Pub Date : 2020-08-01DOI: 10.1109/COINS49042.2020.9191663
Dongjoo Seo, Sina Shahhosseini, Milad Asgari Mehrabadi, Bryan Donyanavard, Sung-Soo Lim, A. Rahmani, N. Dutt
Complex Internet of Things (IoT) applications such as Healthcare IoT include a variety of compute, data, and communication kernel intensities and have diverse sensitivities of QoS requirements including latency, throughput, availability, accuracy, etc. Ensuring QoS requirements for the applications requires a comprehensive tool to perform efficient full-stack analysis. Per our observation, the literature lacks a simulator capable of supporting a full-stack communication-computation co-simulation of an IoT system. Furthermore, IoT system behavior can dramatically change during run-time due to variation in status and context. Therefore, such a system must be dynamically controlled over time. In this paper, for the first time, we propose a full-stack framework to co-simulate communication and computation aspects of an IoT system in a dynamic scenario. We integrate a Transmission Control Protocol (TCP) latency model with the iFogSim simulator. We conduct a health-care IoT-based case study to evaluate the framework. The framework is open-sourced and available on GitHub in the following repository: https://github.com/HealthSciTech/Dynamic iFogSim.
{"title":"Dynamic iFogSim: A Framework for Full-Stack Simulation of Dynamic Resource Management in IoT Systems","authors":"Dongjoo Seo, Sina Shahhosseini, Milad Asgari Mehrabadi, Bryan Donyanavard, Sung-Soo Lim, A. Rahmani, N. Dutt","doi":"10.1109/COINS49042.2020.9191663","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191663","url":null,"abstract":"Complex Internet of Things (IoT) applications such as Healthcare IoT include a variety of compute, data, and communication kernel intensities and have diverse sensitivities of QoS requirements including latency, throughput, availability, accuracy, etc. Ensuring QoS requirements for the applications requires a comprehensive tool to perform efficient full-stack analysis. Per our observation, the literature lacks a simulator capable of supporting a full-stack communication-computation co-simulation of an IoT system. Furthermore, IoT system behavior can dramatically change during run-time due to variation in status and context. Therefore, such a system must be dynamically controlled over time. In this paper, for the first time, we propose a full-stack framework to co-simulate communication and computation aspects of an IoT system in a dynamic scenario. We integrate a Transmission Control Protocol (TCP) latency model with the iFogSim simulator. We conduct a health-care IoT-based case study to evaluate the framework. The framework is open-sourced and available on GitHub in the following repository: https://github.com/HealthSciTech/Dynamic iFogSim.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127660319","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191416
Robert Brylka, Ulrich Schwanecke, Benjamin Bierwirth
In the last decades, many approaches were presented to localize and decode barcodes in images from off the shelf cameras. However, all proposed solutions usually only deal with one type of image artifacts, such as a poorly illuminated or noisy image, or an image that suffers from motion or out-of-focus blur. In this paper, we present a complete, fully automatic pipeline, which allows the localization and decoding of barcodes in real-world scenarios. Our method is capable of localization and decoding the correct barcode information even if the input image is noisy, poorly exposed, and blurred at the same time. We can also decode the correct information from barcode images whose resolution is actually too low, i.e., where the width of the smallest bar depicted is smaller than the width of a single pixel.
{"title":"Camera Based Barcode Localization and Decoding in Real-World Applications","authors":"Robert Brylka, Ulrich Schwanecke, Benjamin Bierwirth","doi":"10.1109/COINS49042.2020.9191416","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191416","url":null,"abstract":"In the last decades, many approaches were presented to localize and decode barcodes in images from off the shelf cameras. However, all proposed solutions usually only deal with one type of image artifacts, such as a poorly illuminated or noisy image, or an image that suffers from motion or out-of-focus blur. In this paper, we present a complete, fully automatic pipeline, which allows the localization and decoding of barcodes in real-world scenarios. Our method is capable of localization and decoding the correct barcode information even if the input image is noisy, poorly exposed, and blurred at the same time. We can also decode the correct information from barcode images whose resolution is actually too low, i.e., where the width of the smallest bar depicted is smaller than the width of a single pixel.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129213111","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 : 2020-08-01DOI: 10.1109/coins49042.2020.9191419
{"title":"COINS 2020 Breaker Page","authors":"","doi":"10.1109/coins49042.2020.9191419","DOIUrl":"https://doi.org/10.1109/coins49042.2020.9191419","url":null,"abstract":"","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097641","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 : 2020-08-01DOI: 10.1109/coins49042.2020.9191424
{"title":"COINS 2020 Cover Page","authors":"","doi":"10.1109/coins49042.2020.9191424","DOIUrl":"https://doi.org/10.1109/coins49042.2020.9191424","url":null,"abstract":"","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125814622","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 : 2020-08-01DOI: 10.1109/coins49042.2020.9191431
{"title":"COINS 2020 TOC","authors":"","doi":"10.1109/coins49042.2020.9191431","DOIUrl":"https://doi.org/10.1109/coins49042.2020.9191431","url":null,"abstract":"","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078193","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191420
Emanuel Regnath, N. Shivaraman, Shanker Shreejith, A. Easwaran, S. Steinhorst
Time synchronization among IoT devices is a fundamental requirement for efficient and reliable communication on a global scale. Common synchronization schemes such as NTP operate on a trust-based client-server model, which does not scale well in a decentralized network because single server failures can lead to a severe downtime before re-establishing synchronization. Public blockchains such as Ethereum provide a trustless network and tamper-proof time-stamped data that is freely available. In this paper, we leverage the availability of time information in the block headers, which are very small (several hundreds of bytes) compared to the full blocks and can be validated without participation in the mining process. Our approach uses two estimators that are fed with the timestamps from block headers as well as the elapsed time between consecutive block receptions to estimate the true time to an accuracy of one second. We evaluate our approach by extensive validation on blockchain data from different geographical locations across the globe and show that global synchronization can be established despite the non-deterministic behavior of blockchains such as mining difficulty, network latencies and forks.
