Pub Date : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595397
D. Agarwal, Pravesh Srivastava, Sergio Martin del Campo, Balasubramaniam Natarajan, Babji Srinivasan
Internet of Things (IoT) is a key enabler of Industry 4.0 with networked devices providing sensor data to help manage, automate, streamline and optimize assets, operations and processes. In such industrial IoT settings, reliability and process experts spend a considerable amount of time in creating accurate ground-truth data to assist with the inferencing capabilities of Artificial Intelligence (AI) engines. This process can be time-consuming and sometimes inaccurate, depending on the complexity of data. Accurate expert annotated data is the foundation for many AI applications because data needs to be classified on several bases, for instance into ‘normal’, ‘abnormal’ or ‘pre-abnormal’ states. Such problem formulations can be appropriately addressed using Active Learning (AL) techniques. We propose an AL framework capable of handling two practical challenges: oracle uncertainty and quantification of model performance in the absence of ground truth. Consequently, the proposed approach addresses uncertainties within AL techniques by fusing information pertaining to expertise levels of the human annotators and their confidence levels corresponding to the annotation provided.
{"title":"Addressing Uncertainties within Active Learning for Industrial IoT","authors":"D. Agarwal, Pravesh Srivastava, Sergio Martin del Campo, Balasubramaniam Natarajan, Babji Srinivasan","doi":"10.1109/WF-IoT51360.2021.9595397","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595397","url":null,"abstract":"Internet of Things (IoT) is a key enabler of Industry 4.0 with networked devices providing sensor data to help manage, automate, streamline and optimize assets, operations and processes. In such industrial IoT settings, reliability and process experts spend a considerable amount of time in creating accurate ground-truth data to assist with the inferencing capabilities of Artificial Intelligence (AI) engines. This process can be time-consuming and sometimes inaccurate, depending on the complexity of data. Accurate expert annotated data is the foundation for many AI applications because data needs to be classified on several bases, for instance into ‘normal’, ‘abnormal’ or ‘pre-abnormal’ states. Such problem formulations can be appropriately addressed using Active Learning (AL) techniques. We propose an AL framework capable of handling two practical challenges: oracle uncertainty and quantification of model performance in the absence of ground truth. Consequently, the proposed approach addresses uncertainties within AL techniques by fusing information pertaining to expertise levels of the human annotators and their confidence levels corresponding to the annotation provided.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116764717","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595417
L. Turchet, P. Bouquet
Interoperability represents an important aspect in research dealing with the emerging class of smart musical instruments (SMIs). To date, no interoperable file format for the exchange of content produced by heterogeneous SMIs has been defined yet. This paper proposes a solution to the issue of sharing presets among heterogeneous SMIs, which are used to conFigure an SMI. The heterogeneity of SMIs may come from the type, structure and implementation of the SMI’s embedded system, its sound engine and sensor interface. The presented solution is based on the “ontology-based data access” paradigm and leverages the existing Smart Musical Instruments Ontology. This approach allows one to share presets between heterogeneous SMIs by mapping information about the configuration of an instrument to the concepts of the ontology. Thanks to this approach, SMIs developers can implement programs that convert proprietary formats for the configuration of the instrument into a common format for SMIs, and vice versa. We present the general architecture and workflow of this approach, and we describe an implementation for it which involves the sharing of presets among two heterogeneous smart guitars.
