M. Ahan, A. Nambi, T. Ganu, Dhananjay Nahata, S. Kalyanaraman
With the increasing adaption of solar energy worldwide, there is a huge interest to develop systems that help drive efficiency during manufacturing and ongoing operations. Due to various real-world conditions and processes, solar panels develop faults during their manufacturing and operations. The objective of this work is to build an End-to-End Fault Detection system to detect and localize faults in solar panels based on their Electroluminescence (EL) Imaging. Today, the majority of fault detection happens through manual inspection of EL images. To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell. We propose a hybrid architecture that contains an ensemble of multiple CNN model architectures for classification and detection. The ensemble is capable of serving both - monocrystalline and polycrystalline solar panels. The proposed system significantly helps in increasing the efficiency of solar panels and reducing warranty and repair costs. We demonstrate the performance of the proposed system using an open EL image dataset with 95% of cell-level fault prediction accuracy and high recall. The proposed algorithms are applicable and can be extended for other solar applications that use RGB, EL, or thermal imaging techniques.
{"title":"AI-assisted Cell-Level Fault Detection and Localization in Solar PV Electroluminescence Images","authors":"M. Ahan, A. Nambi, T. Ganu, Dhananjay Nahata, S. Kalyanaraman","doi":"10.1145/3485730.3493455","DOIUrl":"https://doi.org/10.1145/3485730.3493455","url":null,"abstract":"With the increasing adaption of solar energy worldwide, there is a huge interest to develop systems that help drive efficiency during manufacturing and ongoing operations. Due to various real-world conditions and processes, solar panels develop faults during their manufacturing and operations. The objective of this work is to build an End-to-End Fault Detection system to detect and localize faults in solar panels based on their Electroluminescence (EL) Imaging. Today, the majority of fault detection happens through manual inspection of EL images. To this end, we propose the design and implementation of an end-to-end system that firstly divides the solar panel into individual solar cells and then passes these cell images through a classification + detection pipeline for identifying the fault type and localizing the faults inside a cell. We propose a hybrid architecture that contains an ensemble of multiple CNN model architectures for classification and detection. The ensemble is capable of serving both - monocrystalline and polycrystalline solar panels. The proposed system significantly helps in increasing the efficiency of solar panels and reducing warranty and repair costs. We demonstrate the performance of the proposed system using an open EL image dataset with 95% of cell-level fault prediction accuracy and high recall. The proposed algorithms are applicable and can be extended for other solar applications that use RGB, EL, or thermal imaging techniques.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832242","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}
Edge computing for the Internet of Things prescribes executing applications on server machines closer to devices rather than depending on the cloud. However, server machines are expensive, are not flexible to adapt to varying application requirements, require gateways to interact with IoT devices, and follow a centralized model which increases traffic and application latency. Special-purpose hardware for the edge is becoming increasingly sophisticated, with support for machine learning, secure enclaves etc., and this work is an attempt to leverage such hardware to cooperatively execute edge applications, rather than relying on expensive edge servers. To do so, our design relies on a distributed middleware which can seamlessly scale up with new hardware, and a task scheduler which best matches application requirements with the hardware capabilities available. We have built a prototype middleware that operates on multiple gateways in our testbed of 250 IoT devices, and we plan to further improve our platform to support more varying use cases.
{"title":"Enabling Elasticity on the Edge using Heterogeneous Gateways","authors":"Nabeel Nasir, Bradford Campbell","doi":"10.1145/3485730.3492890","DOIUrl":"https://doi.org/10.1145/3485730.3492890","url":null,"abstract":"Edge computing for the Internet of Things prescribes executing applications on server machines closer to devices rather than depending on the cloud. However, server machines are expensive, are not flexible to adapt to varying application requirements, require gateways to interact with IoT devices, and follow a centralized model which increases traffic and application latency. Special-purpose hardware for the edge is becoming increasingly sophisticated, with support for machine learning, secure enclaves etc., and this work is an attempt to leverage such hardware to cooperatively execute edge applications, rather than relying on expensive edge servers. To do so, our design relies on a distributed middleware which can seamlessly scale up with new hardware, and a task scheduler which best matches application requirements with the hardware capabilities available. We have built a prototype middleware that operates on multiple gateways in our testbed of 250 IoT devices, and we plan to further improve our platform to support more varying use cases.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"T156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125658022","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}
The primary role of a reconfigurable intelligent surface (RIS) is to restore the propagation path between the access point (AP) and the user equipment (UE) in a non-line-of-sight communication link. Depending on the RIS placement with respect to the AP and UE positions, different power levels can reach the UE, thus affecting the quality of the communication. Particularly when the UE moves freely, the RIS position that maximizes the received signal will depend strongly on the UE location. In this context, we use an analytical model to assess the decisions that have to be made concerning the positioning of the RIS, which are determined by the interplay of three crucial quantities, namely (a) the available AP gain, (b) the available positions for the AP and RIS placement, and (c) the minimum desired power levels at the UE. The impact of the AP antenna gain tunability on the RIS placement selection is assessed and illustrated in D-band indoor scenarios.
