國立虎尾科技大學 |

Smartphone-Based Sensing Systems for Data-Intensive Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Smartphone-Based Sensing Systems for Data-Intensive Applications./
作者:	Moazzami, Mohammad-Mahdi.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355505863

Smartphone-Based Sensing Systems for Data-Intensive Applications.
Moazzami, Mohammad-Mahdi.

Smartphone-Based Sensing Systems for Data-Intensive Applications. - 1 online resource (154 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--Michigan State University, 2017.

Includes bibliographical references

Supported by advanced sensing capabilities, increasing computational resources and the advances in Artificial Intelligence, smartphones have become our virtual companions in our daily life. An average modern smartphone is capable of handling a wide range of tasks including navigation, advanced image processing, speech processing, cross app data processing and etc. The key facet that is common in all of these applications is the data intensive computation. In this dissertation we have taken steps towards the realization of the vision that makes the smartphone truly a platform for data intensive computations by proposing frameworks, application and algorithmic solutions. We followed a data-driven approach to the system design. To this end, several challenges must be addressed before smartphones can be used as a system platform for data-intensive applications. The major challenge addressed in this dissertation include high power consumption, high computation cost in advance machine learning algorithms, lack of real-time functionalities, lack of embedded programming support, heterogeneity in the apps, communication interfaces and programming abstractions and lack of customized data processing libraries. The contribution of this dissertation can be summarized as follows. We presented the design, implementation and evaluation of the ORBIT framework, which represents the first system that combines the design requirements of a machine learning system and sensing system together at the same time. We ported for the first time off-the-shelf machine learning algorithms for real-time sensor data processing to smartphone devices. In this process we considered the power and memory limitation of smartphone, and for each algorithm we provided two versions: the light and the heavy version. This is a leap forward from previous approaches, which relied on custom-designed sensing and computing platforms. We highlighted how machine learning on smartphones comes with severe costs that need to be mitigated in order to make smartphone capable of real-time data-intensive processing. Some of the costs can be managed with an adapting re-design of the off-the-shelf processing pipeline with additional real-time hyper-parameter control parameters to control the precision and computation cost of the pipeline respect to available resource smartphone in terms of battery duration. We showed that some of the limitations imposed by a mobile sensing application can be overcome by having a multi-tier framework allowing us to split the computation pipeline between the smartphone and two other tiers namely extension-board and cloud, by identifying the bottlenecks in the computation graph. We showed that computation blocks can be can be adopted at execution time leading to further improvement in the resource consumption while maintaining the algorithm accuracy and yet shortening the computation time. We reported on our experience deploying ORBIT at scale with a few case studies as well as multiple deployments on active volcanos in Ecuador and Chile. We extended the scope of our work from platforms to application and presented SPOT. SPOT aims to address some of the challenges discovered in mobile-based smart-home systems. These challenges prevent us from achieving the promises of smart-homes due to heterogeneity in different aspects of smart devices and the underlining systems. This owes to lack of dominating standards in smart-home technologies, leading to the fragmented digital homes rather than truly smart homes. We face the following major heterogeneities in building smart-homes: (i) Diverse appliance control apps (ii) Communication interface, (iii) Programming abstraction. SPOT is an enabling technology for smart-homes system that allows the integration of hetrogenious smart-device seemless by proposing a novel dynamic draver loading schema. SPOT introduces two driver models namely XML-based and library-based allowing the integration and manipulation of smart devices easy for both programmers and users. SPOT makes the heterogeneous characteristics of smart appliances transparent, and by that minimizes the burden of home automation application developers and the efforts of users who would otherwise have to deal with appliance-specific apps and control interfaces. SPOT is evaluated through several benchmarks and three case studies: cross-device programming, central usage analytics and residential energy management via demand response commands. Our evaluation demonstrates the generality of SPOT's design and its driver model. After discussing two aspects of this dissertation namely the framework and the application, we finally presented the algorithmic aspect of the dissertation by introducing two systems in smartphone-based deep learning area: Deep-Crowd-Label and Deep-Partition. Deep neural models are both computationally and memory intensive, making them difficult to deploy on mobile applications with limited hardware resources. On the other hand, they are the most advanced machine learning algorithms suitable for real-time sensing applications used in the wild. Deep- Partition is an optimization based partitioning meta-algorithm featuring a tiered architecture for smartphone and the back-end cloud, which helps to deploy and execute deep neural models more efficiently. Deep-Partition provides a profile-based model partitioning allowing it to intelligently execute the Deep Learning algorithms among the tiers to minimize the smartphone power consumption by minimizing the deep models feed-forward latency. Extensive microbenchmark evaluation and three case studies on representative deep neural models validate the performance gain by Deep-Partition. In addition, we presented Deep-Crowd-Label, a novel algorithm designed for distributed collaborative smartphone systems for crowd-sourcing applications. Deep-Crowd-Label is prototyped for semantically labeling user's location. Deep-Crowd-Label is a crowd-assisted algorithm that uses crowd-sourcing in both training and inference time. It builds deep convolutional neural models using crowd-sensed images to detect the context (label) of indoor locations. It features domain adaptation and model extension via transfer learning to efficiently build deep models for image labeling. By fully exploiting the pre-trained models and available datasets, Deep-Crowd- Label builds ensemble of models to increase the robustness and improve the accuracy of prediction. Moreover, Deep-Crowd-Label aggregates several individual predictions of images obtained from the same location to infer the contextual label of a location. The prototyped system and the preliminary experiments on 26 different in-door locations show the high accuracy of the model and demonstrates the generality and robustness of the underlying approach. The work presented in this dissertation covers three major facets of data-driven and compute-intensive smartphone-based systems, platforms, applications and algorithms; and helps to spurs a new area of research on smartphone sensing and opens up new directions in mobile computing research.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355505863Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Smartphone-Based Sensing Systems for Data-Intensive Applications.