{"title":"Smart Musical Instruments preset sharing: an ontology-based data access approach","authors":"L. Turchet, P. Bouquet","doi":"10.1109/WF-IoT51360.2021.9595417","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595417","url":null,"abstract":"Interoperability represents an important aspect in research dealing with the emerging class of smart musical instruments (SMIs). To date, no interoperable file format for the exchange of content produced by heterogeneous SMIs has been defined yet. This paper proposes a solution to the issue of sharing presets among heterogeneous SMIs, which are used to conFigure an SMI. The heterogeneity of SMIs may come from the type, structure and implementation of the SMI’s embedded system, its sound engine and sensor interface. The presented solution is based on the “ontology-based data access” paradigm and leverages the existing Smart Musical Instruments Ontology. This approach allows one to share presets between heterogeneous SMIs by mapping information about the configuration of an instrument to the concepts of the ontology. Thanks to this approach, SMIs developers can implement programs that convert proprietary formats for the configuration of the instrument into a common format for SMIs, and vice versa. We present the general architecture and workflow of this approach, and we describe an implementation for it which involves the sharing of presets among two heterogeneous smart guitars.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115066805","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9594925
Jacob Hunte, H. Lutfiyya, A. Haque
This paper looks at providing an efficient method of synchronizing devices deployed in an electrical grid. The proposed method focuses on device synchronization specifically for localizing faults on distribution networks. It analyses the travelling waves that are present on the electrical grid at and around the time of the fault. It is a synchronization method which uses external signals to synchronize the fault events detected by the devices without reliance on accuracy of clocks used in each device. Initial experimental results shows that this is a promising approach.
{"title":"Device Synchronization for Fault Localization in Electrical Distribution Grids","authors":"Jacob Hunte, H. Lutfiyya, A. Haque","doi":"10.1109/WF-IoT51360.2021.9594925","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9594925","url":null,"abstract":"This paper looks at providing an efficient method of synchronizing devices deployed in an electrical grid. The proposed method focuses on device synchronization specifically for localizing faults on distribution networks. It analyses the travelling waves that are present on the electrical grid at and around the time of the fault. It is a synchronization method which uses external signals to synchronize the fault events detected by the devices without reliance on accuracy of clocks used in each device. Initial experimental results shows that this is a promising approach.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121842264","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595142
Zaghloul Saad Zaghloul, Nelly Elsayed, Chengcheng Li, M. Bayoumi
Network security morning (NSM) is essential for any cybersecurity system, where the average cost of a cyberattack is ${$}1.1$ million. No matter how much a system is secure, it will eventually fail without proper and continuous monitoring. No wonder that the cybersecurity market is expected to grow up to ${$} 170.4$ billion in 2022. However, the majority of legacy industries do not invest in NSM implementation until it is too late due to the initial and operation cost and static unutilized resources. Thus, this paper proposes a novel dynamic Internet of things (IoT) architecture for an industrial NSM that features a low installation and operation cost, low power consumption, intelligent organization behavior, and environmentally friendly operation. As a case study, the system is implemented in a midrange oil a gas manufacture facility in the southern states with more than 300 machines and servers over three remote locations and a production plant that features a challenging atmosphere condition. The proposed system successfully shows a significant saving $(gt 65$%) in power consumption, acquires one-tenth the installation cost, develops an intelligent operation expert system tools as well as saves the environment from more than 500 mg of CO2 pollution per hour, promoting green IoT systems.
{"title":"Green IoT System Architecture for Applied Autonomous Network Cybersecurity Monitoring","authors":"Zaghloul Saad Zaghloul, Nelly Elsayed, Chengcheng Li, M. Bayoumi","doi":"10.1109/WF-IoT51360.2021.9595142","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595142","url":null,"abstract":"Network security morning (NSM) is essential for any cybersecurity system, where the average cost of a cyberattack is ${$}1.1$ million. No matter how much a system is secure, it will eventually fail without proper and continuous monitoring. No wonder that the cybersecurity market is expected to grow up to ${$} 170.4$ billion in 2022. However, the majority of legacy industries do not invest in NSM implementation until it is too late due to the initial and operation cost and static unutilized resources. Thus, this paper proposes a novel dynamic Internet of things (IoT) architecture for an industrial NSM that features a low installation and operation cost, low power consumption, intelligent organization behavior, and environmentally friendly operation. As a case study, the system is implemented in a midrange oil a gas manufacture facility in the southern states with more than 300 machines and servers over three remote locations and a production plant that features a challenging atmosphere condition. The proposed system successfully shows a significant saving $(gt 65$%) in power consumption, acquires one-tenth the installation cost, develops an intelligent operation expert system tools as well as saves the environment from more than 500 mg of CO2 pollution per hour, promoting green IoT systems.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"6 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123686632","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595556
D. Suarez-Bagnasco
Aerosols are fine solid particles (particulate matter: PM) or liquid droplets in gas (usually air). Its origin can be natural or anthropogenic. Air PM pollution exposure is linked to diverse human health problems and to many environmental effects. Air samplers are used to study particles in air. Systematic periodic air sampling is needed to have confident air quality assessment. In this work we present a device (named RDMA) and a software application (named Enviro-Air Sampling) we have developed to enable access to environmental data, flow data, geolocation, and meteorological conditions from high volume air samplers (HVAS) with no data acquisition capabilities. One of the objectives of the RDMA (designed ab-initio to be an easy add-on to Tisch HVAS) is to enable a more precise determination (compared to Tisch Dickinson chart recorder) of the mass concentration of particles (MC) and of the standard mass concentration (SMC). In this paper we present some aspects of the work done that involved the use of IoT, Cloud, and DLT (Distributed Ledger Technology) technologies, that are enabling and driving Digital Transformation.
