Wireless Networking

This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks  k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using a tangent type Lyapunov barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching.

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Three synthetic and validated datasets correspond to three typical vehicular communication scenarios. Each subdataset comprises approximately 15000 to 28000 samples that are MAC observations in each CCHI of all vehicles in the network during the travel on the road segment of 2km. Each sample includes six most concerned features: number of successful received packets ls, number of failed transmission on the medium lc, number of error received packets le, number of packet transmitted by the given vehicle ltx, time step ti and estimated number of neighbor vehicles n.

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Good knowledge about a radio environment, especially about the radio channel, is a prerequisite to design and operate ultra-reliable communications systems. Radio Environment Maps (REMs) are therefore a helpful tool to gain channel awareness. Based on a user’s location, the channel conditions can be estimated in the surrounding of the user by extracting the information from the radio map. This data set contains two measured high-resolution REMs of an indoor environment.

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 Millimeter-wave (mmWave) spectrum with wide bandwidth provides a promising solution to enable high throughput in next-generation wireless agricultural networks, characterized by swarms of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels due to the dynamic crop growth behavior.

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The dartmouth/wardriving dataset (v. 2006-06-02)

Location-aware dataset for analyzing the errors in location estimates.

 

The authors collected the dataset through war driving, i.e., collecting Wi-Fi beacons by driving or walking around Dartmouth Campus, to understand the effect of using estimated AP locations.

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MANET dataset of outdoor experments for comparing differnet routing algorithms.

This dataset contains outdoor runs of MANET (Mobile Ad-hoc network) routing algorithms to compare the performance of four different routing algorithms.

date/time of measurement start: 2003-10-17

date/time of measurement end: 2003-10-17

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Syslog, SNMP, and tcpdump data for 3 years or more from wireless network at Dartmouth College.

This dataset includes syslog, SNMP, and tcpdump data for 3 years or more, for over 450 access points and several thousand users at Dartmouth College.

Note: This dataset has multiple versions.  The dataset file names of the data associated with this version are listed below, under the 'Traceset' heading and can be downloaded under 'Dataset Files' on the right-hand side of the page.

last modified :

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This dataset is in support of my Research paper 'Design of 6-DoF Combat Quadcopter'.

Preprint:   

The system is basic, on existing designs.It is very simple for any graduate,degree holder.

 

Related Claim : Novel ß Non-Linear Theory

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