Fan Mo, H. Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, N. Kourtellis
Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System developers, vendors, network operators, etc.). Traditional ML designs assume (user) data are collected and models are trained in a centralized location. However, this approach has privacy consequences related to data collection and processing. Such concerns have incentivized the scientific community to design and develop Privacy-preserving ML methods, including techniques like Federated Learning (FL) where the ML model is trained or personalized on user devices close to the data; Differential Privacy, where data are manipulated to limit the disclosure of private information; Trusted Execution Environments (TEE), where most of the computation is run under a secure/ private environment; and Multi-Party Computation, a cryptographic technique that allows various parties to run joint computations without revealing their private data to each other.
{"title":"PPFL","authors":"Fan Mo, H. Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, N. Kourtellis","doi":"10.1145/3529706.3529715","DOIUrl":"https://doi.org/10.1145/3529706.3529715","url":null,"abstract":"Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System developers, vendors, network operators, etc.). Traditional ML designs assume (user) data are collected and models are trained in a centralized location. However, this approach has privacy consequences related to data collection and processing. Such concerns have incentivized the scientific community to design and develop Privacy-preserving ML methods, including techniques like Federated Learning (FL) where the ML model is trained or personalized on user devices close to the data; Differential Privacy, where data are manipulated to limit the disclosure of private information; Trusted Execution Environments (TEE), where most of the computation is run under a secure/ private environment; and Multi-Party Computation, a cryptographic technique that allows various parties to run joint computations without revealing their private data to each other.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"72 1","pages":"35 - 38"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78192758","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}
Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, S. Venkataraman
Connected sensors, e.g., cameras, LiDARs, air sensors, installed in a mobile platform (e.g., a terrestrial or aerial vehicle, or special in-person devices) can provide broad views of wide-area environments quickly and efficiently. If many vehicles incorporate such sensing systems, they together can be composed into a unique crowd-sourced platform and can gather fine-grained, diverse, and noisy information at city-scales. However, these sensors can generate large amounts of data and such data is hard to aggregate in a central server. We explore the design of a "roaming edge" - the notion that generalpurpose computing be installed in these mobile platforms, which connect over wireless paths to the static infrastructure and to the static edge nodes, to support a broad range of applications. In particular, a roaming edge node allows different sensors and data sources in-range of a mobile platform to connect to it, and supports data processing for necessary local analytics, considering both efficiency and privacy. The roaming edge, of course, does not operate in isolation and we describe a three-tier architecture that integrates it with a static edge and cloudhosted services. This paper also outlines several applications that can leverage opportunities provided by the roaming edge, and focus, briefly, on one - a real-time video query application with public safety implications.
{"title":"The Roaming Edge and its Applications","authors":"Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, S. Venkataraman","doi":"10.1145/3529706.3529708","DOIUrl":"https://doi.org/10.1145/3529706.3529708","url":null,"abstract":"Connected sensors, e.g., cameras, LiDARs, air sensors, installed in a mobile platform (e.g., a terrestrial or aerial vehicle, or special in-person devices) can provide broad views of wide-area environments quickly and efficiently. If many vehicles incorporate such sensing systems, they together can be composed into a unique crowd-sourced platform and can gather fine-grained, diverse, and noisy information at city-scales. However, these sensors can generate large amounts of data and such data is hard to aggregate in a central server. We explore the design of a \"roaming edge\" - the notion that generalpurpose computing be installed in these mobile platforms, which connect over wireless paths to the static infrastructure and to the static edge nodes, to support a broad range of applications. In particular, a roaming edge node allows different sensors and data sources in-range of a mobile platform to connect to it, and supports data processing for necessary local analytics, considering both efficiency and privacy. The roaming edge, of course, does not operate in isolation and we describe a three-tier architecture that integrates it with a static edge and cloudhosted services. This paper also outlines several applications that can leverage opportunities provided by the roaming edge, and focus, briefly, on one - a real-time video query application with public safety implications.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"51 1","pages":"5 - 11"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86924869","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}
L. Zhang, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yunxin Liu
Inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices. In this paper, we propose and develop nn-Meter, a novel and efficient system to accurately predict the DNN inference latency on diverse edge devices. The key idea of nn-Meter is dividing a whole model inference into kernels, i.e., the execution units on a device, and conducting kernel-level prediction. nn-Meter builds atop two key techniques: (i) kernel detection to automatically detect the execution unit of model inference via a set of well-designed test cases; and (ii) adaptive sampling to efficiently sample the most beneficial configurations from a large space to build accurate kernel-level latency predictors. nn-Meter achieves significant high prediction accuracy on four types of edge devices.
