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60 results for “communication networks”
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
<p>Emitting conspicuous signals into the environment to attract mates comes with the increased risk of interception by eavesdropping enemies. As a defence, a commonly described strategy is for signallers to group together in leks, diluting each individual's risk. Lekking systems are often highly social settings in which competing males dynamically alter their signalling behaviour to attract mates. Thus, signalling at the lek requires navigating fluctuations in risk, competition, and reproductive opportunities. Here, we investigate how behavioural defence strategies directed at an eavesdropping enemy have cascading effects across the communication network. We investigated these behaviours in the túngara frog (<em>Engystomops pustulosus</em>), examining how a calling male's swatting defence directed at frog-biting midges indirectly affects the calling behaviour of his rival. We found that the rival responds to swat-induced water ripples by increasing his call rate and complexity. Then, performing phonotaxis experiments, we found that eavesdropping fringe-lipped bats (<em>Trachops cirrhosus</em>) do not exhibit a preference for a swatting male compared to his rival, but females strongly prefer the rival male. Defences to minimize attacks from eavesdroppers thus shift the mate competition landscape in favour of rival males. By modulating the attractiveness of signalling prey to female receivers, we posit that eavesdropping micropredators likely have an unappreciated impact on the ecology and evolution of sexual communication systems.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
The effect of co-location on human communication networks
<p>Representative dataset for "The effect of co-location on human communication networks." The files are serialized python objects pickled using python 3.8. dist_dict_* contains data on the pairwise distance between researchers, while undir_semiactive_* contains networks representing daily email counts between researchers.</p>
A Dataset for Exploring Wi-Fi Network Diversity in Vehicle-to-Infrastructure Communication
<p><strong>Introduction:</strong></p> <p>This dataset contains space and time-indexed performance data for Wi-Fi communication between a moving vehicle and a set of stationary Access Points (APs). In order to allow comparisons between technologies, 3 different types of Wi-Fi are used in parallel: 800.11n, ac, and ad.</p> <p>For more information, please consult the following article: <a href="https://www.cs.vassar.edu/~rpachecomeireles/research/papers/vnc-2020.pdf"><em>Exploring Wi-Fi Network Diversity for Vehicle-to-Infrastructure Communication</em></a>, Rui Meireles, António Rodrigues, Andrei Stanciu, Ana Aguiar, Peter Steenkiste, in the 2020 IEEE Vehicular Networking Conference (VNC 2020), December 2020, <a href="https://doi.org/10.1109/VNC51378.2020.9318407">doi:10.1109/VNC51378.2020.9318407</a>. Video presentation available <a href="https://youtu.be/IREMIGV4XLc">here</a>.</p> <p><strong>Experiment description:</strong></p> <ul> <li> <p>The AP was placed at the corner of a residential area intersection while the mobile client drove a circuit around it. The mobility pattern is shown in the animated file <code>vehicle-movement.gif</code>.</p> </li> <li> <p>The data is divided into traces, gathered on different dates, using different vehicles to support the AP on the roof, as shown below:</p> </li> </ul> <table> <tbody><tr> <th>trace nr</th> <th>date</th> <th>start time</th> <th>n(n)</th> <th>n(ac)</th> <th>n(ad)</th> <th>AP vehicle</th> <th>n(clients)</th> </tr> </tbody><tbody> <tr> <td>302</td> <td>2019-08-20</td> <td>10:28:45</td> <td>3262</td> <td>2787</td> <td>423</td> <td>2001 Honda Civic sedan</td> <td>1</td> </tr> <tr> <td>303</td> <td>2019-08-20</td> <td>11:26:23</td> <td>3374</td> <td>3027</td> <td>312</td> <td>-</td> <td>2</td> </tr> <tr> <td>304</td> <td>2019-08-20</td> <td>12:39:46</td> <td>1711</td> <td>216</td> <td>14</td> <td>-</td> <td>3 (n & ac) 2 (ad)</td> </tr> <tr> <td>401</td> <td>2019-08-22</td> <td>10:19:24</td> <td>1685</td> <td>1681</td> <td>545</td> <td>2003 Peugeot Partner</td> <td>1</td> </tr> <tr> <td>402</td> <td>2019-08-22</td> <td>10:48:26</td> <td>2859</td> <td>2827</td> <td>764</td> <td>-</td> <td>2</td> </tr> <tr> <td>403</td> <td>2019-08-22</td> <td>11:39:36</td> <td>135</td> <td>135</td> <td>116</td> <td>-</td> <td>2</td> </tr> <tr> <td>404</td> <td>2019-08-22</td> <td>11:42:50</td> <td>114</td> <td>114</td> <td>53</td> <td>-</td> <td>2</td> </tr> <tr> <td>405</td> <td>2019-08-22</td> <td>11:45:07</td> <td>2019</td> <td>2019</td> <td>507</td> <td>-</td> <td>2</td> </tr> </tbody> </table> <ul> <li><strong>APs:</strong> all