{"title":"Application case of IoT, Cloud and DLT technologies to enhance particulate matter air sampling","authors":"D. Suarez-Bagnasco","doi":"10.1109/WF-IoT51360.2021.9595556","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595556","url":null,"abstract":"Aerosols are fine solid particles (particulate matter: PM) or liquid droplets in gas (usually air). Its origin can be natural or anthropogenic. Air PM pollution exposure is linked to diverse human health problems and to many environmental effects. Air samplers are used to study particles in air. Systematic periodic air sampling is needed to have confident air quality assessment. In this work we present a device (named RDMA) and a software application (named Enviro-Air Sampling) we have developed to enable access to environmental data, flow data, geolocation, and meteorological conditions from high volume air samplers (HVAS) with no data acquisition capabilities. One of the objectives of the RDMA (designed ab-initio to be an easy add-on to Tisch HVAS) is to enable a more precise determination (compared to Tisch Dickinson chart recorder) of the mass concentration of particles (MC) and of the standard mass concentration (SMC). In this paper we present some aspects of the work done that involved the use of IoT, Cloud, and DLT (Distributed Ledger Technology) technologies, that are enabling and driving Digital Transformation.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122755881","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595769
Meenakshi Sethunath, Yang Peng
Serverless computing functions typically execute in the cloud. However, the high latency of accessing the cloud may require running them on edge servers, which have limited computing power and memory availability. This paper proposes a joint resource allocation and request dispatch scheme to execute serverless computing functions over edge and cloud collaboratively. This new scheme explicitly considers how to allocate server memory and operation budget for concurrent serverless computing requests considering the cold-start latency in design. The proposed scheme has been evaluated through extensive simulations. Its effectiveness has been proved by comparison with the upper-bound results.
{"title":"A Joint Resource Allocation and Request Dispatch Scheme for Performing Serverless Computing over Edge and Cloud","authors":"Meenakshi Sethunath, Yang Peng","doi":"10.1109/WF-IoT51360.2021.9595769","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595769","url":null,"abstract":"Serverless computing functions typically execute in the cloud. However, the high latency of accessing the cloud may require running them on edge servers, which have limited computing power and memory availability. This paper proposes a joint resource allocation and request dispatch scheme to execute serverless computing functions over edge and cloud collaboratively. This new scheme explicitly considers how to allocate server memory and operation budget for concurrent serverless computing requests considering the cold-start latency in design. The proposed scheme has been evaluated through extensive simulations. Its effectiveness has been proved by comparison with the upper-bound results.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128293838","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595496
Ali Shoker, Peter Moertl, Ramiro Robles
Truck platooning is a form of convoy cooperative driving of connected trucks assisted by a lead truck. The aim is to reduce the fuel and driving costs, improve road safety, and reduce CO2 emission. Being semi-autonomous, platoons must be trustworthy in many perspectives. This paper presents a high-level trustworthy requirements analysis on three key perspectives: driver, communication, and security. In addition, we observed that any trustworthy requirement analysis is incomplete if perspectives are addressed independently. Therefore, we propose a simple holistic methodology that addresses the different perspectives as well as their dependencies, and we exemplify the use of the methodology with two use cases presented in the paper. However, we draw attention to the importance of more research to drive a more exhaustive and validated methodology1.
