Pub Date : 2019-07-01DOI: 10.1109/IWMN.2019.8805041
A. Capozzoli, C. Curcio, A. Liseno
The paper deals with the multi-frequency characterization of antennas by using Near-Field measurements collected over one optimized grid common to all the frequencies. In this way the frequency measurements are carried out with a single scan. The approach provides a drastic reduction of the number of measurement points and uses a raster scan grid to take advantage of the classical continuous scan or of the new scanning capabilities shown in [6].
{"title":"Raster Scan Multi-Frequency Near-Field Antenna Characterization","authors":"A. Capozzoli, C. Curcio, A. Liseno","doi":"10.1109/IWMN.2019.8805041","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8805041","url":null,"abstract":"The paper deals with the multi-frequency characterization of antennas by using Near-Field measurements collected over one optimized grid common to all the frequencies. In this way the frequency measurements are carried out with a single scan. The approach provides a drastic reduction of the number of measurement points and uses a raster scan grid to take advantage of the classical continuous scan or of the new scanning capabilities shown in [6].","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729824","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 : 2019-07-01DOI: 10.1109/IWMN.2019.8804995
Jordi Zayuelas i Muñoz, J. Suárez-Varela, P. Barlet-Ros
In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets’ payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.
{"title":"Detecting cryptocurrency miners with NetFlow/IPFIX network measurements","authors":"Jordi Zayuelas i Muñoz, J. Suárez-Varela, P. Barlet-Ros","doi":"10.1109/IWMN.2019.8804995","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8804995","url":null,"abstract":"In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets’ payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122816092","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 : 2019-07-01DOI: 10.1109/IWMN.2019.8805037
S. Spinsante, A. Poli, S. Pirani, L. Gioacchini
A strong and positive association between increased levels of physical activity, exercise participation and improved health in older adults emerges from research studies. It is consequently relevant to measure not only the amount of physical exercise performed during planned sessions, but also the level of activity all over the daytime. This paper presents a wearable solution that exploits properly instrumented shoes and a low power Long Range communication interface, to measure the user’s activity levels in a minimally invasive way. The paper considers the features and limits of the Long Range transmission technology tested in mobility conditions, with a specific focus on the packet loss rate, to evaluate if they match with the requirements of a wearable solution designed to measure the subject’s physical activity levels.
{"title":"LoRa Evaluation in Mobility Conditions for a Connected Smart Shoe Measuring Physical Activity","authors":"S. Spinsante, A. Poli, S. Pirani, L. Gioacchini","doi":"10.1109/IWMN.2019.8805037","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8805037","url":null,"abstract":"A strong and positive association between increased levels of physical activity, exercise participation and improved health in older adults emerges from research studies. It is consequently relevant to measure not only the amount of physical exercise performed during planned sessions, but also the level of activity all over the daytime. This paper presents a wearable solution that exploits properly instrumented shoes and a low power Long Range communication interface, to measure the user’s activity levels in a minimally invasive way. The paper considers the features and limits of the Long Range transmission technology tested in mobility conditions, with a specific focus on the packet loss rate, to evaluate if they match with the requirements of a wearable solution designed to measure the subject’s physical activity levels.","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124548826","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 : 2019-07-01DOI: 10.1109/IWMN.2019.8805032
C. Carobbi, Andrea Guagagnoli
The accurate characterization of a broadband, compact and active antenna designed for use as a travelling sample for interlaboratory comparisons is described. Characterization is based on the combined use of measurement and simulations. The result of the characterization is the value and uncertainty of the generated electromagnetic field at different distances and in different electromagnetic environments (fully- and semi-anechoic). The dominant contribution to the uncertainty of the generated electromagnetic field is the mismatch between the output of the internal power source and the input of the radiating structure. Reduction of mismatch error cannot be easily achieved due to the physical limits imposed by the simultaneous requirements for a compact (small antenna size) and battery operated (low power source) travelling sample.
