The growth rate of Internet-of-Things (IoT) devices sold globally is constantly lower than the forecast. This deceleration is caused in part by the need for batteries and the scalability cost for their replacement. Backscatter has attracted significant interest over the past couple of years to enable sustainable sensing devices by eliminating batteries. IoT devices have been designed for transmitting sensed data with backscatter, but the question of efficient reception of data with battery-free devices is still open. As shown in this paper, classical low-power Radio Frequency (RF) envelope detectors are affected by low sensitivity, false detection alarms, and low energy efficiency. We argue that Light Fidelity (LiFi) can provide downlink and harvesting medium as LED lights are becoming pervasively deployed for illumination. We show, for the first time, that the advantages of LiFi and RF backscatter can be combined for battery-free communication. We design a low-power platform that leverages the complementary nature of these two mediums. We demonstrate that our platform removes energy-inefficiency in the downlink reception typical of RF backscatter, and significantly expands the deployment scenarios for battery-free tags when compared to conventional single-technology designs.
{"title":"Two to tango: hybrid light and backscatter networks for next billion devices","authors":"Ander Galisteo, Ambuj Varshney, D. Giustiniano","doi":"10.1145/3386901.3388918","DOIUrl":"https://doi.org/10.1145/3386901.3388918","url":null,"abstract":"The growth rate of Internet-of-Things (IoT) devices sold globally is constantly lower than the forecast. This deceleration is caused in part by the need for batteries and the scalability cost for their replacement. Backscatter has attracted significant interest over the past couple of years to enable sustainable sensing devices by eliminating batteries. IoT devices have been designed for transmitting sensed data with backscatter, but the question of efficient reception of data with battery-free devices is still open. As shown in this paper, classical low-power Radio Frequency (RF) envelope detectors are affected by low sensitivity, false detection alarms, and low energy efficiency. We argue that Light Fidelity (LiFi) can provide downlink and harvesting medium as LED lights are becoming pervasively deployed for illumination. We show, for the first time, that the advantages of LiFi and RF backscatter can be combined for battery-free communication. We design a low-power platform that leverages the complementary nature of these two mediums. We demonstrate that our platform removes energy-inefficiency in the downlink reception typical of RF backscatter, and significantly expands the deployment scenarios for battery-free tags when compared to conventional single-technology designs.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116886119","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}
Recent years have witnessed novel designs of battery-free IoT tags using RF backscatter. Traditionally, they require a dedicated transmitter to excite the tag. However, such a deployment is infeasible at large scale. To counter this problem, researchers have proposed using ambient RF energy to power up the battery-free tag. In this poster, we evaluate if this ambient RF energy is sufficient to meet the power requirements of a battery-free tag in today's urban and rural areas. We also compare available ambient RF energy across different frequencies. Finally, we discuss open challenges in realising ambient backscatter systems in real-world.
