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Record W3216874683 · doi:10.1145/3479240.3488497

Providing Internet Access for Post-Disaster Communications using Balloon Networks

2021· article· en· W3216874683 on OpenAlexaff
Perumalraja Rengaraju, Kamalesh Sethuramalingam, Chung–Horng Lung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceThe InternetComputer networkBalloonComputer securityTelecommunicationsWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

Natural disasters such as earthquake, tsunami, land sliding, wildfires, flood and hurricane have frequently happened in many places around the world. Once disaster occurred, communication networks and information systems may be damaged, depending on the level of destruction. When communication networks are seriously affected, residents in the disaster area cannot communicate their situations and needs. In recent years, social networks play a vital role in connecting people and providing support with woe for survivors and their family during natural catastrophes around the world. Therefore, it is necessary to provide Internet services to the affected area at the earliest, so that victims can use the emergency communication network to confirm their safety. From the literature study, it was observed that the balloon networks satisfy the needs of post disaster communications. In this paper, a prototype system for a balloon network is constructed using two wireless nodes in the sky for establishing emergency communications. The Internet service is provided for the network using WiFi and tested for disaster communications, e.g., accessing social network sites and VoIP calls. Finally, we evaluate the performance of the disaster communications to analyze the suitability of balloon networks during post disaster communications. From the results it is observed that a WiFi balloon network can be deployed easily to provide Internet services to the affected area to meet users' needs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.294
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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