{"title":"The Combined use of Measurements and Simulations for the Low-Uncertainty Characterization of a Reference Source of Electromagnetic Field","authors":"C. Carobbi, Andrea Guagagnoli","doi":"10.1109/IWMN.2019.8805032","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8805032","url":null,"abstract":"The accurate characterization of a broadband, compact and active antenna designed for use as a travelling sample for interlaboratory comparisons is described. Characterization is based on the combined use of measurement and simulations. The result of the characterization is the value and uncertainty of the generated electromagnetic field at different distances and in different electromagnetic environments (fully- and semi-anechoic). The dominant contribution to the uncertainty of the generated electromagnetic field is the mismatch between the output of the internal power source and the input of the radiating structure. Reduction of mismatch error cannot be easily achieved due to the physical limits imposed by the simultaneous requirements for a compact (small antenna size) and battery operated (low power source) travelling sample.","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125110994","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 : 2019-07-01DOI: 10.1109/IWMN.2019.8805043
C. Trigona, B. Andò, S. Baglio
The possibility to take advantage of external and not deterministic vibrations is of great interest in the modern research including sensing elements and transducers. In particular piezoelectric transducers arouse interest in the international community with particular emphasis in the area of measurements and metrology; in fact the literature presents several examples of transducers based on piezoelectric materials but, very often, focusing the attention on a specific prototype able to save energy by using the active material (transducer for energy harvesting) or suitable to perform measurements (sensor) and to transmit the data by using an active circuit. Authors already addressed the idea of smart harvesting where the kinetic energy coming from the environment can be used as input source of a piezoelectric transducer which is able to harvest the vibrational energy, to perform measures of vibrations and, at the same time, it is also suitable to transmit data through an optical system without the adoption of active conditioning architectures. In this paper a suitable nonlinear configuration will be investigated in order to demonstrate its advantage in presence of wideband mechanical energy. Comparison as respect linear oscillator will be here conducted and an experimental campaign will confirm the suitability of the proposed method.
{"title":"Smart Nonlinear Energy Harvesting","authors":"C. Trigona, B. Andò, S. Baglio","doi":"10.1109/IWMN.2019.8805043","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8805043","url":null,"abstract":"The possibility to take advantage of external and not deterministic vibrations is of great interest in the modern research including sensing elements and transducers. In particular piezoelectric transducers arouse interest in the international community with particular emphasis in the area of measurements and metrology; in fact the literature presents several examples of transducers based on piezoelectric materials but, very often, focusing the attention on a specific prototype able to save energy by using the active material (transducer for energy harvesting) or suitable to perform measurements (sensor) and to transmit the data by using an active circuit. Authors already addressed the idea of smart harvesting where the kinetic energy coming from the environment can be used as input source of a piezoelectric transducer which is able to harvest the vibrational energy, to perform measures of vibrations and, at the same time, it is also suitable to transmit data through an optical system without the adoption of active conditioning architectures. In this paper a suitable nonlinear configuration will be investigated in order to demonstrate its advantage in presence of wideband mechanical energy. Comparison as respect linear oscillator will be here conducted and an experimental campaign will confirm the suitability of the proposed method.","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127969917","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 : 2019-05-23DOI: 10.1109/IWMN.2019.8804984
Chaoyun Zhang, M. Fiore, P. Patras
Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual services. This paper attacks the problem of multi-service mobile traffic forecasting using a sequence-to-sequence (S2S) learning paradigm and convolutional long short-term memories (ConvL-STMs). The proposed architecture is designed so as to effectively extract complex spatiotemporal features of mobile network traffic and predict with high accuracy the future demands for individual services at city scale. We conduct experiments on a mobile traffic dataset collected in a large European metropolis, demonstrating that the proposed S2S-ConvLSTM can forecast the mobile traffic volume produced by tens of different services in advance of up to one hour, by just using measurements taken during the past hour. In particular, our solution achieves mean absolute errors (MAE) at antenna level that are below 13KBps, outperforming other deep learning approaches by up to 31.2%.
{"title":"Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories","authors":"Chaoyun Zhang, M. Fiore, P. Patras","doi":"10.1109/IWMN.2019.8804984","DOIUrl":"https://doi.org/10.1109/IWMN.2019.8804984","url":null,"abstract":"Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual services. This paper attacks the problem of multi-service mobile traffic forecasting using a sequence-to-sequence (S2S) learning paradigm and convolutional long short-term memories (ConvL-STMs). The proposed architecture is designed so as to effectively extract complex spatiotemporal features of mobile network traffic and predict with high accuracy the future demands for individual services at city scale. We conduct experiments on a mobile traffic dataset collected in a large European metropolis, demonstrating that the proposed S2S-ConvLSTM can forecast the mobile traffic volume produced by tens of different services in advance of up to one hour, by just using measurements taken during the past hour. In particular, our solution achieves mean absolute errors (MAE) at antenna level that are below 13KBps, outperforming other deep learning approaches by up to 31.2%.","PeriodicalId":272577,"journal":{"name":"2019 IEEE International Symposium on Measurements & Networking (M&N)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679458","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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