277 research outputs found
Integrated IoT Programming with Selective Abstraction
No full textThe explosion of networked devices has driven a new computing environment called the Internet of Things (IoT), enabling various services such as home automation and health monitoring. Despite the promising applicability of the IoT, developing an IoT service is challenging for programmers, because the programmers should integrate multiple programmable devices and heterogeneous third-party devices. Recent works have proposed integrated programming platforms, but they either require device-specific implementation for third-party devices without any device abstraction, or abstract all the devices to the standard interfaces requiring unnecessary abstraction of programmable devices. To integrate IoT devices with selective abstraction, this work revisits the object oriented programming (OOP) model, and proposes a new language extension and its compiler-runtime framework, called Esperanto. With three annotations that map each object to its corresponding IoT device, the Esperanto language allows programmers to integrate multiple programmable devices into one OOP program and to abstract similar third-party devices into their common ancestor classes. Given the annotations, the Esperanto compiler automatically partitions the integrated program into multiple sub-programs for each programmable IoT device, and inserts communication and synchronization code. Moreover, for the ancestor classes, the Esperanto runtime dynamically identifies connected third-party devices, and links their corresponding descendent objects. Compared to an existing approach on the integrated IoT programming, Esperanto requires 33.3% fewer lines of code to implement 5 IoT services, and reduces their response time by 44.8% on average.1
RT-IFTTT: Real-Time IoT Framework with Trigger Condition-aware Flexible Polling Intervals
No full text1
Rapid prototyping of IoT applications with Esperanto compiler
No full textIntegrating various networked devices, the Internet of Things (IoT) enables various new services like home automation, making its market larger and more competitive. Although rapid development of an IoT application is crucial to keep up with the highly competitive IoT market, developing an IoT application is challenging for programmers because the programmers should integrate multiple programmable devices and heterogeneous third-party devices. Some IoT frameworks integrate programming environments of multiple devices, but they either require device-specific implementation for third- party devices without any device abstraction, or abstract all the devices to the standard interfaces requiring unnecessary abstraction of programmable devices. This work introduces the Esperanto framework that integrates IoT devices with selective abstraction, allowing rapid prototyping of an IoT application. Exploiting the correspondence between an object and a thing in the object oriented programming (OOP) model, the Esperanto framework allows programmers to write only one OOP program instead of multiple programs for each device, and to manipulate third-party devices with their common ancestor classes. Compared to an existing approach on the integrated IoT programming, Esperanto requires 33.3% fewer lines of code to implement 5 IoT services, and reduces their response time by 44.8% on average. Moreover, with an empirical study, this work shows that the Esperanto framework reduces the development time by 52.7%.1
FACT: Functionality-centric Access Control System for IoT Programming Frameworks
No full textImprovement in the security and availability is important for the success of the Internet of Things (IoT). Given that recent IoT devices are likely to have multiple functionalities and support third-party applications, this goal becomes challenging to achieve. Through an in-depth investigation of existing IoT frameworks, we focused on two inherent security flaws in their design caused by their device-centric approaches: (1) coarse-grained access control and (2) lack of resource isolation. Because of the coarse-grained access control, IoT devices suffer from over-privileged applications. Furthermore, the lack of resource isolation allows the possibility of Denial-of-Service attacks.
In this paper, we propose a functionality-centric approach to manage IoT devices, called FACT, which has two design goals, namely, the principle of least privilege and the availability in terms of device functionalities. FACT isolates each functionality of the device using Linux Containers and grants a subject the privilege to access for each required functionality. We provide the overall framework and detailed working procedures between components that constitute FACT. We built a prototype of FACT on IoTivity and show that it accomplishes secure and efficient linkages between applications and functionalities of IoT devices through analysis and experiments.1
Cross-Cultural Differences in Perceived Risk of Online Shopping
No full textThis study investigates the perceived risk that has been considered as influencing the consumer purchase decision process during online shopping. For the purpose of this study, perceived risk is defined as the potential for loss in pursuing a desired outcome from online shopping. Specifically, this research investigates the differences in perceived risk between online shoppers and non-online shoppers, as well as online shoppers perceived risk relating to two culturally different countries (i.e., Korea and the United States). The results indicate that the perceived risk is higher for non- (or less-experienced-) online shoppers than for frequent online shoppers, and that both Korean and US Internet users have a similar aggregated degree of perceived risk toward online shopping, though there are significant relative differences in specific risk items (i.e., social, financial, time, and psychological risk), which reflect the existence of the cultural differences in response to the specific risk factors
GPUpd: A Fast and Scalable Multi-GPU Architecture Using Cooperative Projection and Distribution
No full textGraphics Processing Unit (GPU) vendors have been scaling single-GPU architectures to satisfy the ever-increasing user demands for faster graphics processing. However, as it gets extremely difficult to further scale single-GPU architectures, the vendors are aiming to achieve the scaled performance by simultaneously using multiple GPUs connected with newly developed, fast inter-GPU networks (e.g., NVIDIA NVLink, AMD XDMA). With fast inter-GPU networks, it is now promising to employ split frame rendering (SFR) which improves both frame rate and single-frame latency by assigning disjoint regions of a frame to different GPUs. Unfortunately, the scalability of current SFR implementations is seriously limited as they suffer from a large amount of redundant computation among GPUs.
