B. Sudharsan, Dhruv Sheth, Shailesh Arya, Federica Rollo, Piyush Yadav, Pankesh Patel, John G. Breslin, M. Ali
Transmitting updates of high-dimensional models between client IoT devices and the central aggregating server has always been a bottleneck in collaborative learning - especially in uncertain real-world IoT networks where congestion, latency, bandwidth issues are common. In this scenario, gradient quantization is an effective way to reduce bits count when transmitting each model update, but with a trade-off of having an elevated error floor due to higher variance of the stochastic gradients. In this paper, we propose ElastiCL, an elastic quantization strategy that achieves transmission efficiency plus a low error floor by dynamically altering the number of quantization levels during training on distributed IoT devices. Experiments on training ResNet-18, Vanilla CNN shows that ElastiCL can converge in much fewer transmitted bits than fixed quantization level, with little or no compromise on training and test accuracy.
{"title":"ElastiCL","authors":"B. Sudharsan, Dhruv Sheth, Shailesh Arya, Federica Rollo, Piyush Yadav, Pankesh Patel, John G. Breslin, M. Ali","doi":"10.1145/3485730.3492885","DOIUrl":"https://doi.org/10.1145/3485730.3492885","url":null,"abstract":"Transmitting updates of high-dimensional models between client IoT devices and the central aggregating server has always been a bottleneck in collaborative learning - especially in uncertain real-world IoT networks where congestion, latency, bandwidth issues are common. In this scenario, gradient quantization is an effective way to reduce bits count when transmitting each model update, but with a trade-off of having an elevated error floor due to higher variance of the stochastic gradients. In this paper, we propose ElastiCL, an elastic quantization strategy that achieves transmission efficiency plus a low error floor by dynamically altering the number of quantization levels during training on distributed IoT devices. Experiments on training ResNet-18, Vanilla CNN shows that ElastiCL can converge in much fewer transmitted bits than fixed quantization level, with little or no compromise on training and test accuracy.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"15 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":"133681123","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}
While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location.
{"title":"Thermal Energy Harvesting Profiles in Residential Settings","authors":"V. Sobral, J. Lach, J. Goodall, Bradford Campbell","doi":"10.1145/3485730.3494111","DOIUrl":"https://doi.org/10.1145/3485730.3494111","url":null,"abstract":"While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"58 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":"116630579","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}
Next-generation wireless experimentation benefits from new large-scale open-access software defined radio (SDR) platforms. Each SDR's transmissions must be measured and monitored to guarantee spectrum compliance. The measured spectrum is, however, corrupted by external co-channel signals. This demo presents the Bidirectional Incident/Transmit Signal Separator (BITSS), a system which estimates the linear system model, the SDR's transmit signal, and the signals from other sources incident to the antenna, all on the fly and without a signal prior or system information. We implement and run BITSS on POWDER and evaluate its performance. The demo shows that BITSS enables separation over a range of signal parameters with high accuracy and alerts users and the operator whenever a spectrum violation occurs.
{"title":"A Compliance Monitoring System for Open SDR Platforms","authors":"Jie Wang, J. V. D. Merwe, Neal Patwari","doi":"10.1145/3485730.3492884","DOIUrl":"https://doi.org/10.1145/3485730.3492884","url":null,"abstract":"Next-generation wireless experimentation benefits from new large-scale open-access software defined radio (SDR) platforms. Each SDR's transmissions must be measured and monitored to guarantee spectrum compliance. The measured spectrum is, however, corrupted by external co-channel signals. This demo presents the Bidirectional Incident/Transmit Signal Separator (BITSS), a system which estimates the linear system model, the SDR's transmit signal, and the signals from other sources incident to the antenna, all on the fly and without a signal prior or system information. We implement and run BITSS on POWDER and evaluate its performance. The demo shows that BITSS enables separation over a range of signal parameters with high accuracy and alerts users and the operator whenever a spectrum violation occurs.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"3 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":"122237032","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}
Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.
