Strong and flexible detection systems are vital to protect network infrastructures from distributed Denial of Service (DDoS) attacks, which are becoming more common and sophisticated. To detect DDoS attacks, this stud...
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Wireless sensor devices, edge computing, and fog computing can transform education by improving efficiency and empowering institutions. This research paper explores the application of these technologies in educational...
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This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed f...
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With the rising popularity of Unmanned Aircraft systems (UASs), soon large amounts of UAS are projected to inhabit the low-level airspace. As the skies are getting more and more crowded, it is essential and mandatory ...
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ISBN:
(纸本)9798350304367;9798350304374
With the rising popularity of Unmanned Aircraft systems (UASs), soon large amounts of UAS are projected to inhabit the low-level airspace. As the skies are getting more and more crowded, it is essential and mandatory to provide up-to-date information on vehicles' identities, positions and intentions. One of the main use cases of communications among UASs is the coordination and guidance of vehicles, which is referred to as Unmanned Aircraft System Traffic Management (UTM). Despite numerous proposed data link technologies for intra-UAS communication, there is often a lack of clarity regarding the underlying performance requirements. Therefore, the need arises to quantify the required data rate, delay budget and communication range so that suitable data link technologies can be selected. To gain insight into these requirements, we developed a stochastic communication model and applied it to future UAS traffic scenarios for major German cities. These scenarios are based on predictions of UAS traffic demand generated by specific applications, such as parcel delivery. The proposed model estimates that a communication range of less than 500m is sufficient. The delay budget strongly depends on the diameter and the spatial density of the network but remains lower than 110 ms. For large cities like Berlin, a data rate of 19 Mbps is predicted for the year 2035 which is challenging for many current communication technologies.
Crowd sensing is a way to obtain multiple sensing data onto users or mobile devices and is widely used in industrial Internet, smart city, smart medical, etc. However, when users upload sensing data involving sensitiv...
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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 with the 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.
distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at ...
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ISBN:
(纸本)9798350304817
distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks. Although many gradient compression techniques propose to reduce communication cost, the ideal compression factor that leads to maximum speedup or minimum data exchange remains an open-ended problem since it varies with the quality of compression, model size and structure, hardware, network topology and bandwidth. We propose GraVAC, a framework to dynamically adjust compression factor throughout training by evaluating model progress and assessing gradient information loss associated with compression. GraVAC works in an online, black-box manner without any prior assumptions about a model or its hyperparameters, while achieving the same or better accuracy than dense SGD (i.e., no compression) in the same number of iterations/epochs. As opposed to using a static compression factor, GraVAC reduces end-to-end training time for ResNet101, VGG16 and LSTM by 4.32x, 1.95x and 6.67x respectively. Compared to other adaptive schemes, our framework provides 1.94x to 5.63x overall speedup.
Operations on large file trees in a DFS (distributed File System)-server are a bottleneck in large-scale cloud computing, such as distributed build systems for large software projects. Such operations take much longer...
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This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable ...
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Obstacle avoidance is a critical aspect of robotics that plays a vital role in ensuring safe and efficient operations. Detecting obstacles in complex real-Time environments poses several challenges in robotics. For ex...
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