With the rapid development of wireless sensor networks(WSNs), Mobile Wireless sensor Networks (MWSNs) are receiving increased attention. MWSNs affect the performance of clustered networks, such as coverage and lifetim...
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Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large ...
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The increasing need for remote monitoring within distributedsensor networks has underscored the importance of developing compact and cost-effective sensing solutions. One specific area of emphasis in this context is ...
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ISBN:
(纸本)9798350380903;9798350380910
The increasing need for remote monitoring within distributedsensor networks has underscored the importance of developing compact and cost-effective sensing solutions. One specific area of emphasis in this context is the application of gas sensing. This work proposes the feasibility of a cost-effective, space-efficient, and smart electronic interface for a photoacoustic-based gas sensor aimed to avoid the typical use of sophisticated and bulky readout instruments, that pose challenges in integration into smart, low-cost, stand-alone acquisition systems. The proposed electronic interface is designed to adapt the challenging gas-dependent photoacoustic signal to be acquired by an ADC interface with limited voltage full-scale and processed with low computational resource, characteristic of low-power systems. The designed conditioning electronic focuses on the optimization of system gas sensitivity in the concentration range of interest with optimal resolution. The performance of the developed prototype was experimentally validated for NO2 sensing within the concentration range of 5 ppm to 45 ppm. The assessed gas sensitivity is 65 mV/ppm of NO2, enabling the attainment of a system resolution of 660 ppb of NO2. The results demonstrate high responsiveness and robustness to noise interference. Furthermore, the impact of the characterization setup, specifically the mass flow rate of the gas, on the evaluated performance of the sensing system was explored.
Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in distributedcomputing Continuum systems (DCCS). Implementing centralized ZT architecture is seen as unsuitab...
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The rapid expansion of Wireless sensor Networks (WSNs) has made them a critical component in various applications, from environmental monitoring to military surveillance. However, their inherent vulnerabilities make t...
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There has been a large increase in Internet of Things (IoT) devices used in many parts of life. These devices, also called edge devices, have been known to be vulnerable to attackers and to be used as a medium for net...
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ISBN:
(纸本)9798350387919;9798350387902
There has been a large increase in Internet of Things (IoT) devices used in many parts of life. These devices, also called edge devices, have been known to be vulnerable to attackers and to be used as a medium for network attacks. A simple loT device uses one or more sensors to read data from the real world and performs a simple action based on the sensor readings, such as a motion sensor in a home turning on the lights when motion is detected. Building a dataset to model real-world network traffic, along with pseudo-realistic uses of network attacks is essential for creating an intrusion detection system (IDS). In this paper, we develop an loT network anomaly dataset using synthetic and real-world network attacks. Virtual machines with Ubuntu 22.04 were used to contain the network data in a sandboxed environment. For our edge devices, we use Le Potato by Libre Computers as an alternative to Raspberry Pi 3, which is a popular single board computer used for home automation and hobbyist projects where the Raspberry Pi acts as the computer for the smart device. Each device has Waveshare Weather sensor attached to it, which reads climate data such as temperature, humidity, and barometric pressure. The edge devices are located within the Cyber Defense and AI lab at North Carolina Agriculture and Technology State University. Real network attack tools were used to generate traffic to label the current data as attack data, along with the attack type. In the absence of the attack tools in use, the network traffic is labeled normal, or benign. The dataset is comprised of homogeneous data using environmental sensors that measure local climate data. In this study, we use XGBoost, LSTM+CNN, and Multilayer Perceptron along with various resampling methods to evaluate our synthetic dataset. In our results, we find that XGBoost produces the best performance at classifying attacks when using RandomUndersampler.
作者:
Zhang, ChenRen, HongruMa, HuiZhou, QiSchool of Automation
Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics
Guangdong University of Technology Guangzhou510006 China
This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computingsystems. Firstly, a novel cloud computing system architecture is designed, in which ...
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Wireless personal sensor networks (WPSNs) are an emerging technology that provides a platform for monitoring and collecting data from a variety of sources. These networks are used to monitor environmental, health, and...
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The rising demand for deploying low-latency data analysis and protecting privacy in a cloud-based setting has led to the emergence of federated learning (FL) as an important learning paradigm over wireless sensor netw...
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ISBN:
(纸本)9798350304060;9798350304053
The rising demand for deploying low-latency data analysis and protecting privacy in a cloud-based setting has led to the emergence of federated learning (FL) as an important learning paradigm over wireless sensor networks. Due to the success of FL, generative models such as generative adversarial networks (GANs) are now utilized in FL to provide higher privacy and utility. However, existing GAN-based FL approaches are power-hungry which poses unbearable demands on resource-limited distributed users. Considering practical learning systems involving limited computational power and unlabeled data over wireless networks, this work investigates FL in a resource-constrained and label-free data environment. Specifically, we propose a novel framework known as UFed-GAN that captures sensor-side data distribution without local classification training. We analyze the convergence and privacy of the proposed UFed-GAN. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.
UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and traceability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market...
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ISBN:
(纸本)9781665491907
UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and traceability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market-based methods are widely favored but they rely on the environmentally influenced communication network to complete negotiation, while the onboard computing of UAV is robust and redundant. This paper proposes a distributed scheduling method for networked UAV swarm based on computing for communication, which trades a modest increase in computing for a significant decrease in communication. First, by analyzing the task removal strategies of two representative methods, the consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, a new removal strategy is proposed, which expands the exploration of the bundle and can potentially reduce communication rounds. Second, the proposed task-related optimization method can extract task conflict nodes from the native communication protocol, and use the sampling and estimation strategies to resolve task conflicts in advance. Third, historical bids are cleverly used to infer others' locations, which is necessary for task-related optimization. Fourth, to verify the algorithm in real communication, a hardware-in-the-loop (HIL) ad-hoc network simulation system is constructed, which uses real network protocols and simulated channel transmissions. Finally, the HIL Monte Carlo simulation results show that, compared with CBBA and PI, the proposed method can significantly reduce the number of communication rounds and the total scheduling time, without increasing the communication protocol overhead and loss of optimization.
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