{"title":"Does ambient RF energy suffice to power battery-free IoT?","authors":"A. Bansal, Swarun Kumar, Bob Iannucci","doi":"10.1145/3386901.3396604","DOIUrl":"https://doi.org/10.1145/3386901.3396604","url":null,"abstract":"Recent years have witnessed novel designs of battery-free IoT tags using RF backscatter. Traditionally, they require a dedicated transmitter to excite the tag. However, such a deployment is infeasible at large scale. To counter this problem, researchers have proposed using ambient RF energy to power up the battery-free tag. In this poster, we evaluate if this ambient RF energy is sufficient to meet the power requirements of a battery-free tag in today's urban and rural areas. We also compare available ambient RF energy across different frequencies. Finally, we discuss open challenges in realising ambient backscatter systems in real-world.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115766582","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}
Akarsh Prabhakara, Vaibhav Singh, Swarun Kumar, Anthony G. Rowe
Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire wear affects performance and is an important metric that decides tire replacement, one of the biggest maintenance expense of the global trucking industry. We believe that it is important to measure and monitor tire wear in all automobiles. Current approach to measure tire wear is manual and extremely tedious. Embedding sensor electronics in tires to measure tire wear is challenging, given the inhospitable temperature, pressure and dynamics of the tire. Further, off-tire sensors placed in the well such as laser range-finders are vulnerable to road debris that may settle in tire grooves. This paper presents Osprey, the first on-automobile, mmWave sensing system that can measure accurate tire wear continuously and is robust to road debris. Osprey's key innovation is to leverage existing, high volume, automobile mmWave RADAR, place it in the tire well of automobiles and observe reflections of the RADAR's signal from the tire surface and grooves to measure tire wear, even in the presence of debris. We achieve this through a super-resolution Inverse Synthetic Aperture RADAR algorithm that exploits the natural rotation of the tire and improves range resolution to sub-mm. We show how our system can eliminate debris by attaching specialized metallic structures in the grooves that behave as spatial codes and offer a unique signature, when coupled with the rotation of the tire. In addition to tire wear sensing, we demonstrate the ability to detect and locate unsafe, metallic foreign objects such as nails lodged in the tire. We evaluate Osprey on commercial tires mounted on mechanical, tire-rotation rig and passenger car. We test Osprey at different speeds, in the presence of different types of debris, different levels of debris, on different terrains, and different levels of automobile vibration. We achieve a median absolute tire wear error of 0.68 mm across all our experiments. Osprey also locates foreign objects lodged in the tire with an error of 1.7 cm and detects metallic foreign objects with an accuracy of 92%.
{"title":"Osprey: a mmWave approach to tire wear sensing","authors":"Akarsh Prabhakara, Vaibhav Singh, Swarun Kumar, Anthony G. Rowe","doi":"10.1145/3386901.3389031","DOIUrl":"https://doi.org/10.1145/3386901.3389031","url":null,"abstract":"Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire wear affects performance and is an important metric that decides tire replacement, one of the biggest maintenance expense of the global trucking industry. We believe that it is important to measure and monitor tire wear in all automobiles. Current approach to measure tire wear is manual and extremely tedious. Embedding sensor electronics in tires to measure tire wear is challenging, given the inhospitable temperature, pressure and dynamics of the tire. Further, off-tire sensors placed in the well such as laser range-finders are vulnerable to road debris that may settle in tire grooves. This paper presents Osprey, the first on-automobile, mmWave sensing system that can measure accurate tire wear continuously and is robust to road debris. Osprey's key innovation is to leverage existing, high volume, automobile mmWave RADAR, place it in the tire well of automobiles and observe reflections of the RADAR's signal from the tire surface and grooves to measure tire wear, even in the presence of debris. We achieve this through a super-resolution Inverse Synthetic Aperture RADAR algorithm that exploits the natural rotation of the tire and improves range resolution to sub-mm. We show how our system can eliminate debris by attaching specialized metallic structures in the grooves that behave as spatial codes and offer a unique signature, when coupled with the rotation of the tire. In addition to tire wear sensing, we demonstrate the ability to detect and locate unsafe, metallic foreign objects such as nails lodged in the tire. We evaluate Osprey on commercial tires mounted on mechanical, tire-rotation rig and passenger car. We test Osprey at different speeds, in the presence of different types of debris, different levels of debris, on different terrains, and different levels of automobile vibration. We achieve a median absolute tire wear error of 0.68 mm across all our experiments. Osprey also locates foreign objects lodged in the tire with an error of 1.7 cm and detects metallic foreign objects with an accuracy of 92%.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116056916","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}
Hoang Truong, Nam Bui, Zohreh Raghebi, Marta Čeko, Nhat Pham, Phuc Nguyen, Anh Nguyen, Taeho Kim, Katrina Siegfried, Evan Stene, Taylor Tvrdy, Logan Weinman, T. Payne, D. Burke, Thang Dinh, Sidney K. D’Mello, F. Banaei-Kashani, Tor D. Wager, P. Goldstein, Tam N. Vu
Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management. This paper explores a wearable system, named Painometry, which objectively quantifies users' pain perception based-on multiple physiological signals and facial expressions of pain. We propose a sensing technique, called sweep impedance profiling (SIP), to capture the movement of the facial muscle corrugator supercilii, one of the important physiological expressions of pain. We deploy SIP together with other biosignals, including electroencephalography (EEG), photoplethysmogram (PPG), and galvanic skin response (GSR) for pain quantification. From the anatomical and physiological correlations of pain with these signals, we designed Painometry, a multimodality sensing system, which can accurately quantify different levels of pain safely. We prototyped Painometry by building a custom hardware, firmware, and associated software. Our evaluations use the prototype on 23 subjects, which corresponds to 8832 data points from 276 minutes of an IRB-approved experimental pain-inducing protocol. Using leave-one-out cross-validation to estimate performance on unseen data shows 89.5% and 76.7% accuracy of quantification under 3 and 4 pain states, respectively.
