In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.
{"title":"Robust Environmental Sensing Using UAVs","authors":"Ahmed Boubrima, E. Knightly","doi":"10.1145/3464943","DOIUrl":"https://doi.org/10.1145/3464943","url":null,"abstract":"In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81440381","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}
Riccardo Petrolo, Zhambyl Shaikhanov, Yingyan Lin, E. Knightly
We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.
{"title":"ASTRO","authors":"Riccardo Petrolo, Zhambyl Shaikhanov, Yingyan Lin, E. Knightly","doi":"10.1145/3464942","DOIUrl":"https://doi.org/10.1145/3464942","url":null,"abstract":"We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74068095","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}
Morshed U. Chowdhury, B. Ray, Sujan Chowdhury, S. Rajasegarar
Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack, that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.
{"title":"A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things","authors":"Morshed U. Chowdhury, B. Ray, Sujan Chowdhury, S. Rajasegarar","doi":"10.1145/3466721","DOIUrl":"https://doi.org/10.1145/3466721","url":null,"abstract":"Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack, that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89893050","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}
Sauptik Dhar, Junyao Guo, Jiayi Liu, S. Tripathi, Unmesh Kurup, Mohak Shah
The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing numbers of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state of the art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.
{"title":"A Survey of On-Device Machine Learning","authors":"Sauptik Dhar, Junyao Guo, Jiayi Liu, S. Tripathi, Unmesh Kurup, Mohak Shah","doi":"10.1145/3450494","DOIUrl":"https://doi.org/10.1145/3450494","url":null,"abstract":"The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing numbers of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state of the art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85209834","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}
The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache, which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.
{"title":"Enabling Service Cache in Edge Clouds","authors":"Chih-Kai Huang, Shan-Hsiang Shen","doi":"10.1145/3456564","DOIUrl":"https://doi.org/10.1145/3456564","url":null,"abstract":"The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache, which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86776943","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}
Thilina Buddhika, Matthew Malensek, S. Pallickara, S. Pallickara
Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.
{"title":"Living on the Edge","authors":"Thilina Buddhika, Matthew Malensek, S. Pallickara, S. Pallickara","doi":"10.1145/3450767","DOIUrl":"https://doi.org/10.1145/3450767","url":null,"abstract":"Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74694599","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}
Ratna Mandal, Prasenjit Karmakar, S. Chatterjee, Debaleen Das Spandan, S. Pradhan, Sujoy Saha, Sandip Chakraborty, S. Nandi
Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.
{"title":"Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction","authors":"Ratna Mandal, Prasenjit Karmakar, S. Chatterjee, Debaleen Das Spandan, S. Pradhan, Sujoy Saha, Sandip Chakraborty, S. Nandi","doi":"10.1145/3549548","DOIUrl":"https://doi.org/10.1145/3549548","url":null,"abstract":"Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78516044","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}
User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.
{"title":"WiFi-Enabled User Authentication through Deep Learning in Daily Activities","authors":"Cong Shi, Jian Liu, Hongbo Liu, Yingying Chen","doi":"10.1145/3448738","DOIUrl":"https://doi.org/10.1145/3448738","url":null,"abstract":"User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86790696","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}
Mauro Piva, Andrea Coletta, G. Maselli, J. Stankovic
Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.
{"title":"Environment-driven Communication in Battery-free Smart Buildings","authors":"Mauro Piva, Andrea Coletta, G. Maselli, J. Stankovic","doi":"10.1145/3448739","DOIUrl":"https://doi.org/10.1145/3448739","url":null,"abstract":"Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73556809","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}
As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.
{"title":"Elk Audio OS","authors":"L. Turchet, C. Fischione","doi":"10.1145/3446393","DOIUrl":"https://doi.org/10.1145/3446393","url":null,"abstract":"As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82595066","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：