Wireless Networking
ABSTRACT This paper provides a study scenario of mobility control for the unmanned aerial vehicles that mount base stations, which are called UAVBSs. Mobility control of UAVBSs is considered a solution to serious problems such as interference and physical collisions between these UAVBSs. Thus, we formulated the problem of UAVBSs mobility control as an exact potential game, and we proposed three algorithms in this work to find the solution for that problem.
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This dataset is collected to benchmark different software algorithms for UWB indoor localization. The dataset is collected in a controlled environment with mm-accurate groundtruth to evaluate different algorithms. The data is collected with the Wi-Pos UWB system,a platform developed for research data collection with a wireless long-range Sub-GHz backbone combined with UWB ranging based on the DW1000. The UWB is configured with a center frequency of 6489.6 MHz, a bandwidth of 499.2 MHz (channel 5) and a preamble length of 1024 symbols.
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In the process of target tracking and localization in bearing-only sensor network, it is an essential and significant challenge to solve the problem of plug-and-play expansion while enhancing the accuracy of state estimation and stability of the system. This paper proposes the distributed cubature information filtering method based on the two-layer factor graph.
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The most popular and active area of data mining study is sentiment analysis. Twitter is a crucial platform for collecting and distributing people's thoughts, feelings, views, and attitudes regarding specific entities. There are several social media networks available today. In light of this, sentiment analysis in natural language processing (NLP) field became fascinating. Different techniques have been developed for sentiment analysis. However, there is still a need for improvement in terms of accuracy and system effectiveness.
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This dataset presents a collection of real-world RF signals encompassing three prominent wireless communication technologies: Wi-Fi (IEEE 802.11ax), LTE, and 5G. The data aims to facilitate advanced research in spectrum analysis, interference identification, and wireless communication optimization. The signals were meticulously captured under varying conditions to ensure a broad representation of real-world scenarios, including different modulation schemes, channel conditions, and data rates.
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5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices.
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This dataset contains adversarial attacks on Deep Learning (DL) when it is employed for the classification of wireless modulated communication signals. The attack is executed with an obfuscating waveform that is embedded in the transmitted signal in such a way that prevents the extraction of clean data for training from a wireless eavesdropper. At the same time it allows a legitimate receiver (LRx) to demodulate the data.
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This dataset contains adversarial attacks on Deep Learning (DL) when it is employed for the classification of
wireless modulated communication signals. The attack is executed with an obfuscating waveform that is embedded in the
transmitted signal in such a way that prevents the extraction of clean data for training from a wireless eavesdropper. At the
same time it allows a legitimate receiver (LRx) to demodulate the data. The scheme works for both single carrier and multi-carrier
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Modern, industrial use cases for wireless communications are related to mobile applications such as moving robotics in industrial environments. For the design of communication systems, the behavior of the radio channel, especially over time, is of great importance. Most of the existing data sets for industrial radio channels originate from static measurement procedures, containing an arbitrary subset of the environment.
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The dataset, titled "SensorNetGuard: A Dataset for Identifying Malicious Sensor Nodes," comprises 10,000 samples with 21 features. It is designed to facilitate the identification of malicious sensor nodes in a network environment, specifically focusing on IoT-based sensor networks.
General Metrics
§ Node ID: The unique identifier for each node.
§ Timestamp: The time at which data or a packet is sent or received.
§ IP Address: Internet Protocol address of the node.
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