Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00082
F. Righetti, C. Vallati, G. Anastasi, Giulio Masetti, F. Giandomenico
Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.
{"title":"Failure management strategies for IoT-based railways systems","authors":"F. Righetti, C. Vallati, G. Anastasi, Giulio Masetti, F. Giandomenico","doi":"10.1109/SMARTCOMP50058.2020.00082","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00082","url":null,"abstract":"Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117310657","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00040
K. Shuaib, Heba Saleous, N. Zaki, F. Dankar
Scientific research in the area of human genomics is no longer restricted to scientists. With the introduction of companies such as 23andme and Ancestry, the interested people of the public have been getting involved in genetic studies. However, the nature of the data being collected and its link to confidential, personal information will need to be properly secured and complete to prevent the same issues faced by electronic medical records in healthcare: fragmentation and risk of disclosure. By integrating blockchains with genomics and healthcare, patients and research participants will be able to participate in their own healthcare and research, increasing patient and user centricity. In this paper, a layered architecture of how blockchains can be implemented in genomics and healthcare is proposed. The proposed architecture shows how communication between users, researchers, and medical professionals can be improved while encouraging genetic research while improving security and privacy.
{"title":"A Layered Blockchain Framework for Healthcare and Genomics","authors":"K. Shuaib, Heba Saleous, N. Zaki, F. Dankar","doi":"10.1109/SMARTCOMP50058.2020.00040","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00040","url":null,"abstract":"Scientific research in the area of human genomics is no longer restricted to scientists. With the introduction of companies such as 23andme and Ancestry, the interested people of the public have been getting involved in genetic studies. However, the nature of the data being collected and its link to confidential, personal information will need to be properly secured and complete to prevent the same issues faced by electronic medical records in healthcare: fragmentation and risk of disclosure. By integrating blockchains with genomics and healthcare, patients and research participants will be able to participate in their own healthcare and research, increasing patient and user centricity. In this paper, a layered architecture of how blockchains can be implemented in genomics and healthcare is proposed. The proposed architecture shows how communication between users, researchers, and medical professionals can be improved while encouraging genetic research while improving security and privacy.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117092584","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00052
P. Bellini, D. Bologna, Qi Han, P. Nesi, G. Pantaleo, M. Paolucci
Smart cities are distributed heterogeneous systems of systems connected to each other via a variety of heterogeneous data streams involving multiple stakeholders and organizations. This complexity is reflected also in the data that have to be managed to provide a concrete and useful real time service to the citizens. The data ingestion phase is critical for the whole services, since it has to preserve the information, connect the new data with old data and establish right connections with city entities. This paper describes data ingestion and inspection in the Snap4City open source scalable Smart aNalytic APplication builder, with a specific focus on how heterogeneous data is represented, how its quality is inspected, and how to develop ingestion procedures in an efficient manner. The Snap4City ingestion processes are based on a semantic and unified data ingestion model, capable of aggregating different types of data. A performance comparison of different data ingestion modalities is presented.