positioned at coordinates {lat : 41.111879, lon : -8.631146}</li> </ul> <table> <tbody><tr> <th>ap</th> <th>device</th> <th>802.11 type</th> <th>channel</th> <th>cntr. freq (MHz)</th> <th>bw (MHz)</th> </tr> </tbody><tbody> <tr> <td>unifi-003</td> <td>ubiquiti ac lite</td> <td>n</td> <td>6</td> <td>2437</td> <td>20</td> </tr> <tr> <td>unifi-001</td> <td>-</td> <td>ac</td> <td>40</td> <td>5200</td> <td>40</td> </tr> <tr> <td>tp-01</td> <td>tp-link talon ad7200*</td> <td>ad</td> <td>1</td> <td>60480</td> <td>2160</td> </tr> </tbody> </table> <p>*running tp-link's original firmware, not OpenWrt</p> <ul> <li><strong>Main clients:</strong> all positioned in the moving vehicle's roof, a vw golf mk3</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link archer t4uh</td> <td>2</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-03)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <ul> <li><strong>Background clients:</strong> the purpose is to increase channel util.</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> <th>position</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w2</td> <td>fixed, ~2m away from AP</td> </tr> <tr> <td>n</td> <td>tp-link wn722n</td> <td>1</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w2</td> <td>''</td> </tr> <tr> <td>ac</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>w3</td> <td>''</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-04)</td> <td>-</td> <td>macbook</td> <td>stopped vehicle's roof</td> </tr> </tbody> </table> <ul> <li><strong>Monitor nodes:</strong> all positioned in the moving vehicle's roof</li> </ul> <table> <tbody><tr> <th>802.11 type</th> <th>radio</th> <th>nr. antennas</th> <th>laptop</th> </tr> </tbody><tbody> <tr> <td>n</td> <td>csl usb 2.0 wlan Adapter 300 Mbps</td> <td>2</td> <td>m1</td> </tr> <tr> <td>ac</td> <td>tp-link talon ad7200 (tp-02)</td> <td>8</td> <td>w4</td> </tr> <tr> <td>ad</td> <td>tp-link talon ad7200 (tp-02)</td> <td>-</td> <td>w4</td> </tr> </tbody> </table> <p>Dataset structure:</p> <p>All the packet captures are already digested and ready to use in the file <code>wifi-exp-log-summary.csv</code>. An explanation of the fields below:</p> <ul> <li><strong>systime</strong> : system time (1 Hz resolution) that this row refers to. All node clocks were synchronized through NTP.</li> <li><strong>traceNr</strong> : nr. of the trace the row belongs to.</li> <li><strong>lon</strong> : longitude (in degrees) reported by the receiver's GPS at <code>systime</code></li> <li><strong>lat</strong> : latitude reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverAlt</strong> : altitude (in meters) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverX</strong> : x coordinate of the receiver's position when space is discretized as a Cartesian plane and the sender is set to be the origin of the coordinate system. The x axis corresponds to east-west (positive values are east, negative values are west). Unit is meters.</li> <li><strong>receiverY</strong> : y coordinate of the receiver's position when space is discretized as a Cartesian plane</li> <li><strong>receiverDist</strong> : distance (in meters) of receiver to ap(s)</li> <li><strong>receiverSpeed</strong> : speed (in m/s) reported by the receiver's GPS at <code>systime</code></li> <li><strong>receiverId</strong> : system-specific id for the client (in the vehicle)</li> <li><strong>senderId</strong> : system-specific id for the ap serving the client (side of the road)</li> <li><strong>isIperfOn</strong> : 1 if row's <code>systime</code> corresponds to a period where our UDP packet consumer application is known to have been running on the receiver side.</li> <li><strong>isInLap</strong> : 1 if this row's systime has been marked as being part of a time period where clients were doing laps around the APs, 0 otherwise.</li> <li><strong>rssiMean</strong> : the mean of the RSSI (Received Signal Strength Indicator) values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>snrMean</strong> : SNR (signal to noise ratio) retrieved from 802.11ad sectore sweep frames.</li> <li><strong>channelFreq</strong> : center frequency of the WiFi channel used, in MHz.</li> <li><strong>channelBw</strong> : bandwidth of the WiFi channel used, in MHz.</li> <li><strong>channelUtil</strong> : percentage of time the wireless medium was sensed to be busy during the 1-second period systime period the row refers to. <strong>In traces 40x, the 802.11n and ac routers didn't log channel busy time, and as such we had to approximate channel util. based on x,y coordinates and nr. of active clients.