and comparing aggregation profiles facilitates anomaly detection also caused elsewhere in the system and the impact the anomaly has on aggregators.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131789086","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912955
A. Lioy, Ignazio Pedone, Silvia Sisinni
IEEE 802.1AE is a standard for Media Access Control security (MACsec), which enables data integrity, authentication, and confidentiality for traffic in a broadcast domain. This protects network communications against attacks at link layer, hence it provides a higher degree of security and flexibility compared to other security protocols, such as IPsec. Softwarised network infrastructures, based on Network Functions Virtualisation (NFV) and Software Defined Networking (SDN), provide higher flexibility than traditional networks. Nonetheless, these networks have a larger attack surface compared to legacy infrastructures based on hardware appliances. In this scenario, communication security is important to ensure that the traffic in a broadcast domain is not intercepted or manipulated. We propose an architecture for centralised management of MACsec-enabled switches in a NFV environment. Moreover, we present a PoC that integrates MACsec in the Open Source MANO NFV framework and we evaluate its performance.
IEEE 802.1AE是媒体访问控制安全(Media Access Control security, MACsec)标准，用于保证广播域中的数据完整性、身份验证和机密性。这可以保护网络通信免受链路层的攻击，因此与IPsec等其他安全协议相比，它提供了更高的安全性和灵活性。基于NFV (network Functions virtualization)和SDN (Software Defined Networking)技术的软件化网络基础设施提供了比传统网络更高的灵活性。尽管如此，与基于硬件设备的传统基础设施相比，这些网络具有更大的攻击面。在这种情况下，通信安全对于确保广播域中的流量不被拦截或操纵非常重要。我们提出了一种在NFV环境中集中管理启用macsec的交换机的架构。此外，我们提出了一个将MACsec集成到开源MANO NFV框架中的PoC，并对其性能进行了评估。
{"title":"(POSTER) Using MACsec to protect a Network Functions Virtualisation infrastructure","authors":"A. Lioy, Ignazio Pedone, Silvia Sisinni","doi":"10.1109/ISCC55528.2022.9912955","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912955","url":null,"abstract":"IEEE 802.1AE is a standard for Media Access Control security (MACsec), which enables data integrity, authentication, and confidentiality for traffic in a broadcast domain. This protects network communications against attacks at link layer, hence it provides a higher degree of security and flexibility compared to other security protocols, such as IPsec. Softwarised network infrastructures, based on Network Functions Virtualisation (NFV) and Software Defined Networking (SDN), provide higher flexibility than traditional networks. Nonetheless, these networks have a larger attack surface compared to legacy infrastructures based on hardware appliances. In this scenario, communication security is important to ensure that the traffic in a broadcast domain is not intercepted or manipulated. We propose an architecture for centralised management of MACsec-enabled switches in a NFV environment. Moreover, we present a PoC that integrates MACsec in the Open Source MANO NFV framework and we evaluate its performance.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127752095","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912999
Aneesh Bhattacharya, Risav Rana, Venkanna Udutalapally, Debanjan Das
Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviF L solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection.
{"title":"CoviFL: Edge-Assisted Federated Learning for Remote COVID-19 Detection in an AIoMT Framework","authors":"Aneesh Bhattacharya, Risav Rana, Venkanna Udutalapally, Debanjan Das","doi":"10.1109/ISCC55528.2022.9912999","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912999","url":null,"abstract":"Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviF L solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878732","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912873
Nicholas Lurski, A. Monica, Brooke Peterson, S. Papadakis
Clock glitching is a powerful tool for security analysis of embedded devices. It can be difficult to introduce this type of fault, especially when the clock is driven internally. For this reason, Laser Fault Injection (LFI) is attractive as a method to induce glitches in clocking behavior of a device. In this paper, we outline a methodology for rapidly mapping the silicon features utilized by an FPGA design, identifying areas of interest from that map, performing LFI testing, and characterizing the injected faults. By using this framework, we identify three unique faulting behaviors of the internal clock for the Xilinx Spartan 6 FPGA.
{"title":"Rapid Identification and Characterization of Laser Injected Clock Faults through OBIC Mapping","authors":"Nicholas Lurski, A. Monica, Brooke Peterson, S. Papadakis","doi":"10.1109/ISCC55528.2022.9912873","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912873","url":null,"abstract":"Clock glitching is a powerful tool for security analysis of embedded devices. It can be difficult to introduce this type of fault, especially when the clock is driven internally. For this reason, Laser Fault Injection (LFI) is attractive as a method to induce glitches in clocking behavior of a device. In this paper, we outline a methodology for rapidly mapping the silicon features utilized by an FPGA design, identifying areas of interest from that map, performing LFI testing, and characterizing the injected faults. By using this framework, we identify three unique faulting behaviors of the internal clock for the Xilinx Spartan 6 FPGA.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121275986","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9913026
Moysis Symeonides, Demetris Trihinas, Joanna Georgiou, Michalis Kasioulis, G. Pallis, M. Dikaiakos, Theodoros Toliopoulos, A. Michailidou, A. Gounaris
With the proliferation of raw Internet of Things (IoTs) data, Fog Computing is emerging as a computing paradigm for delay-sensitive streaming analytics with operators deploying big data distributed engines on Fog resources [1]. Nevertheless, the current (Cloud-based) distributed analytics solutions are unaware of the unique characteristics of Fog realms. For instance, task placement algorithms consider homogeneous underlying resources without considering the Fog nodes' heterogeneity and the non-uniform network connections, resulting in sub-optimal processing performance. Moreover, data quality can play an important role, where corrupted data, and network uncertainty may lead to less useful results. In turn, energy consumption can critically impact the overall cost and liveness of the underlying processing infrastructure. Specifically, scheduling tasks on nodes with energy-hungry profiles or battery-powered devices may temporarily be beneficial for the performance, but it may increase the overall cost, or/and the battery-powered devices may not be available when needed. A Fog-enabled analytics stack must allow users to optimize Fog-specific indicators or trade-offs among them. For instance, users may sacrifice a portion of the execution performance to minimize energy consumption or vice versa. Except for the performance issues raised by Fog, the state-of-the-art distributed processing engines offer only low-level procedural programming interfaces with operators facing a steep learning curve to master them. So, query abstractions are crucial for minimizing the deployment time, errors, and debugging.
