Emmanuel Tuyishimire, A. Bagula, S. Rekhis, N. Boudriga
The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings.
{"title":"Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT","authors":"Emmanuel Tuyishimire, A. Bagula, S. Rekhis, N. Boudriga","doi":"10.3390/iot3010010","DOIUrl":"https://doi.org/10.3390/iot3010010","url":null,"abstract":"The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88463793","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}
Current network architectures such as Cloud computing are not adapted to provide an acceptable Quality of Service (QoS) to the large number of tiny devices that compose the Internet of Things (IoT)[...]
{"title":"Emerging Trends and Challenges in Fog and Edge Computing for the Internet of Things","authors":"Bastien Confais, B. Parrein","doi":"10.3390/iot3010009","DOIUrl":"https://doi.org/10.3390/iot3010009","url":null,"abstract":"Current network architectures such as Cloud computing are not adapted to provide an acceptable Quality of Service (QoS) to the large number of tiny devices that compose the Internet of Things (IoT)[...]","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76815296","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}
Rahul Agrahari, Matthew Nicholson, Clare Conran, H. Assem, John D. Kelleher
In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets.
{"title":"Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data","authors":"Rahul Agrahari, Matthew Nicholson, Clare Conran, H. Assem, John D. Kelleher","doi":"10.3390/iot3010008","DOIUrl":"https://doi.org/10.3390/iot3010008","url":null,"abstract":"In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82427884","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}
Rigorous peer-reviews are the basis of high-quality academic publishing [...]
严格的同行评议是高质量学术出版的基础[…]
{"title":"Acknowledgment to Reviewers of IoT in 2021","authors":"","doi":"10.3390/iot3010007","DOIUrl":"https://doi.org/10.3390/iot3010007","url":null,"abstract":"Rigorous peer-reviews are the basis of high-quality academic publishing [...]","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80186367","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}
Suzanne K. Thomas, A. Pockett, K. Seunarine, Michael Spence, D. Raptis, S. Meroni, T. Watson, Matt Jones, Matthew J. Carnie
The number of interconnected devices, often referred to as the Internet of Things (IoT), is increasing at a considerable rate. It is inevitable therefore that so too will the energy demand. IoT describes a range of technologies such as sensors, software, smart meters, wearable devices, and communication beacons for the purpose of connecting and exchanging data with other devices and systems over the internet. Often not located near a mains supply power source, these devices may be reliant on primary battery cells. To avoid the need to periodically replace these batteries, it makes sense to integrate the technologies with a photovoltaic (PV) cell to harvest ambient light, so that the technologies can be said to be self-powered. Perovskite solar cells have proven extremely efficient in low-light conditions but in the absence of ambient and low-light testing standards, or even a consensus on what is defined by “ambient light”, it is difficult to estimate the energy yield of a given PV technology in a given scenario. Ambient light harvesting is complex, subject to spectral considerations, and whether the light source is directly incident on the PV cell. Here, we present a realistic scenario-driven method for measuring the energy yield for a given PV technology in various situations in which an IoT device may be found. Furthermore, we show that laboratory-built p-i-n perovskite devices, for many scenarios, produce energy yields close to that of commercial GaAs solar cells. Finally, we demonstrate an IoT device, powered by a mesoporous carbon perovskite solar module and supercapacitor, and operating through several day–night cycles.
{"title":"Will the Internet of Things Be Perovskite Powered? Energy Yield Measurement and Real-World Performance of Perovskite Solar Cells in Ambient Light Conditions","authors":"Suzanne K. Thomas, A. Pockett, K. Seunarine, Michael Spence, D. Raptis, S. Meroni, T. Watson, Matt Jones, Matthew J. Carnie","doi":"10.3390/iot3010006","DOIUrl":"https://doi.org/10.3390/iot3010006","url":null,"abstract":"The number of interconnected devices, often referred to as the Internet of Things (IoT), is increasing at a considerable rate. It is inevitable therefore that so too will the energy demand. IoT describes a range of technologies such as sensors, software, smart meters, wearable devices, and communication beacons for the purpose of connecting and exchanging data with other devices and systems over the internet. Often not located near a mains supply power source, these devices may be reliant on primary battery cells. To avoid the need to periodically replace these batteries, it makes sense to integrate the technologies with a photovoltaic (PV) cell to harvest ambient light, so that the technologies can be said to be self-powered. Perovskite solar cells have proven extremely efficient in low-light conditions but in the absence of ambient and low-light testing standards, or even a consensus on what is defined by “ambient light”, it is difficult to estimate the energy yield of a given PV technology in a given scenario. Ambient light harvesting is complex, subject to spectral considerations, and whether the light source is directly incident on the PV cell. Here, we present a realistic scenario-driven method for measuring the energy yield for a given PV technology in various situations in which an IoT device may be found. Furthermore, we show that laboratory-built p-i-n perovskite devices, for many scenarios, produce energy yields close to that of commercial GaAs solar cells. Finally, we demonstrate an IoT device, powered by a mesoporous carbon perovskite solar module and supercapacitor, and operating through several day–night cycles.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76086981","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 : 2022-01-01DOI: 10.1007/978-3-030-79496-5_31
O. Pichkov, A. A. Ulanov, Olga S. Vinokurova
{"title":"How Does Green Robotics Differ from Conventional Robotics: Comparative Analysis","authors":"O. Pichkov, A. A. Ulanov, Olga S. Vinokurova","doi":"10.1007/978-3-030-79496-5_31","DOIUrl":"https://doi.org/10.1007/978-3-030-79496-5_31","url":null,"abstract":"","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74041047","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 : 2022-01-01DOI: 10.1007/978-3-030-79496-5_25
Vitaly V. Demidov
{"title":"Not for “Green”: Risks of Inflating a “New-Bubble” by Applying Traditional Financial Mechanisms","authors":"Vitaly V. Demidov","doi":"10.1007/978-3-030-79496-5_25","DOIUrl":"https://doi.org/10.1007/978-3-030-79496-5_25","url":null,"abstract":"","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77570219","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 : 2022-01-01DOI: 10.1007/978-3-030-79496-5_42
O. Karpovich, Murat A. Bulgarov, Nelia Deberdeeva, E. Abashin
{"title":"Mechanism of Implementation of “Green” Investment-Innovative Initiatives in “Smart” Production Under the Control of Artificial Intelligence in the Interests of Environmental Safety of the Region","authors":"O. Karpovich, Murat A. Bulgarov, Nelia Deberdeeva, E. Abashin","doi":"10.1007/978-3-030-79496-5_42","DOIUrl":"https://doi.org/10.1007/978-3-030-79496-5_42","url":null,"abstract":"","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81219670","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 : 2022-01-01DOI: 10.1007/978-3-030-79496-5_32
Jeff Schubert
{"title":"Managing Industry 4 Technology and Innovation","authors":"Jeff Schubert","doi":"10.1007/978-3-030-79496-5_32","DOIUrl":"https://doi.org/10.1007/978-3-030-79496-5_32","url":null,"abstract":"","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78497425","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 : 2022-01-01DOI: 10.1007/978-3-030-79496-5_13
E. Khartukov, Ellen E. Starostina
{"title":"Brazil’s Green Energy: Today and Tomorrow","authors":"E. Khartukov, Ellen E. Starostina","doi":"10.1007/978-3-030-79496-5_13","DOIUrl":"https://doi.org/10.1007/978-3-030-79496-5_13","url":null,"abstract":"","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"419 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78158854","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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