{"title":"Blockchain, what time is it? Trustless Datetime Synchronization for IoT","authors":"Emanuel Regnath, N. Shivaraman, Shanker Shreejith, A. Easwaran, S. Steinhorst","doi":"10.1109/COINS49042.2020.9191420","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191420","url":null,"abstract":"Time synchronization among IoT devices is a fundamental requirement for efficient and reliable communication on a global scale. Common synchronization schemes such as NTP operate on a trust-based client-server model, which does not scale well in a decentralized network because single server failures can lead to a severe downtime before re-establishing synchronization. Public blockchains such as Ethereum provide a trustless network and tamper-proof time-stamped data that is freely available. In this paper, we leverage the availability of time information in the block headers, which are very small (several hundreds of bytes) compared to the full blocks and can be validated without participation in the mining process. Our approach uses two estimators that are fed with the timestamps from block headers as well as the elapsed time between consecutive block receptions to estimate the true time to an accuracy of one second. We evaluate our approach by extensive validation on blockchain data from different geographical locations across the globe and show that global synchronization can be established despite the non-deterministic behavior of blockchains such as mining difficulty, network latencies and forks.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134537523","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191382
Nuno Figueiredo, M. Caeiro
Trustworthiness is a soft-security feature that evaluates the correct behavior of nodes in a network. More specifically, this feature tries to answer the following question: how much should we trust in a certain node? To determine the trustworthiness of a node, our approach focuses on two reputation indicators: the self-data trust, which evaluates the data generated by the node itself taking into account its historical data; and the peer-data trust, which utilizes the nearest nodes’ data. In this paper, we show how these two indicators can be calculated using the Gaussian Overlap and Pearson correlation. This paper includes a validation of our trustworthiness approach using real data from unofficial and official weather stations in Portugal. This is a representative scenario of the current situation in many other areas, with different entities providing different kinds of data using autonomous sensors in a continuous way over the networks.
{"title":"Trustworthiness in Sensor Networks A Reputation-Based Method for Weather Stations","authors":"Nuno Figueiredo, M. Caeiro","doi":"10.1109/COINS49042.2020.9191382","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191382","url":null,"abstract":"Trustworthiness is a soft-security feature that evaluates the correct behavior of nodes in a network. More specifically, this feature tries to answer the following question: how much should we trust in a certain node? To determine the trustworthiness of a node, our approach focuses on two reputation indicators: the self-data trust, which evaluates the data generated by the node itself taking into account its historical data; and the peer-data trust, which utilizes the nearest nodes’ data. In this paper, we show how these two indicators can be calculated using the Gaussian Overlap and Pearson correlation. This paper includes a validation of our trustworthiness approach using real data from unofficial and official weather stations in Portugal. This is a representative scenario of the current situation in many other areas, with different entities providing different kinds of data using autonomous sensors in a continuous way over the networks.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133013645","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191412
Aaqib Saeed, Ye Li, T. Ozcelebi, J. Lukkien
Data augmentation is a crucial technique for effectively learning deep models and for improving their generalization. It has shown remarkable performance gains on complex sets of problems, such as object detection and image classification. However, for sensor (time-series) data, its potential is not thoroughly explored even though the acquisition of large annotated sensor datasets is prohibitively expensive and challenging in real-life. In this work, we propose Sensor Augment - a generalized framework for automatically discovering data-specific augmentation strategies with black-box optimization search algorithms. Our approach makes use of the user-defined transformations to discover an optimal combination of the operations that can be used to train deep networks for a wide variety of tasks. Besides, we propose several augmentation operations that can be used to generate synthetic data and enrich the search space while harnessing existing functions. We show the efficacy of learned augmentation strategies on 7 multi-sensor datasets for 4 complex tasks. In our experiments, we see a substantial performance gain ranging from 1.5 to 10 F-score points over the baseline. We also show that the strategies can be learned from smaller subsets, and they can transfer well between related datasets.
