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Robustness in context-aware mobile computing

Robustness in Context-Aware Mobile Computing

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6th Annual IEEE International Conference on Wireless and mobile computing, Networking and Communications (WiMob)/STMWC/NASSUE/CMPA/VECON/AWSAN/AWSAN

作者： Wolf, Hannes Herrmann, Klaus Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst D-7000 Stuttgart Germany

ISBN: (纸本)9781424477425

High level context recognition and situation detection are enabling technologies for unobtrusive mobile computing systems. Significant progress has been made in processing and managing context information, leading to sophisticated frameworks, middlewares, and algorithms. Despite great improvements, context aware systems still require a significantly increased recognition accuracy for high-level context information on uncertain sensor data to enable the robust execution of context-aware applications. Recently Adaptable Pervasive Work-flows (APF)s have been presented as innovative programming paradigm for mobile context-aware applications. We propose a novel Flow context System (FlowCon) that builds upon APFs. FlowCon uses structural information from the APF to increase accuracy of uncertain high-level context information up to 49%. This way we make an important step to enable robust execution of mobile context-aware applications.

关键词： context-aware mobile computing flows uncertain high-level context information robustness

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SpecTrans: Versatile Material Classification for Interaction with Textureless, Specular and Transparent Surfaces 15

SpecTrans: Versatile Material Classification for Interaction...

引用

33rd Annual CHI Conference on Human Factors in computing Systems (CHI)

作者： Sato, Munehiko Yoshida, Shigeo Olwal, Alex Shi, Boxin Hiyama, Atsushi Tanikawa, Tomohiro Hirose, Michitaka Raskar, Ramesh MIT Media Lab Cambridge MA 02139 USA Univ Tokyo Bunkyo Ku Tokyo Japan

ISBN: (纸本)9781450331456

Surface and object recognition is of significant importance in ubiquitous and wearable computing. While various techniques exist to infer context from material properties and appearance, they are typically neither designed for real-time applications nor for optically complex surfaces that may be specular, textureless, and even transparent. These materials are, however, becoming increasingly relevant in HCI for transparent displays, interactive surfaces, and ubiquitous computing. We present SpecTrans, a new sensing technology for surface classification of exotic materials, such as glass, transparent plastic, and metal. The proposed technique extracts optical features by employing laser and multi-directional, multispectral LED illumination that leverages the material's optical properties. The sensor hardware is small in size, and the proposed classification method requires significantly lower computational cost than conventional image-based methods, which use texture features or reflectance analysis, thereby providing real-time performance for ubiquitous computing. Our evaluation of the sensing technique for nine different transparent materials, including air, shows a promising recognition rate of 99.0%. We demonstrate a variety of possible applications using SpecTrans' capabilities.

关键词： Sensors material classification laser speckle multi-spectral sensing ubiquitous computing context-aware mobile computing

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A Service Architecture for Sensor Data Provisioning for context-aware mobile Applications 08

A Service Architecture for Sensor Data Provisioning for Cont...

引用

23rd Annual ACM Symposium on Applied computing

作者： Goncalves, Bernardo Pereira Filho, Jose G. Guizzardi, Giancarlo Univ Fed Espirito Santo Dept Comp Sci Vitoria ES Brazil

ISBN: (纸本)9781595937537

One of the main issues that inhibit the development of context-aware mobile applications is the lack of systematic methods for sensor data acquisition. This lack, however, is a result of the diversity of sensor data and its acquisition devices. In face of this, there is a need for general engineering solutions in order to address the common sensor data acquisition concerns. This paper presents a service-oriented architecture that allows the rapid prototyping of sensor data provisioning systems. This architecture is then applied to the Healthcare domain for providing cardiac signals in the scope of a context-aware telemonitoring system. The architecture is defined by entity and behavior models through a service-oriented design (SOD) language that has tool support.

关键词： context-aware mobile computing Pervasive computing Domain engineering sensor data acquisition service-oriented architecture

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Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

Deep Thermal Imaging: Proximate Material Type Recognition in...

引用

CHI Conference on Human Factors in computing Systems (CHI)

作者： Cho, Youngjun Bianchi-Berthouze, Nadia Marquardt, Nicolai Julier, Simon J. UCL London England

ISBN: (纸本)9781450356206

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognize 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.