{"title":"Data Ingestion and Inspection for Smart City Applications","authors":"P. Bellini, D. Bologna, Qi Han, P. Nesi, G. Pantaleo, M. Paolucci","doi":"10.1109/SMARTCOMP50058.2020.00052","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00052","url":null,"abstract":"Smart cities are distributed heterogeneous systems of systems connected to each other via a variety of heterogeneous data streams involving multiple stakeholders and organizations. This complexity is reflected also in the data that have to be managed to provide a concrete and useful real time service to the citizens. The data ingestion phase is critical for the whole services, since it has to preserve the information, connect the new data with old data and establish right connections with city entities. This paper describes data ingestion and inspection in the Snap4City open source scalable Smart aNalytic APplication builder, with a specific focus on how heterogeneous data is represented, how its quality is inspected, and how to develop ingestion procedures in an efficient manner. The Snap4City ingestion processes are based on a semantic and unified data ingestion model, capable of aggregating different types of data. A performance comparison of different data ingestion modalities is presented.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122247492","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00031
Simone Bolettieri, R. Bruno
In this study, we focus on the problem of managing a hybrid, shared IoT-based monitoring system, in which stationary sensor devices are complemented with user-carried personal devices embedded with sensing capabilities. The envisioned crowd-assisted monitoring system must support the sharing of the sensing infrastructure among multiple concurrent sensing tasks that can have highly varying QoS requirements. In such a scenario, a key issue is to maximise the utilisation efficiency of the physical sensing resources and the QoS satisfaction of sensing tasks while limiting the redundancy of collected data. As in previous research, we advocate the use of an IoT Broker, an intermediary entity that (i) interacts with the IoT applications to collect their QoS requirements (i.e., spatial coverage, data notification frequency); and (ii) coordinates with the redundant sensor deployments and mobile devices to selectively activate and configure the data streams that are needed to fulfil application requirements in a cost-efficient way. Then, we have developed an optimisation framework to jointly select the set of physical sensing resources to activate and the data update frequency for maximising the overall sensing performance while limiting redundant data. A key feature of our proposed framework is to be privacy-friendly as it only requires coarse-grained space-time knowledge of device location. Extensive simulations under realistic WSN deployments and real-life mobility patterns confirm the efficiency of the proposed solution in terms of data-coverage gain and reduction of data redundancy with respect to classical non-hybrid monitoring systems.
{"title":"QoS-Aware Data Management Mechanisms for Optimal Resource Utilisation in Crowd-Assisted Shared Sensor Networks","authors":"Simone Bolettieri, R. Bruno","doi":"10.1109/SMARTCOMP50058.2020.00031","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00031","url":null,"abstract":"In this study, we focus on the problem of managing a hybrid, shared IoT-based monitoring system, in which stationary sensor devices are complemented with user-carried personal devices embedded with sensing capabilities. The envisioned crowd-assisted monitoring system must support the sharing of the sensing infrastructure among multiple concurrent sensing tasks that can have highly varying QoS requirements. In such a scenario, a key issue is to maximise the utilisation efficiency of the physical sensing resources and the QoS satisfaction of sensing tasks while limiting the redundancy of collected data. As in previous research, we advocate the use of an IoT Broker, an intermediary entity that (i) interacts with the IoT applications to collect their QoS requirements (i.e., spatial coverage, data notification frequency); and (ii) coordinates with the redundant sensor deployments and mobile devices to selectively activate and configure the data streams that are needed to fulfil application requirements in a cost-efficient way. Then, we have developed an optimisation framework to jointly select the set of physical sensing resources to activate and the data update frequency for maximising the overall sensing performance while limiting redundant data. A key feature of our proposed framework is to be privacy-friendly as it only requires coarse-grained space-time knowledge of device location. Extensive simulations under realistic WSN deployments and real-life mobility patterns confirm the efficiency of the proposed solution in terms of data-coverage gain and reduction of data redundancy with respect to classical non-hybrid monitoring systems.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531097","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00060
Dimitrios Sikeridis
Internet of Things (IoT) deployments are becoming the backbone of all future Smart City (SC) environments. They can, therefore, act as massive crowd-sourced data aggregators, driven by device-to-device interactions with SC users' mobile devices and their wireless interfaces. Provided that, our research focuses on developing probabilistic and machine learning models to (a) enable knowledge discovery from passive user interactions with the wireless IoT infrastructure and (b) apply the collected intelligence to increase the energy-efficiency and resiliency of the Smart City's IoT network. In this extended abstract we elaborate on the motivation behind our work, and the related challenges, while pointing to the solutions developed so far.
