Robot systems are currently considered an effective and important means in human life, as they replace humans in many complex tasks at a lower cost. The most important stages that are needed to design any robotic syst...
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Serverless computing offers a scalable event-based paradigm for deploying managed cloud-native applications. Function triggers are essential building blocks in serverless, as they initiate any function execution. Howe...
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ISBN:
(数字)9781665491150
ISBN:
(纸本)9781665491150
Serverless computing offers a scalable event-based paradigm for deploying managed cloud-native applications. Function triggers are essential building blocks in serverless, as they initiate any function execution. However, function triggering is insufficiently studied and inherently hard to measure given the distributed, ephemeral, and asynchronous nature of event-based function coordination. To address this gap, we present TriggerBench, a cross-provider benchmark for evaluating serverless function triggers based on distributed tracing. We evaluate the trigger latency (i.e., time to transition between two functions) of eight types of triggers in Microsoft Azure and three in AWS. Our results show that all triggers suffer from long tail latency, storage triggers introduce variable multi-second delays, and HTTP triggers are most suitable for interactive applications. Our insights can guide developers in choosing optimal event or messaging triggers for latency-sensitive applications. Researchers can extend TriggerBench to study the latency, scalability, and reliability of further trigger types and cloud providers.
ROS is popular in robotic-software development, and thus detecting bugs in ROS programs is important for modern robots. Fuzzing is a promising technique of runtime testing. But existing fuzzing approaches are limited ...
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ISBN:
(纸本)9781728196817
ROS is popular in robotic-software development, and thus detecting bugs in ROS programs is important for modern robots. Fuzzing is a promising technique of runtime testing. But existing fuzzing approaches are limited in testing ROS programs, due to neglecting ROS properties, such as multi-dimensional inputs, temporal features of inputs and the distributed node model. In this paper, we develop a new fuzzing framework named ROZZ, to effectively test ROS programs and detect bugs based on ROS properties. ROZZ has three key techniques: (1) a multi-dimensional generation method to generate test cases of ROS programs from multiple dimensions, including user data, configuration parameters and sensor messages;(2) a distributed branch coverage to describe the overall code coverage of multiple ROS nodes in the robot task;(3) a temporal mutation strategy to generate test cases with temporal information. We evaluate ROZZ on 10 common robotic programs in ROS2, and it finds 43 real bugs. 20 of these bugs have been confirmed and fixed by related ROS developers. We compare ROZZ to existing approaches for testing robotic programs, and ROZZ finds more bugs with higher code coverage.
Sparkfun AS7265x is a reflectance spectroscopy sensor with a wide range of wavelengths falling in UV, Visible and IR ranges. Using this sensor, a small device was developed by assembling it with Arduino Uno to make a ...
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The integration of edge computing and deep neural networks (DNNs) holds great promise for enhancing application intelligence. Edge devices generate or collect vast amounts of data, which DNNs can leverage to make info...
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ISBN:
(纸本)9798350358261;9798350358278
The integration of edge computing and deep neural networks (DNNs) holds great promise for enhancing application intelligence. Edge devices generate or collect vast amounts of data, which DNNs can leverage to make informed decisions. Nevertheless, the limited resources of edge devices pose a significant challenge for deploying DNNs. To accommodate some edge devices (e.g. smart watches), lightweight models are often required. However, the accuracy of these models may not meet user expectations. In this paper, we present EKDF, an ensemble knowledge distillation framework that crafts lightweight models for collaborative DNN inferences. More specifically, we utilize knowledge distillation to compress DNN models. On this basis, we introduce multi-teacher joint supervision and dropout in knowledge distillation to improve model performance and preserve the diversity between the generated DNN models. This process produces a range of compact models of varying computational complexity for different edge devices. The experimental results demonstrate that our proposed EKDF can greatly improve the overall predictive ability.
Cluster heads are the primary consideration used in Wireless sensor Networks (WSN) to transfer data from one point to another point. A wireless sensor network's energy usage can be decreased by minimizing the node...
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Internet of Things (IoT) domains are characterized by continuous streams of data originating from diverse, geographically distributedsensors. sensor/network failures that result in data stream interruptions is a majo...
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ISBN:
(纸本)9781665462839
Internet of Things (IoT) domains are characterized by continuous streams of data originating from diverse, geographically distributedsensors. sensor/network failures that result in data stream interruptions is a major challenge in applying ML techniques to IoT domains. Unfortunately, the performance of many ML applications quickly degrades when faced with data incompleteness. With the aim of building robust IoT-coupled ML applications, this paper proposes SECOE - a unique, proactive approach for alleviating potentially simultaneous sensor failures. The fundamental idea behind SECOE is to create a carefully chosen ensemble of ML models in which each model is trained assuming a set of failed sensors. SECOE includes a novel technique to minimize the number of models in the ensemble by harnessing the correlations among sensors. We demonstrate the efficacy of the SECOE approach through a series of experiments involving two distinct datasets.
A wireless sensor network (WSN) monitors and records the physical state of the environment, relays the collected data to a central location, and records it to track geophysical processes over a long period of time in ...
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Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels. Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimat...
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The potential applications of Wireless senor networks (WSN) has increased rapidly in weather reporting, target tracking, oxygen content monitoring and many other applications due to their low cost and less power requi...
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