{"title":"A First Step Towards Holistic Trustworthy Platoons","authors":"Ali Shoker, Peter Moertl, Ramiro Robles","doi":"10.1109/WF-IoT51360.2021.9595496","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595496","url":null,"abstract":"Truck platooning is a form of convoy cooperative driving of connected trucks assisted by a lead truck. The aim is to reduce the fuel and driving costs, improve road safety, and reduce CO2 emission. Being semi-autonomous, platoons must be trustworthy in many perspectives. This paper presents a high-level trustworthy requirements analysis on three key perspectives: driver, communication, and security. In addition, we observed that any trustworthy requirement analysis is incomplete if perspectives are addressed independently. Therefore, we propose a simple holistic methodology that addresses the different perspectives as well as their dependencies, and we exemplify the use of the methodology with two use cases presented in the paper. However, we draw attention to the importance of more research to drive a more exhaustive and validated methodology1.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124626280","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595077
M. Bundas, Chasity Nadeau, T. Nguyen, Jeannine Shantz, M. Balduccini, Tran Cao Son
While Artificial Intelligence (AI) and Machine Learning provide a pathway of new and exciting possibilities for AI-Enabled Cyber-Physical and Internet of Things systems, these technology solutions are not without challenges that may hinder adoption. We do not always understand why AI components behave in the way they do, nor can we always predict what they will do under new circumstances. In this paper, we discuss possible approaches for extending the NIST CPS Framework in a way that provides designers, operators and other stakeholders with a shared vocabulary and a collaborative framework allowing them to discuss, identify, express, and verify requirements on the behavior of AI-enabled Cyber-Physical and Internet of Things Systems.
{"title":"Towards a Framework for Characterizing the Behavior of AI-Enabled Cyber-Physical and IoT Systems","authors":"M. Bundas, Chasity Nadeau, T. Nguyen, Jeannine Shantz, M. Balduccini, Tran Cao Son","doi":"10.1109/WF-IoT51360.2021.9595077","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595077","url":null,"abstract":"While Artificial Intelligence (AI) and Machine Learning provide a pathway of new and exciting possibilities for AI-Enabled Cyber-Physical and Internet of Things systems, these technology solutions are not without challenges that may hinder adoption. We do not always understand why AI components behave in the way they do, nor can we always predict what they will do under new circumstances. In this paper, we discuss possible approaches for extending the NIST CPS Framework in a way that provides designers, operators and other stakeholders with a shared vocabulary and a collaborative framework allowing them to discuss, identify, express, and verify requirements on the behavior of AI-enabled Cyber-Physical and Internet of Things Systems.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128050600","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595025
P. Rojas, Sara Alahmadi, M. Bayoumi
As the Internet of Things (IoT) grows, the number of resource-constrained devices also grows. The limitations of these devices impedes the usage of conventional security methods. However, physical layer security (PLS) has many diverse techniques that do not require significant resources that can be used to bolster the defenses of these devices. Due to the heterogeneity in IoT, we first consider relevant IoT communication protocols that are being used (WiFi, ZigBee, LoRaWAN) to connect these devices, and then the scope of surveyed PLS techniques is narrowed-down to a set of promising techniques that can be applied with the communication protocol. In this paper we explore recent developments in PLS techniques that require minimal to no overhead in their implementation, and provide security against some of the attacks that IoT devices and networks are vulnerable against: spoofing, jamming and eavesdropping attacks. The solutions explored include radio frequency (RF) fingerprinting, spread spectrum coding, and beamforming.