{"title":"IoT-enabled Knowledge Extraction and Edge Device Sustainability in Smart Cities","authors":"Dimitrios Sikeridis","doi":"10.1109/SMARTCOMP50058.2020.00060","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00060","url":null,"abstract":"Internet of Things (IoT) deployments are becoming the backbone of all future Smart City (SC) environments. They can, therefore, act as massive crowd-sourced data aggregators, driven by device-to-device interactions with SC users' mobile devices and their wireless interfaces. Provided that, our research focuses on developing probabilistic and machine learning models to (a) enable knowledge discovery from passive user interactions with the wireless IoT infrastructure and (b) apply the collected intelligence to increase the energy-efficiency and resiliency of the Smart City's IoT network. In this extended abstract we elaborate on the motivation behind our work, and the related challenges, while pointing to the solutions developed so far.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131128259","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00048
Sopicha Stirapongsasuti, Yugo Nakamura, K. Yasumoto
Recently smart homes equipped with many sensors and IoT devices are widespread. However, when smart home users receive smart home services like elderly monitoring, they need to upload their privacy sensitive data to potentially untrusted cloud servers where the service quality (user's benefit) depends on the amount/frequency of the uploaded data. In this paper, aiming to minimize the risk of privacy leakage and maximize users' benefit obtained through services, we propose a novel privacy-aware data management method that works on a smart-home system composed of smart homes with sensors, edge computing servers, and a cloud server. We formulate a combinatorial optimization problem which determines the best choice of data type (raw or activity label recognized at the edge) and upload frequency in each time slot taking into account the constraints of edge server resources and users' budgets as well as the k-anonymity of activities and users' preferences. Since the target problem is NP-hard, we propose a heuristic algorithm to derive semi-optimal solutions by determining choices with better objective function values in a greedy manner. Through experiments using smart-home open dataset, we confirmed that the proposed method outperforms the conventional methods using only a cloud server.
{"title":"Privacy-Aware Sensor Data Upload Management for Securely Receiving Smart Home Services","authors":"Sopicha Stirapongsasuti, Yugo Nakamura, K. Yasumoto","doi":"10.1109/SMARTCOMP50058.2020.00048","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00048","url":null,"abstract":"Recently smart homes equipped with many sensors and IoT devices are widespread. However, when smart home users receive smart home services like elderly monitoring, they need to upload their privacy sensitive data to potentially untrusted cloud servers where the service quality (user's benefit) depends on the amount/frequency of the uploaded data. In this paper, aiming to minimize the risk of privacy leakage and maximize users' benefit obtained through services, we propose a novel privacy-aware data management method that works on a smart-home system composed of smart homes with sensors, edge computing servers, and a cloud server. We formulate a combinatorial optimization problem which determines the best choice of data type (raw or activity label recognized at the edge) and upload frequency in each time slot taking into account the constraints of edge server resources and users' budgets as well as the k-anonymity of activities and users' preferences. Since the target problem is NP-hard, we propose a heuristic algorithm to derive semi-optimal solutions by determining choices with better objective function values in a greedy manner. Through experiments using smart-home open dataset, we confirmed that the proposed method outperforms the conventional methods using only a cloud server.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"20 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327005","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 : 2020-09-01DOI: 10.1109/smartcomp50058.2020.00014
Ssc
It is our great pleasure to welcome you to the 6th IEEE Workshop on Sensors and Smart Cities (SSC 2020) co-located with the IEEE International Conference on Smart Computing (IEEE SMARTCOMP 2020). Smart cities represent an improvement of today’s cities both functionally and structurally that strategically utilizes many smart factors, such as information and communications technology, to increase the city’s sustainable growth and strengthen city functions. At the same time, smart cities aim to ensure citizens’ a better quality of life and health, even allowing them to be actors in their city. In this context, SSC provides an interesting forum where up-to-date technologies and applications are presented and new ideas and directions discussed among attendees from both academia and industry.
{"title":"Message from the Workshop Co-Chairs","authors":"Ssc","doi":"10.1109/smartcomp50058.2020.00014","DOIUrl":"https://doi.org/10.1109/smartcomp50058.2020.00014","url":null,"abstract":"It is our great pleasure to welcome you to the 6th IEEE Workshop on Sensors and Smart Cities (SSC 2020) co-located with the IEEE International Conference on Smart Computing (IEEE SMARTCOMP 2020). Smart cities represent an improvement of today’s cities both functionally and structurally that strategically utilizes many smart factors, such as information and communications technology, to increase the city’s sustainable growth and strengthen city functions. At the same time, smart cities aim to ensure citizens’ a better quality of life and health, even allowing them to be actors in their city. In this context, SSC provides an interesting forum where up-to-date technologies and applications are presented and new ideas and directions discussed among attendees from both academia and industry.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002127","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00029
H. Muccini, Karthik Vaidhyanathan
The use of IoT systems is increasing day by day. However, these systems due to their heterogeneity and inherently dynamic nature, face different uncertainties from the context, environment, etc. Such uncertainties can have a big impact on the overall system QoS, especially on energy efficiency and data traffic. This calls for better ways of architecting IoT systems that may self-adapt to keep the desired QoS. This paper presents an approach that leverages the use of machine learning (ML) techniques to perform a proactive adaptation of IoT architectures using self-adaptation patterns. It i) continuously monitors the QoS parameters; ii) forecasts possible deviations from the acceptable QoS parameters; iii) selects the best adaptation pattern based on forecasts using reinforcement learning (RL) techniques; iv) checks the quality of the selected decision using feedback mechanisms; and v) continuously performs the loop of the forecast, adaptation, and feedback. The results of our evaluations show that our approach can provide accurate QoS forecasts and further improve the energy efficiency of the system while maintaining the required data traffic.