{"title":"Throughput Maximization in Low-Power IoT Networks via Tuning the Size of the TSCH Slotframe","authors":"Omid Tavallaie, J. Taheri, Albert Y. Zomaya","doi":"10.1145/3485730.3492894","DOIUrl":"https://doi.org/10.1145/3485730.3492894","url":null,"abstract":"Time-Slotted Channel Hopping (TSCH) was standardized as a part of IEEE 802.15.4e to address the strict reliability and timeliness requirements of low-power Internet of Things (IoT) applications. Setting the size of the TSCH slotframe has a considerable effect on the performance of scheduling algorithms used in IoT networks. Although IETF and IEEE standards define general mechanisms for communication of TSCH nodes, finding the optimal size of the TSCH slotframe has been left open and unresolved. In this poster, we propose an algorithm called S-TSCH to find the optimal size of the TSCH slotframe for maximizing network throughput based on 1) the number of nodes placed in the topology, 2) the data generation rate of applications running on IoT nodes, 3) and the maximum rate of generating TSCH/RPL control packets. To evaluate the performance of our contribution, we implement S-TSCH on Zolerita Firefly IoT motes and the Contiki-NG operating system. Evaluation results show that our proposed method improves the performance of distributed TSCH scheduling algorithms in terms of reliability and delay.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"59 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":"123898316","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}
Matias Quintana, Mahmoud Abdelrahman, Mario Frei, F. Tartarini, Clayton Miller
Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable resource to better understand how people perceive their environment. Moreover, they provide a more realistic scenario to those conducted in thermal chambers. Our objective was to share publicly a longitudinal dataset comprising data collected over a 4-week long experiment. A total of 17 participants completed thermal preferences surveys which accounted for a total of approximately 1400 unique responses across indoor and outdoor 17 spaces. For the whole duration of the study, we monitored environmental variables (e.g., temperature and relative humidity) throughout 3 buildings. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Their indoor location was continuously monitored using a custom-designed smartphone application. Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics and personality traits (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits), was collected using an on-boarding survey administered at the beginning of the experiment. The dataset is available at https://zenodo.org/record/5502441#.YT7xyaARUTs.
{"title":"Longitudinal personal thermal comfort preference data in the wild","authors":"Matias Quintana, Mahmoud Abdelrahman, Mario Frei, F. Tartarini, Clayton Miller","doi":"10.1145/3485730.3493693","DOIUrl":"https://doi.org/10.1145/3485730.3493693","url":null,"abstract":"Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable resource to better understand how people perceive their environment. Moreover, they provide a more realistic scenario to those conducted in thermal chambers. Our objective was to share publicly a longitudinal dataset comprising data collected over a 4-week long experiment. A total of 17 participants completed thermal preferences surveys which accounted for a total of approximately 1400 unique responses across indoor and outdoor 17 spaces. For the whole duration of the study, we monitored environmental variables (e.g., temperature and relative humidity) throughout 3 buildings. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Their indoor location was continuously monitored using a custom-designed smartphone application. Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics and personality traits (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits), was collected using an on-boarding survey administered at the beginning of the experiment. The dataset is available at https://zenodo.org/record/5502441#.YT7xyaARUTs.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"73 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":"126295645","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}
Tara Boroushaki, I. Perper, Mergen Nachin, Alberto Rodriguez, Fadel M. Adib
We present the design, implementation, and evaluation of RFusion, a robotic system that can search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. RFusion consists of a robotic arm that has a camera and antenna strapped around its gripper. Our design introduces two key innovations: the first is a method that geometrically fuses RF and visual information to reduce uncertainty about the target object's location, even when the item is fully occluded. The second is a novel reinforcement-learning network that uses the fused RF-visual information to efficiently localize, maneuver toward, and grasp target items. We built an end-to-end prototype of RFusion and tested it in challenging real-world environments. Our evaluation demonstrates that RFusion localizes target items with centimeter-scale accuracy and achieves 96% success rate in retrieving fully occluded objects, even if they are under a pile. The system paves the way for novel robotic retrieval tasks in complex environments such as warehouses, manufacturing plants, and smart homes.