{"title":"nn-METER","authors":"L. Zhang, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yunxin Liu","doi":"10.1145/3529706.3529712","DOIUrl":"https://doi.org/10.1145/3529706.3529712","url":null,"abstract":"Inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices. In this paper, we propose and develop nn-Meter, a novel and efficient system to accurately predict the DNN inference latency on diverse edge devices. The key idea of nn-Meter is dividing a whole model inference into kernels, i.e., the execution units on a device, and conducting kernel-level prediction. nn-Meter builds atop two key techniques: (i) kernel detection to automatically detect the execution unit of model inference via a set of well-designed test cases; and (ii) adaptive sampling to efficiently sample the most beneficial configurations from a large space to build accurate kernel-level latency predictors. nn-Meter achieves significant high prediction accuracy on four types of edge devices.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"70 1","pages":"19 - 23"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81563365","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}
Seyeon Kim, Kyung Bin, Sangtae Ha, Kyunghan Lee, S. Chong
With the advent of mobile processors integrating CPU and GPU, high-performance tasks, such as deep learning, gaming, and image processing are running on mobile devices. To fully exploit CPU and GPU's capability on mobile devices, we need to utilize their processing capability as much as possible. However, it is challenging due to the nature of mobile devices whose users are sensitive to battery consumption and device temperature. Many researchers have studied techniques enabling energy-efficient operations in mobile processors, mostly at managing the temperature and power consumption below predefined thresholds. DVFS (Dynamic Voltage and Frequency Scaling) is a technique that reduces heat generation and power consumption from the circuit by adjusting CPU or GPU voltage-frequency levels at runtime. To best utilize its benefits, many DVFS techniques have been developed for mobile processors. Still, it is challenging to implement a DVFS that performs ideally for mobile devices, and there are several reasons behind this difficulty.
{"title":"zTT","authors":"Seyeon Kim, Kyung Bin, Sangtae Ha, Kyunghan Lee, S. Chong","doi":"10.1145/3529706.3529714","DOIUrl":"https://doi.org/10.1145/3529706.3529714","url":null,"abstract":"With the advent of mobile processors integrating CPU and GPU, high-performance tasks, such as deep learning, gaming, and image processing are running on mobile devices. To fully exploit CPU and GPU's capability on mobile devices, we need to utilize their processing capability as much as possible. However, it is challenging due to the nature of mobile devices whose users are sensitive to battery consumption and device temperature. Many researchers have studied techniques enabling energy-efficient operations in mobile processors, mostly at managing the temperature and power consumption below predefined thresholds. DVFS (Dynamic Voltage and Frequency Scaling) is a technique that reduces heat generation and power consumption from the circuit by adjusting CPU or GPU voltage-frequency levels at runtime. To best utilize its benefits, many DVFS techniques have been developed for mobile processors. Still, it is challenging to implement a DVFS that performs ideally for mobile devices, and there are several reasons behind this difficulty.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"30 1","pages":"30 - 34"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86586401","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}
Earbuds, ear-worn wearables, have attracted growing attention from both industry and academia. This trend has witnessed manufacturers embedding multiple sensors on earbuds to enrich their functionalities. For example, Apple AirPods, Sony WF-1000XM3, and Bose QuietControl 30, have been equipped with accelerometers for tapping interaction or multiple microphones for noise cancellation. On the other hand, the research community regards earbuds as a powerful personal-scale human sensing and computing platform. By integrating sensors like PPG, barometer, and ultrasonic sensors, researchers have been devising a plethora of earable sensing applications, such as blood pressure monitoring [1], facial expression recognition [2], and authentication [3].