This paper proposes GPUpd, a novel multi-GPU architecture for fast and scalable SFR. With small hardware extensions, GPUpd introduces a new graphics pipeline stage called Cooperative Projection & Distribution (C-PD) where all GPUs cooperatively project 3D objects to 2D screen and efficiently redistribute the objects to their corresponding GPUs. C-PD not only eliminates the redundant computation among GPUs, but also incurs minimal inter-GPU network traffic by transferring object IDs instead of mid-pipeline outcomes between GPUs. To further reduce the redistribution overheads, GPUpd minimizes inter-GPU synchronizations by implementing batching and runahead-execution of draw commands. Our detailed cycle-level simulations with 8 real-world game traces show that GPUpd achieves a geomean speedup of 4.98X in single-frame latency with 16 GPUs, whereas the current SFR implementations achieve only 3.07X geomean speedup which saturates on 4 or more GPUs.1
RTScale: Sensitivity-Aware Adaptive Image Scaling for Real-Time Object Detection
No full textReal-time object detection is crucial in autonomous driving. To avoid catastrophic accidents, an autonomous car should detect objects with multiple cameras and make decisions within a certain time limit. Object detection systems can meet the real-time constraint by dynamically downsampling input images to proper scales according to their time budget. However, simply applying the same scale to all the images from multiple cameras can cause unnecessary accuracy loss because downsampling can incur a significant accuracy loss for some images.
To reduce the accuracy loss while meeting the real-time constraint, this work proposes RTScale, a new adaptive real-time image scaling scheme that applies different scales to different images reflecting their sensitivities to the scaling and time budget. RTScale infers the sensitivities of multiple images from multiple cameras and determines an appropriate image scale for each image considering the real-time constraint. This work evaluates object detection accuracy and latency with RTScale for two driving datasets. The evaluation results show that RTScale can meet real-time constraints with minimal accuracy loss
Building On and Honoring Forty Years of PBL Scholarship from Howard Barrows: A Scientometric, Large-Scale Data, and Visualization-based Analysis
No full textOver the past forty years, Howard Barrows’ contributions to PBL research have influenced and guided educational research and practice in a diversity of domains. It is necessary to make visible to all PBL scholars what has been accomplished, what is perceived as significant, and what is the scope of applicability for Barrows’ groundbreaking findings. As more disciplines recognize Barrows’ efforts and adopt PBL in education, it becomes crucial but challenging to sustain community memory so that PBL scholars are kept well informed of research innovations in various domains. In this paper, we review Barrows’ scholarly efforts in PBL and reveal the impacts on subsequent studies in various domains. A bibliometrics analysis is conducted on Barrows’ PBL publications and the corresponding citations to quantitatively measure Barrows’ impact. Our findings demonstrate Barrows’ exceptional contributions to PBL and the disciplinary differences in conducting PBL studies based on Barrows’ work. It is also revealed that PBL scholars who share similar interests have rarely collaborated with each other. The PBL research community has a real opportunity to connect isolated research groups and reduce the fragmentation so that research innovations in one domain can be disseminated to inform other scholars
Autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor
No full textSound and vibration analysis are prominent tools for machine health diagnosis. Especially, neural network (NN) strategies have focused on finding complex and nonlinear relationships between the sensor signal and the machine status to detect machine faults. However, it is difficult to collect enough amount of fault data as much as normal status data for training general NN models. To resolve the issue, this paper proposes the autoencoder-based anomaly detection framework for industrial robot arms using an internal sound sensor. The autoencoder uses signals in the normal state of the robots for training the model. It reconstructs the input signals as output, and anomalous states are found from high reconstruction error. Two stethoscopes were attached to the surface of the robot joint as sensors, and the sounds were recorded by USB microphone attached to the outlet of the stethoscopes. Features were extracted from STFT spectrogram images of the gathered sound, then used to train and test an autoencoder model. The reconstruction errors of the autoencoder were compared to distinguish the abnormal status from normal one. The experimental results suggest that the stethoscopes prevent the interference of noise, and the collected sound signals can be utilized for detecting machine anomalies.
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