Underwater Wireless sensor Networks (UWSNs) are gaining importance for a wide range of applications, from environmental monitoring to underwater exploration. However, the challenging underwater environment poses signi...
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Withthe continuous development of information technology, the management of university libraries and archives has gradually shifted from the traditional paper management mode to the digital management mode. Digital m...
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the 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effective...
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ISBN:
(纸本)9798350350227;9798350350210
the 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line withthe requirements of 5G green technology. therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. the numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.
First, we introduce the basic theory of multi-sensor cross-fidelity technology, establish a distributedsensor management structure based on a community sensor network model and multi-agent technology, and analyze the...
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this research designs a hardware-software platform for diagnosing the temperature field of a moving oil stream. the theory of distributed parameter systems and similarity theory are chosen as the subject of the study....
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Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whethe...
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ISBN:
(纸本)9781665495127
Publishing data from IoT devices raises concerns of leaking sensitive information. In this paper we consider the scenario of publishing data on events with timestamps. We formulate three privacy issues, namely, whether one can tell if an event happened or not;whether one can nail down the timestamp of an event within a given time interval;and whether one can infer the relative order of any two nearby events. We show that perturbation of event timestamps or adding fake events following carefully chosen distributions can address these privacy concerns. We present a rigorous study of privately publishing discrete event timestamps with privacy guarantees under the Pufferfish privacy framework. We also conduct extensive experiments to evaluate utility of the modified time series with real world location checkin and app usage data. Our mechanisms preserve the statistical utility of event data which are suitable for aggregate queries.
One possibility to extend the service life of engineering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distribu...
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ISBN:
(纸本)9781665497701
One possibility to extend the service life of engineering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. the current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. the final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.
Authentication and integrity are foundational security services for trustworthy systems and the prerequisite of privacy preservation. At the heart of these services lies digital signatures, widely deployed in real-lif...
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ISBN:
(纸本)9798350323856
Authentication and integrity are foundational security services for trustworthy systems and the prerequisite of privacy preservation. At the heart of these services lies digital signatures, widely deployed in real-life applications and supported by various standards. Yet, newly emerging next-generation (NextG) networked systems are vastly distributed, include many resource-limited components, and demand advanced features such as privacy, anonymity, and post-quantum (PQ) security. However, the current signature standards and specialized signatures only meet some of these important requirements in isolation. Hence, there is a significant gap in the state-of-theart in identifying the needs of emerging networked systems and synergizing them withthe features of advanced signatures. In this work, we strive to mitigate this gap by uniting burgeoning ubiquitous systems with advancements in digital signatures and then envisioning the trust via signatures with extended features for NextG networked systems. We investigate the current signature standardizations and advanced constructions for their potentials and drawbacks in three essential aspects of NextG networks - decentralized, privacy-preserving, and resource-constraint settings. We first analyze threshold cryptography efforts proffered by NIST, both from secure multi-party computation and custom design constructions, with applications on distributedsystems like blockchains, federated cloud, and NextG Public Key Infrastructures (PKIs) in mind. We then investigate the intersections of distributed signatures and privacy-preservation techniques for privacy-sensitive NextG applications (e.g., medical, cryptocurrency). We also focus on research gaps for resource and time-limited systems and identify suitable signatures to remedy this gap for security-critical applications (e.g., vehicular networks, smart grids). Finally, we discuss potential directions for these ubiquitous NextG systems and advanced signatures in the PQ era.
this paper presents a vision-based approach for detecting speed bumps, which is crucial for enabling safe and efficient speed control in autonomous vehicles. Given the diverse range of speed bump sizes and characteris...
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ISBN:
(纸本)9798350339024
this paper presents a vision-based approach for detecting speed bumps, which is crucial for enabling safe and efficient speed control in autonomous vehicles. Given the diverse range of speed bump sizes and characteristics encountered in Indian scenarios, a robust detection algorithm is required. To this end, we evaluate two state-of-the-art deep learning-based object detection models, Faster R-CNN and YOLOv5, and compare their performance. Our study specifically focuses on detecting both marked and unmarked speed bumps in real-world environments. However, we also address the challenge of misclassifying pedestrian crosswalks, which can be mistaken for speed bumps due to their similar features. To enhance the accuracy of detecting marked speed bumps, we employ the Negative Sample Training (NST) method. the results show that training with NST improved the detection performance of both Faster R-CNN and YOLOv5 models, achieving an average precision increase of 5.58% and 2.3%, respectively, for marked speed bump detection. Furthermore, we conduct real-time testing of the proposed model on the NVIDIA Jetson platform, which yields an inference speed of 18.5ms per frame.
In recent years, advancements and applications of computer vision techniques such as neural radiance fields (NeRF) and Gaussian splatting have set new expectations of mapping and 3D reconstruction tasks. Achieving hig...
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