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The major challenge addressed in this dissertation include high power consumption, high computation cost in advance machine learning algorithms, lack of real-time functionalities, lack of embedded programming support, heterogeneity in the apps, communication interfaces and programming abstractions and lack of customized data processing libraries. The contribution of this dissertation can be summarized as follows. We presented the design, implementation and evaluation of the ORBIT framework, which represents the first system that combines the design requirements of a machine learning system and sensing system together at the same time. We ported for the first time off-the-shelf machine learning algorithms for real-time sensor data processing to smartphone devices. In this process we considered the power and memory limitation of smartphone, and for each algorithm we provided two versions: the light and the heavy version. This is a leap forward from previous approaches, which relied on custom-designed sensing and computing platforms. We highlighted how machine learning on smartphones comes with severe costs that need to be mitigated in order to make smartphone capable of real-time data-intensive processing. Some of the costs can be managed with an adapting re-design of the off-the-shelf processing pipeline with additional real-time hyper-parameter control parameters to control the precision and computation cost of the pipeline respect to available resource smartphone in terms of battery duration. We showed that some of the limitations imposed by a mobile sensing application can be overcome by having a multi-tier framework allowing us to split the computation pipeline between the smartphone and two other tiers namely extension-board and cloud, by identifying the bottlenecks in the computation graph. We showed that computation blocks can be can be adopted at execution time leading to further improvement in the resource consumption while maintaining the algorithm accuracy and yet shortening the computation time. We reported on our experience deploying ORBIT at scale with a few case studies as well as multiple deployments on active volcanos in Ecuador and Chile. We extended the scope of our work from platforms to application and presented SPOT. SPOT aims to address some of the challenges discovered in mobile-based smart-home systems. These challenges prevent us from achieving the promises of smart-homes due to heterogeneity in different aspects of smart devices and the underlining systems. This owes to lack of dominating standards in smart-home technologies, leading to the fragmented digital homes rather than truly smart homes. We face the following major heterogeneities in building smart-homes: (i) Diverse appliance control apps (ii) Communication interface, (iii) Programming abstraction. SPOT is an enabling technology for smart-homes system that allows the integration of hetrogenious smart-device seemless by proposing a novel dynamic draver loading schema. SPOT introduces two driver models namely XML-based and library-based allowing the integration and manipulation of smart devices easy for both programmers and users. SPOT makes the heterogeneous characteristics of smart appliances transparent, and by that minimizes the burden of home automation application developers and the efforts of users who would otherwise have to deal with appliance-specific apps and control interfaces. SPOT is evaluated through several benchmarks and three case studies: cross-device programming, central usage analytics and residential energy management via demand response commands. Our evaluation demonstrates the generality of SPOT's design and its driver model. After discussing two aspects of this dissertation namely the framework and the application, we finally presented the algorithmic aspect of the dissertation by introducing two systems in smartphone-based deep learning area: Deep-Crowd-Label and Deep-Partition. Deep neural models are both computationally and memory intensive, making them difficult to deploy on mobile applications with limited hardware resources. On the other hand, they are the most advanced machine learning algorithms suitable for real-time sensing applications used in the wild. Deep- Partition is an optimization based partitioning meta-algorithm featuring a tiered architecture for smartphone and the back-end cloud, which helps to deploy and execute deep neural models more efficiently. Deep-Partition provides a profile-based model partitioning allowing it to intelligently execute the Deep Learning algorithms among the tiers to minimize the smartphone power consumption by minimizing the deep models feed-forward latency. Extensive microbenchmark evaluation and three case studies on representative deep neural models validate the performance gain by Deep-Partition. In addition, we presented Deep-Crowd-Label, a novel algorithm designed for distributed collaborative smartphone systems for crowd-sourcing applications. Deep-Crowd-Label is prototyped for semantically labeling user's location. Deep-Crowd-Label is a crowd-assisted algorithm that uses crowd-sourcing in both training and inference time. It builds deep convolutional neural models using crowd-sensed images to detect the context (label) of indoor locations. It features domain adaptation and model extension via transfer learning to efficiently build deep models for image labeling. By fully exploiting the pre-trained models and available datasets, Deep-Crowd- Label builds ensemble of models to increase the robustness and improve the accuracy of prediction. Moreover, Deep-Crowd-Label aggregates several individual predictions of images obtained from the same location to infer the contextual label of a location. The prototyped system and the preliminary experiments on 26 different in-door locations show the high accuracy of the model and demonstrates the generality and robustness of the underlying approach. The work presented in this dissertation covers three major facets of data-driven and compute-intensive smartphone-based systems, platforms, applications and algorithms; and helps to spurs a new area of research on smartphone sensing and opens up new directions in mobile computing research.
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