{"title":"Demo: The RAINBOW Analytics Stack for the Fog Continuum","authors":"Moysis Symeonides, Demetris Trihinas, Joanna Georgiou, Michalis Kasioulis, G. Pallis, M. Dikaiakos, Theodoros Toliopoulos, A. Michailidou, A. Gounaris","doi":"10.1109/ISCC55528.2022.9913026","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913026","url":null,"abstract":"With the proliferation of raw Internet of Things (IoTs) data, Fog Computing is emerging as a computing paradigm for delay-sensitive streaming analytics with operators deploying big data distributed engines on Fog resources [1]. Nevertheless, the current (Cloud-based) distributed analytics solutions are unaware of the unique characteristics of Fog realms. For instance, task placement algorithms consider homogeneous underlying resources without considering the Fog nodes' heterogeneity and the non-uniform network connections, resulting in sub-optimal processing performance. Moreover, data quality can play an important role, where corrupted data, and network uncertainty may lead to less useful results. In turn, energy consumption can critically impact the overall cost and liveness of the underlying processing infrastructure. Specifically, scheduling tasks on nodes with energy-hungry profiles or battery-powered devices may temporarily be beneficial for the performance, but it may increase the overall cost, or/and the battery-powered devices may not be available when needed. A Fog-enabled analytics stack must allow users to optimize Fog-specific indicators or trade-offs among them. For instance, users may sacrifice a portion of the execution performance to minimize energy consumption or vice versa. Except for the performance issues raised by Fog, the state-of-the-art distributed processing engines offer only low-level procedural programming interfaces with operators facing a steep learning curve to master them. So, query abstractions are crucial for minimizing the deployment time, errors, and debugging.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128559376","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9913061
A. Caruso, S. Chessa, Soledad Escolar, Fernando Rincón Calle, J. C. López
Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different tasks characterized by different energy costs (and quality), depending on the current energy production of the energy harvesting subsystem and on the residual battery charge. In this context, we propose a novel scheduling problem that aims at keeping the energy neutrality of the scheduling while maximizing the overall quality of the executed tasks and minimizing the leaps of quality among consecutive tasks, so to improve the stability of the output of the device itself. We propose for this problem an algorithm based on a dynamic programming approach that can be executed even on low-power devices. By simulation we show that, with respect to the state of the art, the scheduling by our algorithm greatly improve the stability of the device with a minor penalty in terms of overall quality.
{"title":"Task Scheduling Stabilization for Solar Energy Harvesting Internet of Things Devices","authors":"A. Caruso, S. Chessa, Soledad Escolar, Fernando Rincón Calle, J. C. López","doi":"10.1109/ISCC55528.2022.9913061","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913061","url":null,"abstract":"Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different tasks characterized by different energy costs (and quality), depending on the current energy production of the energy harvesting subsystem and on the residual battery charge. In this context, we propose a novel scheduling problem that aims at keeping the energy neutrality of the scheduling while maximizing the overall quality of the executed tasks and minimizing the leaps of quality among consecutive tasks, so to improve the stability of the output of the device itself. We propose for this problem an algorithm based on a dynamic programming approach that can be executed even on low-power devices. By simulation we show that, with respect to the state of the art, the scheduling by our algorithm greatly improve the stability of the device with a minor penalty in terms of overall quality.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040484","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912795
Tianyu Meng, Dali Zhu, Xiaodong Xie, Hualin Zeng
The biometric technology of heart signal has always been an important research direction of identity recognition. In this paper, we propose a method for heart rate signal extraction and identification based on speckle images. It contains two parts: contactless heart rate signal acquisition and identification. Irradiate the human body with laser to get speckle images, and obtain the heart rate signal by image correlation and filtering. Next, build a dataset with signals and the convolutional neural network model is used to realize the identification. The experimental results show that, the speckle image correlation method can achieve heart rate signal extraction in places where the pulse vibration is weak. In addition, compared with k- Nearest Neighbor and random forest, the convolutional neural model is more accurate in identification. The model achieved an accuracy of 87.33 % on the dataset, which confirms that it is effective for identification based on non-contact heart rate signal.
{"title":"Non-Contact Heart Rate Signal Extraction and Identification Based on Speckle Image","authors":"Tianyu Meng, Dali Zhu, Xiaodong Xie, Hualin Zeng","doi":"10.1109/ISCC55528.2022.9912795","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912795","url":null,"abstract":"The biometric technology of heart signal has always been an important research direction of identity recognition. In this paper, we propose a method for heart rate signal extraction and identification based on speckle images. It contains two parts: contactless heart rate signal acquisition and identification. Irradiate the human body with laser to get speckle images, and obtain the heart rate signal by image correlation and filtering. Next, build a dataset with signals and the convolutional neural network model is used to realize the identification. The experimental results show that, the speckle image correlation method can achieve heart rate signal extraction in places where the pulse vibration is weak. In addition, compared with k- Nearest Neighbor and random forest, the convolutional neural model is more accurate in identification. The model achieved an accuracy of 87.33 % on the dataset, which confirms that it is effective for identification based on non-contact heart rate signal.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114225215","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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