{"title":"Painometry","authors":"Hoang Truong, Nam Bui, Zohreh Raghebi, Marta Čeko, Nhat Pham, Phuc Nguyen, Anh Nguyen, Taeho Kim, Katrina Siegfried, Evan Stene, Taylor Tvrdy, Logan Weinman, T. Payne, D. Burke, Thang Dinh, Sidney K. D’Mello, F. Banaei-Kashani, Tor D. Wager, P. Goldstein, Tam N. Vu","doi":"10.1145/3386901.3389022","DOIUrl":"https://doi.org/10.1145/3386901.3389022","url":null,"abstract":"Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management. This paper explores a wearable system, named Painometry, which objectively quantifies users' pain perception based-on multiple physiological signals and facial expressions of pain. We propose a sensing technique, called sweep impedance profiling (SIP), to capture the movement of the facial muscle corrugator supercilii, one of the important physiological expressions of pain. We deploy SIP together with other biosignals, including electroencephalography (EEG), photoplethysmogram (PPG), and galvanic skin response (GSR) for pain quantification. From the anatomical and physiological correlations of pain with these signals, we designed Painometry, a multimodality sensing system, which can accurately quantify different levels of pain safely. We prototyped Painometry by building a custom hardware, firmware, and associated software. Our evaluations use the prototype on 23 subjects, which corresponds to 8832 data points from 276 minutes of an IRB-approved experimental pain-inducing protocol. Using leave-one-out cross-validation to estimate performance on unseen data shows 89.5% and 76.7% accuracy of quantification under 3 and 4 pain states, respectively.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117063870","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}
In client-server architectures, there are cases where there is a need to transfer large amounts of data from the server to the client (or less frequently, in the opposite direction). Mobile application markets are a notable example where the clients need to download large chunks of data in the order of megabytes at a time, compared to a typical RPC request which is in the order of kilobytes.
{"title":"Deduplicating future data transfer using data exchanged in the past to decrease mobile bandwidth usage","authors":"Mohammad Nasirifar, Angela Demke Brown","doi":"10.1145/3386901.3396605","DOIUrl":"https://doi.org/10.1145/3386901.3396605","url":null,"abstract":"In client-server architectures, there are cases where there is a need to transfer large amounts of data from the server to the client (or less frequently, in the opposite direction). Mobile application markets are a notable example where the clients need to download large chunks of data in the order of megabytes at a time, compared to a typical RPC request which is in the order of kilobytes.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122902747","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}
This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained embedded systems. The goal of neural weight virtualization is two-fold: (1) packing multiple DNNs into a fixed-sized main memory whose combined memory requirement is larger than the main memory, and (2) enabling fast in-memory execution of the DNNs. To this end, we propose a two-phase approach: (1) virtualization of weight parameters for fine-grained parameter sharing at the level of weights that scales up to multiple heterogeneous DNNs of arbitrary network architectures, and (2) in-memory data structure and run-time execution framework for in-memory execution and context-switching of DNN tasks. We implement two multitask learning systems: (1) an embedded GPU-based mobile robot, and (2) a microcontroller-based IoT device. We thoroughly evaluate the proposed algorithms as well as the two systems that involve ten state-of-the-art DNNs. Our evaluation shows that weight virtualization improves memory efficiency, execution time, and energy efficiency of the multitask learning systems by 4.1x, 36.9x, and 4.2x, respectively.