{"title":"RFusion","authors":"Tara Boroushaki, I. Perper, Mergen Nachin, Alberto Rodriguez, Fadel M. Adib","doi":"10.1145/3485730.3485944","DOIUrl":"https://doi.org/10.1145/3485730.3485944","url":null,"abstract":"We present the design, implementation, and evaluation of RFusion, a robotic system that can search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. RFusion consists of a robotic arm that has a camera and antenna strapped around its gripper. Our design introduces two key innovations: the first is a method that geometrically fuses RF and visual information to reduce uncertainty about the target object's location, even when the item is fully occluded. The second is a novel reinforcement-learning network that uses the fused RF-visual information to efficiently localize, maneuver toward, and grasp target items. We built an end-to-end prototype of RFusion and tested it in challenging real-world environments. Our evaluation demonstrates that RFusion localizes target items with centimeter-scale accuracy and achieves 96% success rate in retrieving fully occluded objects, even if they are under a pile. The system paves the way for novel robotic retrieval tasks in complex environments such as warehouses, manufacturing plants, and smart homes.","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":"120952931","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}
Chenning Li, Hanqing Guo, Shuai Tong, Xiao Zeng, Zhichao Cao, Mi Zhang, Qiben Yan, Li Xiao, Jiliang Wang, Yunhao Liu
Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.
{"title":"NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation","authors":"Chenning Li, Hanqing Guo, Shuai Tong, Xiao Zeng, Zhichao Cao, Mi Zhang, Qiben Yan, Li Xiao, Jiliang Wang, Yunhao Liu","doi":"10.1145/3485730.3485928","DOIUrl":"https://doi.org/10.1145/3485730.3485928","url":null,"abstract":"Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"12 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":"122439719","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}
Time-of-flight (ToF) depth cameras have been increasingly adopted in various real-world applications, e.g., used with RGB cameras for advanced computer vision tasks like 3-D mapping or deployed alone in privacy-sensitive applications such as sleep monitoring. In this paper, we propose UltraDepth, the first system that can expose high-resolution texture from depth maps captured by off-the-shelf ToF cameras, simply by introducing a distorting IR source. The exposed texture information can significantly augment depth-based applications. Moreover, such a capability can be used to launch privacy attacks, which poses a major concern due to the prominence of ToF cameras. To design UltraDepth, we present an in-depth analysis on the impact of the distorting IR light on the distance measurement. We further show that, the reflection properties (reflectivity and incidence angle) of the objects will be encoded in the distorted depth map and hence can be leveraged to reveal texture of objects in UltraDepth. We then propose two practical implementations of UltraDepth, i.e., reflection-based and external IR-based implementations. Our extensive real-world experiments show that, the depth maps output by UltraDepth achieve 89.06%, 99.33%, 81.25% mean accuracy in object detection, face recognition and character recognition, respectively, which offers over 10x improvement over the ordinary depth maps and even approaches the performance of RGB and IR images in a number of scenarios. The findings of this work provide key insights for new research on depth-related computer vision and security of depth sensing devices.