{"title":"Leveraging Machine Learning Techniques for Architecting Self-Adaptive IoT Systems","authors":"H. Muccini, Karthik Vaidhyanathan","doi":"10.1109/SMARTCOMP50058.2020.00029","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00029","url":null,"abstract":"The use of IoT systems is increasing day by day. However, these systems due to their heterogeneity and inherently dynamic nature, face different uncertainties from the context, environment, etc. Such uncertainties can have a big impact on the overall system QoS, especially on energy efficiency and data traffic. This calls for better ways of architecting IoT systems that may self-adapt to keep the desired QoS. This paper presents an approach that leverages the use of machine learning (ML) techniques to perform a proactive adaptation of IoT architectures using self-adaptation patterns. It i) continuously monitors the QoS parameters; ii) forecasts possible deviations from the acceptable QoS parameters; iii) selects the best adaptation pattern based on forecasts using reinforcement learning (RL) techniques; iv) checks the quality of the selected decision using feedback mechanisms; and v) continuously performs the loop of the forecast, adaptation, and feedback. The results of our evaluations show that our approach can provide accurate QoS forecasts and further improve the energy efficiency of the system while maintaining the required data traffic.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128389730","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00061
F. Righetti
The IETF is defining the 6TiSCH Architecture for the Industrial Internet of Things (IIoT) to provide low-latency, low jitter, and high-reliability communication. The 6TiSCH architecture identifies different ways to manage communication resources, namely static, centralized, autonomous, distributed, and hop-by-hop approaches. The distributed approach has gained more attention thanks to its capabilities of self-configuration and adaptation to different network conditions. In distributed scheduling, each node runs a Scheduling Function (SF) to dynamically compute the number of resources to allocate, and leverages the 6top protocol (6P) to negotiate them with its neighbors. In this paper, we focus on the distributed mode and provide an overview of our ongoing research activity on this topic.
{"title":"6TiSCH Architecture for the Industrial Internet of Things: Performance Analysis","authors":"F. Righetti","doi":"10.1109/SMARTCOMP50058.2020.00061","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00061","url":null,"abstract":"The IETF is defining the 6TiSCH Architecture for the Industrial Internet of Things (IIoT) to provide low-latency, low jitter, and high-reliability communication. The 6TiSCH architecture identifies different ways to manage communication resources, namely static, centralized, autonomous, distributed, and hop-by-hop approaches. The distributed approach has gained more attention thanks to its capabilities of self-configuration and adaptation to different network conditions. In distributed scheduling, each node runs a Scheduling Function (SF) to dynamically compute the number of resources to allocate, and leverages the 6top protocol (6P) to negotiate them with its neighbors. In this paper, we focus on the distributed mode and provide an overview of our ongoing research activity on this topic.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275684","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 : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00047
Eura Shin, A. R. Khamesi, Zachary Bahr, S. Silvestri, Denise A. Baker
Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called $K$ -Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.
{"title":"A User-Centered Active Learning Approach for Appliance Recognition","authors":"Eura Shin, A. R. Khamesi, Zachary Bahr, S. Silvestri, Denise A. Baker","doi":"10.1109/SMARTCOMP50058.2020.00047","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00047","url":null,"abstract":"Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called $K$ -Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129058928","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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