{"title":"Fast and scalable in-memory deep multitask learning via neural weight virtualization","authors":"Seulki Lee, S. Nirjon","doi":"10.1145/3386901.3388947","DOIUrl":"https://doi.org/10.1145/3386901.3388947","url":null,"abstract":"This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained embedded systems. The goal of neural weight virtualization is two-fold: (1) packing multiple DNNs into a fixed-sized main memory whose combined memory requirement is larger than the main memory, and (2) enabling fast in-memory execution of the DNNs. To this end, we propose a two-phase approach: (1) virtualization of weight parameters for fine-grained parameter sharing at the level of weights that scales up to multiple heterogeneous DNNs of arbitrary network architectures, and (2) in-memory data structure and run-time execution framework for in-memory execution and context-switching of DNN tasks. We implement two multitask learning systems: (1) an embedded GPU-based mobile robot, and (2) a microcontroller-based IoT device. We thoroughly evaluate the proposed algorithms as well as the two systems that involve ten state-of-the-art DNNs. Our evaluation shows that weight virtualization improves memory efficiency, execution time, and energy efficiency of the multitask learning systems by 4.1x, 36.9x, and 4.2x, respectively.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125939735","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}
Air quality has impacts on our health and environment extremely. To monitor air pollutants, with the maturity of wireless sensor network, low-cost air sensors are deployed to trace pollution sources and detect personal exposure. Regular quality audit of deployed sensors is essential to ensure data quality of large-scale air monitoring networks. However, inspecting tremendous sensors by professional technicians regularly will take much human resources. This paper proposed the key sensor discovery for efficient and effective quality audit of large-scale air sensor network.
{"title":"Key sensor discovery for quality audit of air sensor networks","authors":"Tzu-Heng Huang, Cheng-Hsien Tsai, M. Shan","doi":"10.1145/3386901.3396606","DOIUrl":"https://doi.org/10.1145/3386901.3396606","url":null,"abstract":"Air quality has impacts on our health and environment extremely. To monitor air pollutants, with the maturity of wireless sensor network, low-cost air sensors are deployed to trace pollution sources and detect personal exposure. Regular quality audit of deployed sensors is essential to ensure data quality of large-scale air monitoring networks. However, inspecting tremendous sensors by professional technicians regularly will take much human resources. This paper proposed the key sensor discovery for efficient and effective quality audit of large-scale air sensor network.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114454729","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}
Aditya Singh Rathore, Weijin Zhu, Afee Daiyan, Chenhan Xu, Kun Wang, Feng Lin, K. Ren, Wenyao Xu
The advent of smart devices has caused unprecedented security and privacy concerns to its users. Although the fingerprint technology is a go-to biometric solution in high-impact applications (e.g., smart-phone security, monetary transactions and international-border verification), the existing fingerprint scanners are vulnerable to spoofing attacks via fake-finger and cannot be employed across smart devices (e.g., wearables) due to hardware constraints. We propose SonicPrint that extends fingerprint identification beyond smartphones to any smart device without the need for traditional fingerprint scanners. SonicPrint builds on the fingerprint-induced sonic effect (FiSe) caused by a user swiping his fingertip on smart devices and the resulting property, i.e., different users' fingerprint would result in distinct FiSe. As the first exploratory study, extensive experiments verify the above property with 31 participants over four different swipe actions on five different types of smart devices with even partial fingerprints. SonicPrint achieves up to a 98% identification accuracy on smartphone and an equal-error-rate (EER) less than 3% for smartwatch and headphones. We also examine and demonstrate the resilience of SonicPrint against fingerprint phantoms and replay attacks. A key advantage of SonicPrint is that it leverages the already existing microphones in smart devices, requiring no hardware modifications. Compared with other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacy-oriented and secure approach to identify users across smart devices of unique form-factors.