{"title":"UltraDepth","authors":"Zhiyuan Xie, Xiaomin Ouyang, Xiaoming Liu, Guoliang Xing","doi":"10.1145/3485730.3485927","DOIUrl":"https://doi.org/10.1145/3485730.3485927","url":null,"abstract":"Time-of-flight (ToF) depth cameras have been increasingly adopted in various real-world applications, e.g., used with RGB cameras for advanced computer vision tasks like 3-D mapping or deployed alone in privacy-sensitive applications such as sleep monitoring. In this paper, we propose UltraDepth, the first system that can expose high-resolution texture from depth maps captured by off-the-shelf ToF cameras, simply by introducing a distorting IR source. The exposed texture information can significantly augment depth-based applications. Moreover, such a capability can be used to launch privacy attacks, which poses a major concern due to the prominence of ToF cameras. To design UltraDepth, we present an in-depth analysis on the impact of the distorting IR light on the distance measurement. We further show that, the reflection properties (reflectivity and incidence angle) of the objects will be encoded in the distorted depth map and hence can be leveraged to reveal texture of objects in UltraDepth. We then propose two practical implementations of UltraDepth, i.e., reflection-based and external IR-based implementations. Our extensive real-world experiments show that, the depth maps output by UltraDepth achieve 89.06%, 99.33%, 81.25% mean accuracy in object detection, face recognition and character recognition, respectively, which offers over 10x improvement over the ordinary depth maps and even approaches the performance of RGB and IR images in a number of scenarios. The findings of this work provide key insights for new research on depth-related computer vision and security of depth sensing devices.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"53 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":"122796958","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}
Andreas Biri, Reto Da Forno, Tonio Gsell, Tobias Gatschet, J. Beutel, Lothar Thiele
Voor achtergronden, toelichting en het tot stand komen van dit stappenplan wordt u verwezen naar de algemene toelichting van de VSI en de verantwoording. Dit stappenplan is een aanvulling op de LCIrichtlijn E. coli (shigatoxineproducerende E. coli-infectie, STEC). De LCI spreekt zich niet uit over de taakverdeling tussen disciplines bij de uitvoering van de verschillende stappen. Daarvoor zijn de interne werkafspraken van de betreffende GGD leidend.
{"title":"STeC","authors":"Andreas Biri, Reto Da Forno, Tonio Gsell, Tobias Gatschet, J. Beutel, Lothar Thiele","doi":"10.1145/3485730.3485951","DOIUrl":"https://doi.org/10.1145/3485730.3485951","url":null,"abstract":"Voor achtergronden, toelichting en het tot stand komen van dit stappenplan wordt u verwezen naar de algemene toelichting van de VSI en de verantwoording. Dit stappenplan is een aanvulling op de LCIrichtlijn E. coli (shigatoxineproducerende E. coli-infectie, STEC). De LCI spreekt zich niet uit over de taakverdeling tussen disciplines bij de uitvoering van de verschillende stappen. Daarvoor zijn de interne werkafspraken van de betreffende GGD leidend.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"9 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":"115272751","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}
Adit Goyal, Anubhav Elhence, V. Chamola, B. Sikdar
Having a health insurance is important for everybody, bearing in mind the increasing medical costs. Medical emergencies can have a severe financial and emotional impact. However, the current insurance system is very expensive and the claim settlement process is excessively lengthy, making it tedious. This results in policyholders not being able to successfully make a claim with their insurance company. In this paper, we focus on developing a fast and cost-effective framework based on blockchain technology and machine learning for the health insurance industry. Blockchain is capable of removing all third-party organisations by forming a smart contract, making the entire process more smooth, secure, and efficient. The contract settles the claim on documents submitted by the claimant. A ridge regression model is used for computing the premiums optimally, based on the total amount claimed under the current policy tenure, along with several other factors. A random forest classifier is applied for predicting the risk that helps in the computation of risk-rated premium rebate.
{"title":"A Blockchain and Machine Learning based Framework for Efficient Health Insurance Management","authors":"Adit Goyal, Anubhav Elhence, V. Chamola, B. Sikdar","doi":"10.1145/3485730.3493685","DOIUrl":"https://doi.org/10.1145/3485730.3493685","url":null,"abstract":"Having a health insurance is important for everybody, bearing in mind the increasing medical costs. Medical emergencies can have a severe financial and emotional impact. However, the current insurance system is very expensive and the claim settlement process is excessively lengthy, making it tedious. This results in policyholders not being able to successfully make a claim with their insurance company. In this paper, we focus on developing a fast and cost-effective framework based on blockchain technology and machine learning for the health insurance industry. Blockchain is capable of removing all third-party organisations by forming a smart contract, making the entire process more smooth, secure, and efficient. The contract settles the claim on documents submitted by the claimant. A ridge regression model is used for computing the premiums optimally, based on the total amount claimed under the current policy tenure, along with several other factors. A random forest classifier is applied for predicting the risk that helps in the computation of risk-rated premium rebate.","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":"131001860","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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