Wireless Networking

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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According to the different proportion of agent loss and link loss in faults, five experimental data sets are designed. Among them, the fault type corresponding to data set A is agent loss where the proportion of link loss in faults is 0%, the fault type of data set B, C and D is agent & link loss where the proportion of link loss in faults is 25%, 50% and 75% respectively, and the fault type of data set E is link loss where the proportion of link loss in faults is 100%.

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The increasing complexity of cellular networks has resulted in dynamic network performance optimization (NPO) playing a critical role in streamlining network operations. While the success of NPO techniques primarily depends upon the quality and quantity of telemetry data available from the underlying network, up until now, third-party access to such data has been largely limited due to the prevalence of proprietary interfaces throughout the access network. However, the upcoming open radio access network (RAN) architecture is set to change this trend.

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Dataset Description:

Based on some real-world events, the dataset offers a synthetic representation of 5G network states and metrics during a high traffic event, such as a major sports gathering in a city. Each row corresponds to a unique record capturing the attributes of the network at a particular moment, and each column corresponds to a specific feature or attribute.

Significance:

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1034 Views

Seismic data, obtained from sensors placed on the Earth's surface or subsurface, provides valuable insights into the composition and structure of the Earth's subsurface layers. This data is typically collected in the form of digital recordings, which represent the vibrations produced by seismic waves generated by controlled sources or natural events such as earthquakes. Converting seismic data into speech signals allows researchers and professionals to gain aural insights into the subsurface characteristics.

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91 Views

Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system.

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55 Views

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

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300 Views

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

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216 Views

Specific emitter identification (SEI)  is a promising authentication paradigm in physical layer security (PLS). Despite the significant success of existing SEI schemes, most of them assume that the distributions of the training dataset and the test dataset are consistent. However, in most practical scenarios, when the signal parameters change, the distribution of the samples will changes,  resulting in a significant performance degradation.

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184 Views

Extended reality (XR) head-mounted displays (HMDs) are increasingly starting to rely on wireless task
offloading in a bid to allow unobstructed XR user movement, while still rendering high-resolution video on
a remote processing node. An example is the Oculus (Meta) Quest 2. However, congestion and reliability
issues associated with the wireless network can cause high latency and an overall low quality of service (QoS).
Therefore, understanding XR user mobility is of vital importance for supporting XR applications in future
wireless networks.

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