{"title":"Multi-sensor data augmentation for robust sensing","authors":"Aaqib Saeed, Ye Li, T. Ozcelebi, J. Lukkien","doi":"10.1109/COINS49042.2020.9191412","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191412","url":null,"abstract":"Data augmentation is a crucial technique for effectively learning deep models and for improving their generalization. It has shown remarkable performance gains on complex sets of problems, such as object detection and image classification. However, for sensor (time-series) data, its potential is not thoroughly explored even though the acquisition of large annotated sensor datasets is prohibitively expensive and challenging in real-life. In this work, we propose Sensor Augment - a generalized framework for automatically discovering data-specific augmentation strategies with black-box optimization search algorithms. Our approach makes use of the user-defined transformations to discover an optimal combination of the operations that can be used to train deep networks for a wide variety of tasks. Besides, we propose several augmentation operations that can be used to generate synthetic data and enrich the search space while harnessing existing functions. We show the efficacy of learned augmentation strategies on 7 multi-sensor datasets for 4 complex tasks. In our experiments, we see a substantial performance gain ranging from 1.5 to 10 F-score points over the baseline. We also show that the strategies can be learned from smaller subsets, and they can transfer well between related datasets.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125565206","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191411
Mehrdad Poorhosseini, W. Nebel, Kim Grüttner
The GNU Compiler Collection (GCC) is the traditional compiler for most embedded systems, since it supports many different instruction set architectures (ISA) in its back-end. GCC has also been the first compiler that supported the RISC-V ISA. Since a while Clang/LLVM has gained more and more interest in the embedded software community. Recently, RISC-V is also supported in the LLVM back-end and maintained in the official LLVM release. In this paper we propose a benchmark environment for the comparison of compilers in the RISC-V ecosystem. We perform a comparison of GCC against LLVM for an embedded software benchmark considering compile time, size of the resulting binary, number of instructions and execution time. The results show that LLVM compiles faster in 88% of the experiments, while GCC and LLVM produce nearly the same binary size in 51% of the experiments. In 37% GCC wins and in 12% LLVM wins. In 94% of the experiments the difference between the resulting binary size in GCC and LLVM is +/-5%. The execution time analysis shows that in 42% of the experiments GCC and LLVM have nearly the same execution time clock cycles while in 40% GCC wins and in 18% LLVM wins.
{"title":"A Compiler Comparison in the RISC-V Ecosystem","authors":"Mehrdad Poorhosseini, W. Nebel, Kim Grüttner","doi":"10.1109/COINS49042.2020.9191411","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191411","url":null,"abstract":"The GNU Compiler Collection (GCC) is the traditional compiler for most embedded systems, since it supports many different instruction set architectures (ISA) in its back-end. GCC has also been the first compiler that supported the RISC-V ISA. Since a while Clang/LLVM has gained more and more interest in the embedded software community. Recently, RISC-V is also supported in the LLVM back-end and maintained in the official LLVM release. In this paper we propose a benchmark environment for the comparison of compilers in the RISC-V ecosystem. We perform a comparison of GCC against LLVM for an embedded software benchmark considering compile time, size of the resulting binary, number of instructions and execution time. The results show that LLVM compiles faster in 88% of the experiments, while GCC and LLVM produce nearly the same binary size in 51% of the experiments. In 37% GCC wins and in 12% LLVM wins. In 94% of the experiments the difference between the resulting binary size in GCC and LLVM is +/-5%. The execution time analysis shows that in 42% of the experiments GCC and LLVM have nearly the same execution time clock cycles while in 40% GCC wins and in 18% LLVM wins.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127241614","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 : 2020-08-01DOI: 10.1109/COINS49042.2020.9191405
L. Gerrits, Roland Kromes, F. Verdier
In this paper, we will present a new model of distributed ledger-based IoT network, in which we combined Hyperledger Sawtooth as blockchain with Inter Planetary File System (IPFS) as a distributed storage system. The combination of these two types of distributed ledger technologies can allow more efficient data storage than in other blockchain implementations. This work was initiated by the automotive manufacturer Renault based on the idea of a new ecosystem of smart vehicles containing IoT devices. We will focus on an accident use case. After the accident, the cars send their data to a dedicated smart contract. We will also describe furthermore our implementation, the characteristics of Hyperledger Sawtooth and IPFS and finally, we demonstrate the realistic feasibility of this implementation by latency measurements.
{"title":"A True Decentralized Implementation Based on IoT and Blockchain: a Vehicle Accident Use Case","authors":"L. Gerrits, Roland Kromes, F. Verdier","doi":"10.1109/COINS49042.2020.9191405","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191405","url":null,"abstract":"In this paper, we will present a new model of distributed ledger-based IoT network, in which we combined Hyperledger Sawtooth as blockchain with Inter Planetary File System (IPFS) as a distributed storage system. The combination of these two types of distributed ledger technologies can allow more efficient data storage than in other blockchain implementations. This work was initiated by the automotive manufacturer Renault based on the idea of a new ecosystem of smart vehicles containing IoT devices. We will focus on an accident use case. After the accident, the cars send their data to a dedicated smart contract. We will also describe furthermore our implementation, the characteristics of Hyperledger Sawtooth and IPFS and finally, we demonstrate the realistic feasibility of this implementation by latency measurements.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126543109","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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