ArticleSensors (Basel, Switzerland)2022
Global Emergency System Based on WPAN and LPWAN Hybrid Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
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- Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are multiple methods of communication for the transmission of every type of alarm. In the case of emergency systems, they are usually controlled by private companies that work as bridges between the source and the receiver of an emergency. Furthermore, it is necessary to use an independent communication system for each building, requiring human vigilance, leading to an increase in infrastructure and service costs as well as response time. This paper proposes a hybrid network by combining both wireless personal access network (WPAN) and low power wide access network (LPWAN) communication for the complete development of a communication architecture oriented to an emergency system. The main aim of this work is to provide a global emergency system that is focused on fire detection but is also suitable for other critical events, with low energy consumption, a wide communication range covering up to 50 km, and a low cost of service and infrastructure. This proposal reduces the total energy consumption of the system with respect to typical fire detection systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.