</strong></li> <li><strong>wifiType</strong> : 802.11 type (e.g., n, ac or ad).</li> <li><strong>nrClients</strong> : nr. of parallel clients operating in <code>wifiType</code> mode, on the same channel and bandwidth as <code>receiverId</code>.</li> <li><strong>dataRateMedian</strong> : the median of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>dataRateMean</strong> : the mean of the bitrate values of frames received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>nBytesReceived</strong> : total number of bytes received by the client from the ap during the 1-second period systime period the row refers to.</li> <li><strong>tghptConsumer</strong> : throughput reported by the UDP packet consumer application, during the 1-second period systime period the row refers to.</li> <li><strong>nRetries</strong> : nr. of WLAN-level re-transmissions on 1 second period</li> <li><strong>meanBeaconRssi</strong> : mean RSSI measured from beacons in 1 second period. nan values are filled with -100 dBm.</li> <li><strong>meanInterBeaconTime</strong> : mean interval between consecutive beacons, within 1 second period. nan values are filled with 1 sec.</li> <li><strong>nBeacons</strong> : total nr. of beacons received by client within 1 second period.</li> </ul>
Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Reading, University of Southampton)
<p>This video is the eleventh talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Communicating with the Network & Landscape Workshops - Dr Samantha Kanza (University of Southampton).</p> <p>Bio: Dr Samantha Kanza is an Enterprise Fellow at the University of Southampton. She completed her MEng in Computer Science at the University of Southampton and then worked for BAE Systems Applied Intelligence for a year before returning to do an iPhD in Web Science (in Computer Science and Chemistry), which focused on Semantic Tagging of Scientific Documents and Electronic Lab Notebooks. She was awarded her PhD in April 2018. Samantha works in the interdisciplinary research area of applying computer science techniques to the scientific domain, specifically through the use of semantic web technologies and artificial intelligence. Her research includes looking at electronic lab notebooks and smart laboratories, to improve the digitization and knowledge management of the scientific record using semantic web technologies; and using IoT devices in the laboratory. She has also worked on a number of interdisciplinary Semantic Web projects in different domains, including agriculture, chemistry and the social sciences.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/fvRb9ULGU2I</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
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Data from: Fish communicate with water flow to enhance a school's social network
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Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
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Data from: Synchronized mating signals in a communication network: the challenge of avoiding predators while attracting mates
Conspicuous mating signals attract mates but also expose signalers to predators and parasites. Signal evolution, therefore, is driven by conflicting selective pressures from multiple receivers, both target and nontarget. Synchronization of mating signals, for example, is an evolutionary puzzle given the assumed high cost of reduced female attraction when signals overlap. Synchronization may be beneficial, however, if overlapping signals reduce attraction of nontarget receivers. We investigate how signal synchronization is shaped by the tradeoff between natural and sexual selection in two anuran species: pug-nosed tree frogs (<i>Smilisca sila</i>), in which males produce mating calls in near-perfect synchrony, and túngara frogs (<i>Engystomops pustulosus</i>), in which males alternate their calls. To examine the tradeoff imposed by signal synchronization, we conducted field and laboratory playback experiments on eavesdropping enemies (bats and midges) and target receivers (female frogs). Our results suggest that, while synchronization can be a general strategy for signalers to reduce their exposure to eavesdroppers, relaxed selection by females for unsynchronized calls is key to the evolution and maintenance of signal synchrony. This study highlights the role of relaxed selection in our understanding the origin of mating signals and displays.