{"title":"Innovative Human Motion Sensing With Earbuds","authors":"Dong Ma, Andrea Ferlini, C. Mascolo","doi":"10.1145/3529706.3529713","DOIUrl":"https://doi.org/10.1145/3529706.3529713","url":null,"abstract":"Earbuds, ear-worn wearables, have attracted growing attention from both industry and academia. This trend has witnessed manufacturers embedding multiple sensors on earbuds to enrich their functionalities. For example, Apple AirPods, Sony WF-1000XM3, and Bose QuietControl 30, have been equipped with accelerometers for tapping interaction or multiple microphones for noise cancellation. On the other hand, the research community regards earbuds as a powerful personal-scale human sensing and computing platform. By integrating sensors like PPG, barometer, and ultrasonic sensors, researchers have been devising a plethora of earable sensing applications, such as blood pressure monitoring [1], facial expression recognition [2], and authentication [3].","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"7 1","pages":"24 - 29"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89688532","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}
A mechanical ventilator keeps a patient with respiratory failure alive by pumping precisely controlled amounts of air (or an air/O2 mixture) at controlled pressure into the patient's lungs [3, 5]. During intake (inspiration), the ventilator meters the flow of air and the duration of the flow to deliver a controlled tidal volume of air (typically 50 to 800 mL). During the exhaust (expiration) phase, the flow is turned off and a path is opened to allow the patient to exhale to the atmosphere - possibly with a positive pressure maintained at the end of the expiratory period (PEEP). The timing of the breaths can be entirely managed by the ventilator, or a new breath can be initiated by the patient.
{"title":"OP-VENT","authors":"W. Dally","doi":"10.1145/3529706.3529710","DOIUrl":"https://doi.org/10.1145/3529706.3529710","url":null,"abstract":"A mechanical ventilator keeps a patient with respiratory failure alive by pumping precisely controlled amounts of air (or an air/O2 mixture) at controlled pressure into the patient's lungs [3, 5]. During intake (inspiration), the ventilator meters the flow of air and the duration of the flow to deliver a controlled tidal volume of air (typically 50 to 800 mL). During the exhaust (expiration) phase, the flow is turned off and a path is opened to allow the patient to exhale to the atmosphere - possibly with a positive pressure maintained at the end of the expiratory period (PEEP). The timing of the breaths can be entirely managed by the ventilator, or a new breath can be initiated by the patient.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"178 1","pages":"12 - 18"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79954433","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}
Junfeng Guan, Jitian Zhang, Ruochen Lu, Hyungjoo Seo, Jin Zhou, S. Gong, Haitham Hassanieh
The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].
{"title":"Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators","authors":"Junfeng Guan, Jitian Zhang, Ruochen Lu, Hyungjoo Seo, Jin Zhou, S. Gong, Haitham Hassanieh","doi":"10.1145/3511285.3511293","DOIUrl":"https://doi.org/10.1145/3511285.3511293","url":null,"abstract":"The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"46 1","pages":"23 - 27"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73080494","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}
Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.