{"title":"An analytical framework for Reconfigurable Intelligent Surfaces placement in a mobile user environment","authors":"Giorgos Stratidakis, S. Droulias, A. Alexiou","doi":"10.1145/3485730.3494038","DOIUrl":"https://doi.org/10.1145/3485730.3494038","url":null,"abstract":"The primary role of a reconfigurable intelligent surface (RIS) is to restore the propagation path between the access point (AP) and the user equipment (UE) in a non-line-of-sight communication link. Depending on the RIS placement with respect to the AP and UE positions, different power levels can reach the UE, thus affecting the quality of the communication. Particularly when the UE moves freely, the RIS position that maximizes the received signal will depend strongly on the UE location. In this context, we use an analytical model to assess the decisions that have to be made concerning the positioning of the RIS, which are determined by the interplay of three crucial quantities, namely (a) the available AP gain, (b) the available positions for the AP and RIS placement, and (c) the minimum desired power levels at the UE. The impact of the AP antenna gain tunability on the RIS placement selection is assessed and illustrated in D-band indoor scenarios.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126760574","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}
The EU data strategy postulates that by 2025 there will be a paradigm shift towards more decentralized intelligence and data processing at the edge. The convergence of a large number of nodes at the IoT edge along with multiple service providers and network operators exposes data owners and resource providers to potential threats. To address cloud-edge risks, trust-based decentralized management is needed. Blockchain technology has created an opportunity to decentralize IoT ecosystems, through its intrinsic properties and together with machine learning (ML) it can be used to provide a trusted backbone for managing IoT ecosystems to support automated and adaptive trust management. This paper presents a novel approach for crosslayer intelligent trust computation modelling leveraging ML and Blockchain for decentralized trust management in IoT ecosystems. The effectiveness of the proposed approach for flow-based trust assessment is demonstrated using the Hyperledger Framework and the Cooja-based simulation environment. Finally, an initial evaluation is presented to understand the performance in terms of scalability and trust convergence of the proposed model.
{"title":"The convergence of Blockchain and Machine Learning for Decentralized Trust Management in IoT Ecosystems","authors":"T. Ranathunga, A. Mcgibney, S. Rea","doi":"10.1145/3485730.3493375","DOIUrl":"https://doi.org/10.1145/3485730.3493375","url":null,"abstract":"The EU data strategy postulates that by 2025 there will be a paradigm shift towards more decentralized intelligence and data processing at the edge. The convergence of a large number of nodes at the IoT edge along with multiple service providers and network operators exposes data owners and resource providers to potential threats. To address cloud-edge risks, trust-based decentralized management is needed. Blockchain technology has created an opportunity to decentralize IoT ecosystems, through its intrinsic properties and together with machine learning (ML) it can be used to provide a trusted backbone for managing IoT ecosystems to support automated and adaptive trust management. This paper presents a novel approach for crosslayer intelligent trust computation modelling leveraging ML and Blockchain for decentralized trust management in IoT ecosystems. The effectiveness of the proposed approach for flow-based trust assessment is demonstrated using the Hyperledger Framework and the Cooja-based simulation environment. Finally, an initial evaluation is presented to understand the performance in terms of scalability and trust convergence of the proposed model.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036391","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}
We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.
{"title":"Device or User: Rethinking Federated Learning in Personal-Scale Multi-Device Environments","authors":"Hyunsung Cho, Akhil Mathur, F. Kawsar","doi":"10.1145/3485730.3493449","DOIUrl":"https://doi.org/10.1145/3485730.3493449","url":null,"abstract":"We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127511539","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}
Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.
{"title":"Vision Paper: Towards Software-Defined Video Analytics with Cross-Camera Collaboration","authors":"Juheon Yi, Chulhong Min, F. Kawsar","doi":"10.1145/3485730.3493453","DOIUrl":"https://doi.org/10.1145/3485730.3493453","url":null,"abstract":"Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875243","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}
Fan Yang, A. Thangarajan, Sam Michiels, W. Joosen, D. Hughes
Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.
{"title":"Morphy","authors":"Fan Yang, A. Thangarajan, Sam Michiels, W. Joosen, D. Hughes","doi":"10.1145/3485730.3485947","DOIUrl":"https://doi.org/10.1145/3485730.3485947","url":null,"abstract":"Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116875955","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}
The ECCO-Box is a framework to establish connectivity of heterogeneous devices and sensors and distribute computing on the computing continuum dynamically at runtime. The goal of the dissertation is to design an architecture concept and develop a working prototype as a proof of concept. There are several requirements to the application which have to be fulfilled and will be evaluated in the form of an experimental study in the end. The main use case of the application will be processing of sensor data and analysis with artificial intelligence in an industrial environment.
{"title":"ECCO-Box: An Edge Computing and Connectivity Framework","authors":"Jannik Blähser","doi":"10.1145/3485730.3492898","DOIUrl":"https://doi.org/10.1145/3485730.3492898","url":null,"abstract":"The ECCO-Box is a framework to establish connectivity of heterogeneous devices and sensors and distribute computing on the computing continuum dynamically at runtime. The goal of the dissertation is to design an architecture concept and develop a working prototype as a proof of concept. There are several requirements to the application which have to be fulfilled and will be evaluated in the form of an experimental study in the end. The main use case of the application will be processing of sensor data and analysis with artificial intelligence in an industrial environment.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114348822","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}
Wiebke Toussaint, Akhil Mathur, A. Ding, F. Kawsar
When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. Through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions.
{"title":"Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning","authors":"Wiebke Toussaint, Akhil Mathur, A. Ding, F. Kawsar","doi":"10.1145/3485730.3493448","DOIUrl":"https://doi.org/10.1145/3485730.3493448","url":null,"abstract":"When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. Through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371028","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}
Jian Xu, A. Bhattacharya, A. Balasubramanian, Donald E. Porter
Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application's Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.
{"title":"Sensor Virtualization for Efficient Sharing of Mobile and Wearable Sensors","authors":"Jian Xu, A. Bhattacharya, A. Balasubramanian, Donald E. Porter","doi":"10.1145/3485730.3493451","DOIUrl":"https://doi.org/10.1145/3485730.3493451","url":null,"abstract":"Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application's Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131598485","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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