The Programmable Data Plane (PDP) is a paradigm of providing in-network computing for computing Power Networks (CPN). With PDP, multiple computing services (e.g., DNS) can be offloaded on edge-network-devices. These i...
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With the development of wireless sensor networks to combat the problem of reaching places otherwise unreachable for humans, there is a need to keep these remote renewable devices charged. Optimizing the network to uti...
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ISBN:
(纸本)9798350385939;9798350385922
With the development of wireless sensor networks to combat the problem of reaching places otherwise unreachable for humans, there is a need to keep these remote renewable devices charged. Optimizing the network to utilize the minimum amount of energy becomes paramount. With the advent of powerful Quantum Variational Algorithms suited for the current Noisy Intermediate Scale Quantum (NISQ) era of quantum computers, we can exploit the power of these quantum processors to solve classically hard problems. In this paper, we use the Quantum Alternating Operator Ansatz (QAOA) followed by Grover Searching, which amplifies the possible paths to find the optimal path in a multi-hop network. We perform experiments using quantum simulators to obtain useful insights into the algorithm's performance with respect to various parameters of interest. Our approach involving QAOA and Grover Searching is a useful benchmark for more general and complex optimization problems in remote renewable wireless networks.
During the last 10 years, Cloud computing has become an evolving technology providing several benefits such as cost reduction and high flexibility. However, one of the main challenges related to cloud computing is rel...
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In this paper, we describe the design and assessment of an Internet of Things (IoT)-based smart air pollution moni-toring system. The system provides accurate, fast, and complete information on air quality through rea...
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For the problem of high computational overhead and high communication overhead faced by resource-constrained IoT devices during federated learning, this thesis proposes a lightweight intrusion detection model. By inte...
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ISBN:
(纸本)9798350379860;9798350379877
For the problem of high computational overhead and high communication overhead faced by resource-constrained IoT devices during federated learning, this thesis proposes a lightweight intrusion detection model. By integrating hybrid feature enhancement units and multi-scale convolutional units, the model significantly reduces both the number of parameters and the computational demands. Experimental results show that the lightweight model exhibits good classification performance in a variety of federated learning scenarios.
This study investigates the application of deep learning models, including CNN, ResNet50, and VGG16, for the early detection of skin cancer. Utilizing a dataset of 3,307 images, the models were trained to classify ski...
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The EDITH project represents a significant advance in the application of machine learning in healthcare. Harnessing the power of massive medical data and sophisticated algorithms, EDITH aims to transform disease detec...
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Compared to H.264/AVC, the next-generation High Efficiency Video coding (HEVC) achieves a 50% reduction in bit rate while maintaining equivalent video quality. However, this improvement comes at the cost of significan...
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One popular technology to improve the processing and storage capacities of vehicular networks (VNs) through the offloading of computing tasks is vehicular edge computing (VEC). Moreover, to provide better services for...
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ISBN:
(纸本)9781665462686
One popular technology to improve the processing and storage capacities of vehicular networks (VNs) through the offloading of computing tasks is vehicular edge computing (VEC). Moreover, to provide better services for users in proximity, microservices can be dynamically deployed, easily migrated among edge clouds on demand, and launched rapidly in a VEC environment. However, the environment of VNs is rapidly changing and unpredictable, making it difficult to provide service with low latency. Therefore, in order to deliver real-time services in microservice-enabled VNs, a multi-armed bandit (MAB) learning-based computation offloading (MLCO) strategy is introduced in this study. The proposed scheme enables that vehicles can learn the offloading delay performance of the candidates while offloading computing tasks. Furthermore, we modified the MAB algorithms and added an input-awareness strategy to our proposed algorithm for adapting to a rapidly changing task offloading vehicular environment. Extensive simulation results show that our proposal outperforms other existing baselines in terms of average service latency and successfully offloads more tasks in different scenarios.
In order to improve the coding performance, the H.266/VVC video coding standard adds binary tree split and ternary tree split to complement quadtree split. It also expands the number of angle prediction modes from 33 ...
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