{"title":"Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning","authors":"Francesco Restuccia, T. Melodia","doi":"10.1145/3511285.3511294","DOIUrl":"https://doi.org/10.1145/3511285.3511294","url":null,"abstract":"Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"6 1","pages":"28 - 33"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74984299","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}
Agrim Gupta, C. Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania. K. Morimoto, Dinesh Bharadia
All interactions of objects, humans, and machines with the physical world are via contact forces. For instance, objects placed on a table exert their gravitational forces, and the contact interactions via our hands/feet are guided by the sense of contact force felt by our skin. Thus, the ability to sense the contact forces can allow us to measure all these ubiquitous interactions, enabling a myriad of applications. Furthermore, force sensors are a critical requirement for safer surgeries, which require measuring complex contact forces experienced as a surgical instrument interacts with the surrounding tissues during the surgical procedure. However, with currently available discrete point-force sensors, which require a battery to sense the forces and communicate the readings wirelessly, these ubiquitous sensing and surgical sensing applications are not practical. This motivates the development of new force sensors that can sense, and communicate wirelessly without consuming significant power to enable a battery-free design. In this magazine article, we present WiForce, a low-power wireless force sensor utilizing a joint sensing-communication paradigm. That is, instead of having separate sensing and communication blocks, WiForce directly transduces the force measurements onto variations in wireless signals reflecting WiForce from the sensor. This novel trans-duction mechanism also allows WiForce to generalize easily to a length continuum, where we can detect as well as localize forces acting on the continuum. We fabricate and test our sensor prototype in different scenarios, including testing beneath a tissue phantom, and obtain sub-N sensing and sub-mm localizing accuracies (0.34 N and 0.6 mm, respectively).
{"title":"Expanding the Horizons of Wireless Sensing","authors":"Agrim Gupta, C. Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania. K. Morimoto, Dinesh Bharadia","doi":"10.1145/3511285.3511296","DOIUrl":"https://doi.org/10.1145/3511285.3511296","url":null,"abstract":"All interactions of objects, humans, and machines with the physical world are via contact forces. For instance, objects placed on a table exert their gravitational forces, and the contact interactions via our hands/feet are guided by the sense of contact force felt by our skin. Thus, the ability to sense the contact forces can allow us to measure all these ubiquitous interactions, enabling a myriad of applications. Furthermore, force sensors are a critical requirement for safer surgeries, which require measuring complex contact forces experienced as a surgical instrument interacts with the surrounding tissues during the surgical procedure. However, with currently available discrete point-force sensors, which require a battery to sense the forces and communicate the readings wirelessly, these ubiquitous sensing and surgical sensing applications are not practical. This motivates the development of new force sensors that can sense, and communicate wirelessly without consuming significant power to enable a battery-free design. In this magazine article, we present WiForce, a low-power wireless force sensor utilizing a joint sensing-communication paradigm. That is, instead of having separate sensing and communication blocks, WiForce directly transduces the force measurements onto variations in wireless signals reflecting WiForce from the sensor. This novel trans-duction mechanism also allows WiForce to generalize easily to a length continuum, where we can detect as well as localize forces acting on the continuum. We fabricate and test our sensor prototype in different scenarios, including testing beneath a tissue phantom, and obtain sub-N sensing and sub-mm localizing accuracies (0.34 N and 0.6 mm, respectively).","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"6 1","pages":"38 - 42"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76407758","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}
I believe that the Internet of Tiny Things (IoTT) will be the next big driver of the computing and semiconductor industry - imagine applications such as smart city, home sensors, wearables, implantables, single-use electronic tags for pharmaceuticals and produce, and more. Trillions of tiny devices may be needed every year to enable these applications, while meeting extreme requirements in terms of cost (sometimes only a few cents), power (often self-powered), and trust (often physically accessible and producing sensitive data). Our research over last few years has been focused on enabling an internet of these tiny things by addressing the unique cost, power, and trust challenges of these devices.
{"title":"When Tiny Goes Big","authors":"Rakesh Kumar","doi":"10.1145/3511285.3511289","DOIUrl":"https://doi.org/10.1145/3511285.3511289","url":null,"abstract":"I believe that the Internet of Tiny Things (IoTT) will be the next big driver of the computing and semiconductor industry - imagine applications such as smart city, home sensors, wearables, implantables, single-use electronic tags for pharmaceuticals and produce, and more. Trillions of tiny devices may be needed every year to enable these applications, while meeting extreme requirements in terms of cost (sometimes only a few cents), power (often self-powered), and trust (often physically accessible and producing sensitive data). Our research over last few years has been focused on enabling an internet of these tiny things by addressing the unique cost, power, and trust challenges of these devices.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"28 1","pages":"12 - 17"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82821219","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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