关键词： Material recognition in the wild deep learning sensing context-aware mobile computing thermal imaging

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A Framework for Continuous Group Activity Recognition Using mobile Devices: Concept and Experimentation 15

A Framework for Continuous Group Activity Recognition Using ...

引用

15th IEEE International Conference on mobile Data Management (IEEE MDM)

作者： BakhshandehAbkenar, Amin Loke, Seng W. Rahayu, Wenny La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic Australia

ISBN: (纸本)9781479957057

Group Activity Recognition (GAR) is a challenging research area in context-aware computing which has attracted much attention recently. Many studies have been conducted in the field of activity recognition (AR) along with their applications in domains such as health, smart homes, daily living and life logging. However, still many open issues exist. Lack of an energy-efficient approach is one of the most vital issues in the context of AR. GAR work often suffers from energy consumption issues for the reason that, apart from AR process, there is the requirement to have more interaction among members of the group and a need to run more complex recognition processes. Moreover, almost all work in GAR are technology-oriented and assume that our real-life environment remains fixed once the system has been established, but this may not be the case. Hence, we propose a framework called Group(1)Sense for GAR towards addressing these issues. Also, a relatively simple scheme for GAR, with a protocol for the exchange of information required for GAR, has been implemented, tested and evaluated. We then conclude with lessons learnt for GAR.

关键词： group activity recognition energy efficient group activity recognition context-aware mobile computing mobile sensing

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Estimating Local Coverage Areas for Location Dependent Queries 18

Estimating Local Coverage Areas for Location Dependent Queri...

引用

33rd Annual ACM Symposium on Applied computing (ACM SAC)

作者： Bernad, Jorge Bobed, Carlos Mena, Eduardo Univ Zaragoza Zaragoza Spain Univ Rennes CNRS IRISA Rennes France

ISBN: (纸本)9781450351911

Location-Based Services (LBS) are currently attracting many research efforts due to the pervasiveness of mobile devices in our daily life. Mechanisms to monitor distributedly the remote (and sometimes huge or dynamic) geographic areas referenced from location-based constraints are needed in order to retrieve the required data. In some cases (e.g., ad hoc networks), selecting the best places from which to scan a certain area is still an open problem;but moreover, it depends on another open problem: We should know the actual area that each object can scan (i.e., its communication coverage area) from its location in order to be sure that the whole interesting area is actually covered from a certain set of objects. Different disk-based coverage area models are usually adopted;however, knowing the real communication coverage area of an object is a very difficult task. Therefore, when accuracy is important, there is a need of a mechanism that estimates somehow the unknown real coverage area that a certain object is able to see, anytime and anywhere. In this paper, we face the problem of estimating the real (and unknown) coverage area of a certain (mobile or static) object in a wireless scenario by considering as input data just the location of the devices that can communicate with that object at each time. To do this task we propose different algorithms, including a combination of all of them, and compare their accuracy and efficiency against a set of synthetic and real scenarios with different topologies and obstacles. We show that estimating the real coverage area of an object without assuming a disk of fixed radius is not only possible, but also simple and efficient.

关键词： Coverage Area Estimation Location Based Services context-aware mobile computing Location-aware applications

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MyActivity: Cloud-Hosted Continuous Activity Recognition using Ontology Based Stream Reasoning

MyActivity: Cloud-Hosted Continuous Activity Recognition usi...

引用

IEEE International Conference on mobile Cloud computing, Services, and Engineering

作者： Amin BakhshandehAbkenar Seng W. Loke Department of Computer Science and Computer Engineering. La Trobe University

ISBN: (纸本)9781479944248

Activity recognition and context-aware systems with mobile users have attracted much attention recently, with numerous applications. Also, for a system to be scalable, cloud computing is a useful paradigm, but needs to be appropriately integrated. This paper presents an innovative approach for mobile activity recognition that exploits a hybrid model using both machine learning techniques and stream reasoning using an ontological representation of activities. As a proof-of-concept, we implemented a cloud-hosted framework called MyActivity using Continuous SPARQL (C-SPARQL) to run queries in order to recognize human activities with accelerometer and GPS data from mobile devices.

关键词： Continuous activity recognition Stream reasoning context-aware mobile computing mobile cloud application

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