{"title":"Physical Layer Security for IoT Communications - A Survey","authors":"P. Rojas, Sara Alahmadi, M. Bayoumi","doi":"10.1109/WF-IoT51360.2021.9595025","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595025","url":null,"abstract":"As the Internet of Things (IoT) grows, the number of resource-constrained devices also grows. The limitations of these devices impedes the usage of conventional security methods. However, physical layer security (PLS) has many diverse techniques that do not require significant resources that can be used to bolster the defenses of these devices. Due to the heterogeneity in IoT, we first consider relevant IoT communication protocols that are being used (WiFi, ZigBee, LoRaWAN) to connect these devices, and then the scope of surveyed PLS techniques is narrowed-down to a set of promising techniques that can be applied with the communication protocol. In this paper we explore recent developments in PLS techniques that require minimal to no overhead in their implementation, and provide security against some of the attacks that IoT devices and networks are vulnerable against: spoofing, jamming and eavesdropping attacks. The solutions explored include radio frequency (RF) fingerprinting, spread spectrum coding, and beamforming.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126445555","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9596007
Steven Wyatt, David Elliott, A. Aravamudan, C. Otero, L. D. Otero, G. Anagnostopoulos, Anthony O. Smith, A. Peter, Wesley Jones, Steven Leung, Eric Lam
The unprecedented growth of edge sensor infrastructure is driving the demand function for in situ analytics, i.e. automated decision support at the point of data collection. In the present work, we detail our state-of-the-art Environmental Sound Classification (ESC) framework that is capable of near real-time acoustic categorization directly at the edge. Existing ESC algorithms primarily train and test on pristine datasets that fail in real-world deployments due their inability to handle real-world noisy environments. Methods to denoise the sounds are often computationally expensive for edge devices and do not guarantee performance improvements. To this end, we investigate a way to make existing ESC models robust and make them work in operational resource-constrained settings. Our framework employs a noisy classification model consisting of a tiny BERT-based Transformer (less than 20,000 parameters) and considers hardening of this model through the use of transmission channel noise augmentation. We detail real-world results through its deployment on a Raspberry Pi Zero and demonstrate its classification performance.
边缘传感器基础设施的空前增长推动了现场分析的需求功能，即在数据收集点的自动化决策支持。在目前的工作中，我们详细介绍了我们最先进的环境声音分类(ESC)框架，该框架能够直接在边缘进行近乎实时的声学分类。现有的ESC算法主要在原始数据集上进行训练和测试，这些数据集由于无法处理真实的噪声环境而在实际部署中失败。对于边缘设备来说，去噪声音的方法通常在计算上是昂贵的，并且不能保证性能的提高。为此，我们研究了一种方法，使现有的ESC模型具有鲁棒性，并使其在操作资源受限的环境下工作。我们的框架采用了一个由基于bert的小型变压器(小于20,000个参数)组成的噪声分类模型，并考虑通过使用传输通道噪声增强来强化该模型。我们通过在Raspberry Pi Zero上的部署详细介绍了实际结果，并演示了其分类性能。
{"title":"Environmental Sound Classification with Tiny Transformers in Noisy Edge Environments","authors":"Steven Wyatt, David Elliott, A. Aravamudan, C. Otero, L. D. Otero, G. Anagnostopoulos, Anthony O. Smith, A. Peter, Wesley Jones, Steven Leung, Eric Lam","doi":"10.1109/WF-IoT51360.2021.9596007","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9596007","url":null,"abstract":"The unprecedented growth of edge sensor infrastructure is driving the demand function for in situ analytics, i.e. automated decision support at the point of data collection. In the present work, we detail our state-of-the-art Environmental Sound Classification (ESC) framework that is capable of near real-time acoustic categorization directly at the edge. Existing ESC algorithms primarily train and test on pristine datasets that fail in real-world deployments due their inability to handle real-world noisy environments. Methods to denoise the sounds are often computationally expensive for edge devices and do not guarantee performance improvements. To this end, we investigate a way to make existing ESC models robust and make them work in operational resource-constrained settings. Our framework employs a noisy classification model consisting of a tiny BERT-based Transformer (less than 20,000 parameters) and considers hardening of this model through the use of transmission channel noise augmentation. We detail real-world results through its deployment on a Raspberry Pi Zero and demonstrate its classification performance.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132963793","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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