{"title":"SonicPrint: a generally adoptable and secure fingerprint biometrics in smart devices","authors":"Aditya Singh Rathore, Weijin Zhu, Afee Daiyan, Chenhan Xu, Kun Wang, Feng Lin, K. Ren, Wenyao Xu","doi":"10.1145/3386901.3388939","DOIUrl":"https://doi.org/10.1145/3386901.3388939","url":null,"abstract":"The advent of smart devices has caused unprecedented security and privacy concerns to its users. Although the fingerprint technology is a go-to biometric solution in high-impact applications (e.g., smart-phone security, monetary transactions and international-border verification), the existing fingerprint scanners are vulnerable to spoofing attacks via fake-finger and cannot be employed across smart devices (e.g., wearables) due to hardware constraints. We propose SonicPrint that extends fingerprint identification beyond smartphones to any smart device without the need for traditional fingerprint scanners. SonicPrint builds on the fingerprint-induced sonic effect (FiSe) caused by a user swiping his fingertip on smart devices and the resulting property, i.e., different users' fingerprint would result in distinct FiSe. As the first exploratory study, extensive experiments verify the above property with 31 participants over four different swipe actions on five different types of smart devices with even partial fingerprints. SonicPrint achieves up to a 98% identification accuracy on smartphone and an equal-error-rate (EER) less than 3% for smartwatch and headphones. We also examine and demonstrate the resilience of SonicPrint against fingerprint phantoms and replay attacks. A key advantage of SonicPrint is that it leverages the already existing microphones in smart devices, requiring no hardware modifications. Compared with other biometrics including physiological patterns and passive sensing, SonicPrint is a low-cost, privacy-oriented and secure approach to identify users across smart devices of unique form-factors.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114624223","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}
B. Reynders, Franco Minucci, Erma Perenda, Hazem Sallouha, Roberto Calvo-Palomino, Yago Lizarribar, Markus Fuchs, Matthias Schäfer, Markus Engel, B. Van den Bergh, S. Pollin, D. Giustiniano, Gérôme Bovet, Vincent Lenders
Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons.
{"title":"SkySense","authors":"B. Reynders, Franco Minucci, Erma Perenda, Hazem Sallouha, Roberto Calvo-Palomino, Yago Lizarribar, Markus Fuchs, Matthias Schäfer, Markus Engel, B. Van den Bergh, S. Pollin, D. Giustiniano, Gérôme Bovet, Vincent Lenders","doi":"10.1145/3386901.3389026","DOIUrl":"https://doi.org/10.1145/3386901.3389026","url":null,"abstract":"Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126058951","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}
J. O. Lacruz, Dolores García, Pablo Jiménez Mateo, J. Palacios, J. Widmer
Millimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments. To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra-fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal's angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second time-scale while receiving a packet preamble. Our implementation is open source and is made publicly available to the research community.
{"title":"mm-FLEX: an open platform for millimeter-wave mobile full-bandwidth experimentation","authors":"J. O. Lacruz, Dolores García, Pablo Jiménez Mateo, J. Palacios, J. Widmer","doi":"10.1145/3386901.3389034","DOIUrl":"https://doi.org/10.1145/3386901.3389034","url":null,"abstract":"Millimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments. To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra-fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal's angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second time-scale while receiving a packet preamble. Our implementation is open source and is made publicly available to the research community.","PeriodicalId":345029,"journal":{"name":"Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114691884","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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