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Activity at the DAEMON booth in the European Conference on Networks and Communications
<p>@h2020daemon booth and 3 Demos at European Conference on Networks and Communications (@EuCNC) 2022 @Telefonica_En @tudelft @InformaticaUMA @IMDEA_SOFTWARE @UC3M @nec_sws @i2CAT @ADLINK_Tech @IMEC @BellLabs @SrsSystems @wings_ict @zettascaletech <a href="https://www.youtube.com/hashtag/h2020daemon">#h2020daemon</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a> @EU_H2020 @5GPPP</p>
Overview of the DAEMON booth at the European Conference on Networks and Communications
<p>@h2020daemon booth and 3 Demos at European Conference on Networks and Communications (@EuCNC) 2022 @Telefonica_En @tudelft @InformaticaUMA @IMDEA_SOFTWARE @UC3M @nec_sws @i2CAT @ADLINK_Tech @IMEC @BellLabs @SrsSystems @wings_ict @zettascaletech <a href="https://www.youtube.com/hashtag/h2020daemon">#h2020daemon</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a> @EU_H2020 @5GPPP</p>
Network embedding for understanding the National Park System through the lenses of news media, scientific communication and biogeography
<p>The United States national parks encompass a variety of biophysical and historical resources important for national cultural heritage. Yet how these resources are socially constructed often depends upon the beholder. Parks tend to be conceptualized according to their (fixed) geographic context, so our understanding of this system of systems is dominated by this geographic lens. To expose the systemic structure that exists beyond their geographic embedding, we analyze three representations of the national park system using park-park similarity networks according to their co-occurrence in: (a) ~423,000 news media articles; (b) ~11,000 research publications; and (c) ~60,000 species inhabiting parks. We quantify structural variation between network representations by leveraging similarity measures at different scales: park-level (park-park correlations) and system-level (network communities' consistency). Because parks are governed and experienced at multiple scales, cross-network comparison informs how management should account for the varying objectives and constraints that dominate at each scale. Our results identify an interesting paradox: whereas park-level correlations depend strongly on the representative lens, the network communities are remarkably robust and consistent with the underlying geographic embedding. Our data-driven methodology is generalizable to other geographically embedded socio-environmental systems and supports the holistic analysis of systems-level structure that may elude other approaches.</p>
Data from: Synchronized mating signals in a communication network: the challenge of avoiding predators while attracting mates
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Network embedding for understanding the National Park System through the lenses of news media, scientific communication and biogeography
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Self-stabilizing Byzantine-resilient communication in dynamic networks (video)
Full video presentation of the paper: Self-stabilizing Byzantine-resilient communication in dynamic networks.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network (video)
Full video presentation of the paper: Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Code and data for "An integrated microwave neural network for broadband computation and communication"
<div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains code and data used in the presentation of results in the article "An integrated microwave neural network for broadband computation and communication". The contents of the zipped files are:</p> <ul> <li><strong>Spectrum Analyzer Outputs (Datasets and ML scripts for digital emulation, radar and signal encoding classification.zip)</strong>: Reduced-bandwidth outputs used to train the backend for results presented in Figs. 3 and 4 and Supplementary Fig. 3.</li> <li><strong>Simulation Code (Coupled mode simulation of integrated MNN.zip) </strong>: For modeling the coupled MNN system shown in Fig. 2 and Extended Figs. 4 and 5.</li> <li><strong>Radar Signal Simulation (Training data and code for simulating dynamic targets in simulated airspace.zip)</strong>: Code to simulate received baseband signals from radar targets.</li> </ul> <p>Each folder contains readme files on how to run the code and analyze data.</p> <p>Please install a recent Python release (https://www.python.org/downloads/) and a recent release of MATLAB (https://www.mathworks.com/help/install/) to run the code. No non-standard hardware is required. </p> </div> </div> </div> </div> </div> </div>
26/11 Mumbai Terror Network Communication Dataset
<div> <div> <div> <div> <div> <div> <p>This dataset provides an adjacency matrix representing the communication dynamics among members of the terrorist group involved in the 26/11 Mumbai attacks. Sourced from Ze L., et al., this dataset contains critical insights into the interactions and relationships among 13 identified members, facilitating a deeper understanding of their operational collaboration.</p> <p>Dataset Structure:<br>Members: 13 individuals associated with the terrorist group.<br>Format: The dataset is structured as a binary adjacency matrix, where rows and columns correspond to the group members. Each cell in the matrix indicates whether a communication link existed between the corresponding members (1 for communication, 0 for no communication).<br>Key Features:<br>Adjacency Matrix: A 13x13 matrix capturing the communication relationships among members.<br>Binary Representation: Indicates the presence (1) or absence (0) of communication between each pair of members.<br>Purpose and Use Cases:<br>This dataset is intended for researchers and analysts focused on:</p> <p>Analyzing terrorist communication networks and their structural properties.<br>Exploring the dynamics of group interactions in the context of terrorism.<br>Developing algorithms for network analysis and visualization.</p> <p>Citation:</p> <p>[15] Ze L., et al., “Detecting Key Individuals in Terrorist Network Based on FANP Model,” no. Asonam, pp. 724–727, 2014.</p> </div> </div> </div> </div> </div> <div> <div> </div> <h2>License</h2> <p><a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener noreferrer">Attribution 4.0 International (CC BY 4.0)</a><a>Edit</a></p> </div> </div> <div> <div> <div> <div> <div> <div> <h2> </h2> </div> </div> </div> </div> </div> </div>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.